R/buildData.R
In Blanch-Font/SOPHIA: What the Package Does (One Line, Title Case)
Defines functions
FunCovar
getDbuseHTNRx_TimeCovariateData
createHTNRx_TimeCovariateSettings
getDbuseC10_TimeCovariateData
createC10_TimeCovariateSettings
buildFollowUp
getDbuseT1Rx_AgeCovariateData
createT1Rx_AgeCovariateSettings
getDbuseT1Rx_TimeCovariateData
createT1Rx_TimeCovariateSettings
getDbuseT1DM_AgeCovariateData
createT1DM_AgeCovariateSettings
getDbuseT1DM_TimeCovariateData
createT1DM_TimeCovariateSettings
getDbuseT2DM_TimeCovariateData
createT2DM_TimeCovariateSettings
getDbSmokingCovariateData
createSmokingCovariateSettings
transformToFlat
buildData
Documented in
buildData
buildFollowUp
createC10_TimeCovariateSettings
createHTNRx_TimeCovariateSettings
createSmokingCovariateSettings
createT1DM_AgeCovariateSettings
createT1DM_TimeCovariateSettings
createT1Rx_AgeCovariateSettings
createT1Rx_TimeCovariateSettings
createT2DM_TimeCovariateSettings
FunCovar
getDbSmokingCovariateData
getDbuseC10_TimeCovariateData
getDbuseHTNRx_TimeCovariateData
getDbuseT1DM_AgeCovariateData
getDbuseT1DM_TimeCovariateData
getDbuseT1Rx_AgeCovariateData
getDbuseT1Rx_TimeCovariateData
getDbuseT2DM_TimeCovariateData
transformToFlat
#' Build a Covariate Data for cohort
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#'
#' @return A covariateData object
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' # Not yet
buildData <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId = 1){
covDemo <- FeatureExtraction::createCovariateSettings(
useDemographicsGender = TRUE,
useDemographicsAge = TRUE)
BMI_conceptId <- c(3038553, 40762638) #LOINC
height_conceptId <- c(3036277, 3015514) #LOINC
weight_conceptId <- c(3025315) #LOINC
covMeasValueAny <- FeatureExtraction::createCovariateSettings(
useMeasurementValueAnyTimePrior = TRUE,
includedCovariateConceptIds = c(BMI_conceptId,
height_conceptId,
weight_conceptId),
addDescendantsToInclude = TRUE)
SBP_conceptId <- c(3004249, #LOINC
4152194) #SNOMED
DBP_conceptId <- c(3012888, #LOINC
4154790) #SNOMED
cT_conceptId <- c(3019900, 3027114) #LOINC
cHDL_conceptId <- c(3011884, 3007070, 3023602) #LOINC
cLDL_conceptId <- c(3028437, 3001308) #LOINC
cVLDL_conceptId <- c(3022487) #LOINC
Tg_conceptId <- c(3022192, 42868692) #LOINC
glu_conceptId <- c(3004501,
46235168, 40757523, 3005834, 40757527, 3016567, 40757528, 40757529,
40757627, 40757628,
3018251, 46236948,
3015024, 3036895, 3001022, 3016701, 3018582, 3008799, 3045700,
3020491) #LOINC
alt_conceptId <- c(3006923, 46235106)
CRP_conceptId <- c(3020460) #LOINC
ferritin_conceptId <- c(3001122) #LOINC
WBC_conceptId <- c(3010813) #LOINC
neutrophils_conceptId <- c(3017732, 3046321, 43055364) #LOINC
basophils_conceptId <- c(3006315, 43055368) #LOINC
eosinophils_conceptId <- c(3013115, 43055367) #LOINC
monocytes_conceptId <- c(3001604, 43055365) #LOINC
lymphocytes_conceptId <- c(3019198, 43055366) #LOINC
HbA1c_conceptId <- c(3034639, 3004410) #LOINC
creatinine_conceptId <- c(3016723)
albumin_ser_conceptId <- c(3024561)
CKDEPI_conceptId <- c(40764999)
GOT_conceptId <- c(3010587)
GGT_conceptId <- c(3026910)
vitD_conceptId <- c(43055034)
PEPTIDCs_conceptId <- c(3010084)
hemoglobin_conceptId <- c(3000963)
covMeasValueLong <- FeatureExtraction::createCovariateSettings(
useMeasurementValueLongTerm = TRUE,
longTermStartDays = (-2)*365.25,
endDays = 0*365.25,
includedCovariateConceptIds = c(
SBP_conceptId,
DBP_conceptId,
# cT_conceptId,
# cHDL_conceptId,
# cLDL_conceptId,
# cVLDL_conceptId,
# Tg_conceptId,
glu_conceptId,
alt_conceptId,
CRP_conceptId,
ferritin_conceptId,
WBC_conceptId,
neutrophils_conceptId,
basophils_conceptId,
eosinophils_conceptId,
monocytes_conceptId,
lymphocytes_conceptId,
HbA1c_conceptId,
creatinine_conceptId,
albumin_ser_conceptId,
CKDEPI_conceptId,
GOT_conceptId,
GGT_conceptId,
vitD_conceptId,
PEPTIDCs_conceptId,
hemoglobin_conceptId),
addDescendantsToInclude = TRUE)
covMeasValue_lipid <- FeatureExtraction::createTemporalCovariateSettings(
useMeasurementValue = TRUE,
temporalStartDays = c(-2, 1, 3/12)*365.25,
temporalEndDays = c(0, 2, 1)*365.25,
includedCovariateConceptIds = c(
cT_conceptId,
cHDL_conceptId,
cLDL_conceptId,
cVLDL_conceptId,
Tg_conceptId),
addDescendantsToInclude = TRUE)
T2DM_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 111,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 111,
analysisName = "T2DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(201826, 443732, 40482801, 40485020),
addDescendantsToInclude = TRUE)
obesity_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 112,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 112,
analysisName = "Obesity",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(433736),
addDescendantsToInclude = TRUE)
angor_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 113,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 113,
analysisName = "Angina pectoris",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(#I20
321318, #Angina pectoris
315296, #Preinfarction syndrome
4127089), #Coronary artery spasm,
addDescendantsToInclude = FALSE)
ami_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 114,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 114,
analysisName = "AMI",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(312327, #Acute myocardial infarction
4296653, #Acute ST segment elevation myocardial infarction
45766075, #Acute anterior ST segment elevation myocardial infarction
45766116, #Acute ST segment elevation myocardial infarction of inferior wall
4296653, #Acute ST segment elevation myocardial infarction
4270024, #Acute non-ST segment elevation myocardial infarction
4329847, #Myocardial infarction
#I22
4108217, #Subsequent myocardial infarction
4108677, #Subsequent myocardial infarction of anterior wall
4108218, #Subsequent myocardial infarction of inferior wall
45766241, #Subsequent non-ST segment elevation myocardial infarction
45766114, #Subsequent ST segment elevation myocardial infarction
#I23
4108678, #Hemopericardium due to and following acute myocardial infarction
438172, #Atrial septal defect due to and following acute myocardial infarction
4119953, #Post-infarction ventricular septal defect
4108679, #Rupture of cardiac wall without hemopericardium as current complication following acute myocardial infarction
4108219, #Rupture of chordae tendinae due to and following acute myocardial infarction
4108220, #Rupture of papillary muscle as current complication following acute myocardial infarction
4108680, #Thrombosis of atrium, auricular appendage, and ventricle due to and following acute myocardial infarction
4198141, #Post infarct angina
# Altres
434376, #Acute myocardial infarction of anterior wall
438170, # Acute myocardial infarction of inferior wall
4176969, #Sequelae of cardiovascular disorders
37309626, #Myocardial infarction due to demand ischemia
43020460, #Acute ST segment elevation myocardial infarction involving left anterior descending coronary artery
46270162, # Acute ST segment elevation myocardial infarction due to left coronary artery occlusion
46270163),
addDescendantsToInclude = FALSE)
stroke_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 115,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 115,
analysisName = "Stroke",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(
#I63
443454, #Cerebral infarction
4110189, #Cerebral infarct due to thrombosis of precerebral arteries
4110190, #Cerebral infarction due to embolism of precerebral arteries
4043731, #Infarction - precerebral
4110192, #Cerebral infarction due to thrombosis of cerebral arteries
4108356, #Cerebral infarction due to embolism of cerebral arteries
4111714, #Cerebral infarction due to cerebral venous thrombosis, non-pyogenic
#I64
#I65
43022059, #Disease of non-coronary systemic artery
4153380, #Disorder of carotid artery
4159164, #Disorder of basilar artery
443239, #Precerebral arterial occlusion
#Altres
255919, #Finding of head and neck region
313226, #Carotid artery occlusion
321887, #Disorder of artery
381316, #Cerebrovascular accident
381591, #Cerebrovascular disease
4006294, #Basilar artery embolism
4028073, #Disorder of artery of neck
4213731, #Carotid artery embolism
4273526, #Vertebral artery thrombosis
4274969, #Vertebral artery embolism
4288310, #Carotid artery obstruction
4311124, #Carotid artery thrombosis
4338227, #Basilar artery thrombosis
45767658, #Cerebral infarction due to thrombosis of middle cerebral artery
45772786, #Cerebral infarction due to embolism of middle cerebral artery
46270031, #Cerebral infarction due to occlusion of precerebral artery
46273649 #Cerebral infarction due to occlusion of basilar artery
),
addDescendantsToInclude = FALSE)
TIA_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 116,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 116,
analysisName = "TIA",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(#G45
373503, #Transient cerebral ischemia
437306, #Transient global amnesia
4338523, #Amaurosis fugax
381036, # Multiple AND bilateral precerebral artery stenosis
4112020, #Carotid artery syndrome hemispheric
4048785, #Vertebrobasilar territory transient ischemic attack
#G46
381591, #Cerebrovascular disease
4110194, #Middle cerebral artery syndrome
4108360, #Anterior cerebral artery syndrome
4110195, #Posterior cerebral artery syndrome
4111710, #Brainstem stroke syndrome
4111711, #Cerebellar stroke syndrome
4045737, #Pure motor lacunar infarction
4045738, #Pure sensory lacunar infarction
4046360 #Lacunar infarction
),
addDescendantsToInclude = FALSE)
COPD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 117,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 117,
analysisName = "COPD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(255841, 261325, 255573),
addDescendantsToInclude = TRUE)
CKD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 118,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 118,
analysisName = "CKD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(46271022, 192359),
addDescendantsToInclude = TRUE)
cancer_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 119,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 119,
analysisName = "Cancer",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(443392, 4144289, 439392, 200962, 139750, 4311499, 137809, 197500),
addDescendantsToInclude = TRUE)
depress_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 120,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 120,
analysisName = "Depress",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(4282096, 4282316, 433440),
addDescendantsToInclude = TRUE)
htn_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 121,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 121,
analysisName = "HTN",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(320128, 442604, 444101, 319034, 443919, 44782429, 44784621,
439696, 319826, 317895, 443771, 4110948, 319826),
addDescendantsToInclude = TRUE)
hf_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 122,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 122,
analysisName = "HF",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(316139, 319835, 439846, 443580, 443587, 4229440, 4273632, 40479192,
40479576, 40480602, 40480603, 40481042, 40481043, 40482727, 44782718,
44782733),
addDescendantsToInclude = TRUE)
liver_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 123,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 123,
analysisName = "LiverFailure",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(4098652, 197795, 4211974, 4012113,
192240, 192242, 193693, 196625, 197490, 197494, 198683,
198964, 439673, 439674, 439675,
4245975, 200763, 4267417, 194990, 194984,
192675, 192680, 196455, 199867, 200762,
201901, 377604, 4026125, 4046123, 4058696, 4059290, 4064161,
4135822, 4238978, 4240725, 4313846, 4340390,
4340394, 4340941, 4340948, 40484532, 46269836),
addDescendantsToInclude = TRUE)
ra_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 124,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 124,
analysisName = "RA",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(36684997, 80809), #M05, M06
addDescendantsToInclude = TRUE)
sleep_apnea_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 125,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 125,
analysisName = "SleepApnea",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(313459), #G47.3
addDescendantsToInclude = TRUE)
pcos_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 126,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 126,
analysisName = "PCOS",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(40443308), #E28.2
addDescendantsToInclude = TRUE)
T1DM_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 127,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 127,
analysisName = "T1DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(201254, 435216, 40484648, 40484649),
addDescendantsToInclude = TRUE)
nephro_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 128,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 128,
analysisName = "Nephropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(192279, #Disorder of kidney due to diabetes mellitus
200687, #Renal disorder due to type 1 diabetes mellitus
443731, #Renal disorder due to type 2 diabetes mellitus
43531578 #Chronic kidney disease due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
retino_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 129,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 129,
analysisName = "Retinopathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(376114, #Severe nonproliferative retinopathy due to diabetes mellitus
376979, #Cataract due to diabetes mellitus
377552, #Moderate nonproliferative retinopathy due to diabetes mellitus
378743, #Mild nonproliferative retinopathy due to diabetes mellitus
380096, # Proliferative retinopathy due to diabetes mellitus
380097, # Macular edema due to diabetes mellitus
443733, #Disorder of eye due to type 2 diabetes mellitus
443767, #Disorder of eye due to diabetes mellitus
4174977, #Retinopathy due to diabetes mellitus
4221495, #Cataract due to diabetes mellitus type 2
4225656, #Cataract due to diabetes mellitus type 1
4227210, #Retinopathy due to type 1 diabetes mellitus
4266637, #Severe nonproliferative retinopathy without macular edema due to diabetes mellitus
4290822, #Severe nonproliferative retinopathy with clinically significant macular edema due to diabetes mellitus
4338896, #Traction retinal detachment involving macula
4338897, #Combined traction and rhegmatogenous retinal detachment
4338901, #Traction detachment of retina due to diabetes mellitus
37016179, #Mild nonproliferative retinopathy due to type 1 diabetes mellitus
37016180, #Moderate nonproliferative retinopathy due to type 1 diabetes mellitus
42538169, #Disorder of eye due to type 1 diabetes mellitus
43530656, #Nonproliferative retinopathy due to type 2 diabetes mellitus
43530685, #Proliferative retinopathy due to type 2 diabetes mellitus
45757435, # Mild nonproliferative retinopathy due to type 2 diabetes mellitus
45763583, #Nonproliferative diabetic retinopathy due to type 1 diabetes mellitus
45763584, #Proliferative retinopathy due to type 1 diabetes mellitus
45769873, #Traction detachment of retina due to type 1 diabetes mellitus
45770830, #Macular edema and retinopathy due to type 2 diabetes mellitus
45770881, #Moderate nonproliferative retinopathy due to type 2 diabetes mellitus
45773064 #Traction detachment of retina due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
neuro_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 130,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 130,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(376065, #Disorder of nervous system due to type 2 diabetes mellitus
376112, #Polyneuropathy due to diabetes mellitus
377821, #Disorder of nervous system due to type 1 diabetes mellitus
443730, #Disorder of nervous system due to diabetes mellitus
4044391, #Neuropathy due to diabetes mellitus
4048028, #Diabetic mononeuropathy
4140466, #Lumbosacral radiculoplexus neuropathy due to type 2 diabetes mellitus
4143857, #Lumbosacral radiculoplexus neuropathy due to type 1 diabetes mellitus
4175440, #Autonomic neuropathy due to diabetes mellitus
4191611, #Lumbosacral radiculoplexus neuropathy due to diabetes mellitus
4222415, #Mononeuropathy due to type 2 diabetes mellitus
4225055, #Mononeuropathy due to type 1 diabetes mellitus
37016767, #Autonomic neuropathy due to type 1 diabetes mellitus
37016768, #Autonomic neuropathy due to type 2 diabetes mellitus
37017431, #Polyneuropathy due to type 1 diabetes mellitus
37017432 #Polyneuropathy due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
PAD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 131,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 131,
analysisName = "PAD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(
74719, #Ulcer of foot
134380, #Erythromelalgia
# 138525, #Pain in limb
195834, #Atherosclerosis of renal artery
197304, #Ulcer of lower extremity
312934, #Atherosclerosis of aorta
314965, #Embolism and thrombosis of an arm or leg artery
315558, #Atherosclerosis of arteries of the extremities
317577, #Arteriosclerotic gangrene
318443, #Arteriosclerotic vascular disease
321052, #Peripheral vascular disease
321882, #Generalized atherosclerosis
442287, #Rest pain
442774, #Intermittent claudication
443358, #Ulcer of heel
443593, #Ulcer of thigh
4171556, #Ankle ulcer
4177703, #Ulcer
4316222, #Venous intermittent claudication
4325344, #Pain at rest due to peripheral vascular disease
35611566, #Bilateral lower limb atherosclerosis pain at rest co-occurrent and due to atherosclerosis
35615028, #Bilateral atherosclerosis of lower limbs with gangrene
36712805, #Pain at rest of left lower limb co-occurrent and due to atherosclerosis
36712806, #Intermittent claudication of right lower limb co-occurrent and due to atherosclerosis
36712807, #Pain at rest of right lower limb co-occurrent and due to atherosclerosis
36712963, #Gangrene of left lower limb due to atherosclerosis
36717006, #Intermittent claudication of bilateral lower limbs co-occurrent and due to atherosclerosis
36717279, #Gangrene of right lower limb due to atherosclerosis
36717286, #Intermittent claudication of left lower limb co-occurrent and due to atherosclerosis
37110250, #Atherosclerosis of artery of lower limb
37312519, #Ulcer of calf due to atherosclerosis of artery of lower limb
37312520, #Ischemic foot ulcer due to atherosclerosis of artery of lower limb
37312524, #Ulcer of ankle due to atherosclerosis of artery of lower limb
37312529, #Intermittent claudication due to atherosclerosis of artery of limb
37312531, #Gangrene of limb due to atherosclerosis of artery of limb
40479625, #Atherosclerosis of artery
40483538, #Atherosclerosis of bypass graft of limb
40484541, #Atherosclerosis of autologous vein bypass graft of limb
40484551, #Atherosclerosis of nonautologous biological bypass graft of limb
44782819, #Chronic occlusion of artery of extremity
46271459 #Atherosclerosis of bypass graft of lower limb
),
addDescendantsToInclude = FALSE)
dka_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 132,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 132,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(439770, #Ketoacidosis due to type 1 diabetes mellitus
4009303, #Diabetic ketoacidosis without coma
4224254 #Ketoacidotic coma due to type 1 diabetes mellitus
),
addDescendantsToInclude = FALSE)
hypoglyc_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 133,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 133,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(45769876, #Hypoglycemia due to type 1 diabetes mellitus
4228112 #Hypoglycemic coma due to type 1 diabetes mellitus
),
addDescendantsToInclude = FALSE)
hypoglyc_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 134,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 134,
analysisName = "Anaemia",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(439777 #Anemia
),
addDescendantsToInclude = FALSE)
A10_conceptId <- c(21600712,
782681, 793321, 1502829, 1502830, 1503327, 1525221, 1529352,
1547554, 1596977, 1597761, 1597772, 1597773, 1597781, 1597792,
19006931, 19021312, 19023424, 19023425, 19023426, 19029030, 19029061,
19058398, 19059800, 19077638, 19077682, 19078552, 19078559, 19079293,
19079465, 19095211, 19095212, 19099055, 19101729, 19112791, 19125041,
19125045, 19125049, 19129179, 19133793, 19135264, 21022404, 21036596,
21061594, 21061613, 21076306, 21081251, 21086042, 21100924, 21114195,
21133671, 21169719, 35408233, 35410536, 35412102, 35412890, 35412958,
36403507, 36403509, 36884964, 40044221, 40051377, 40052768, 40054707,
40139098, 40164885, 40164888, 40164891, 40164897, 40164913, 40164916,
40164942, 40164943, 40164946, 40166037, 40166041, 40239218, 42479624,
42479783, 42481504, 42481541, 42482012, 42482588, 42656231, 42656236,
42656240, 42708086, 42708090, 42899447, 42902356, 42902587, 42902742,
42902821, 42902945, 42902992, 42903059, 42903341, 43013885, 43013905,
43013911, 43013918, 43013924, 43013928, 43526467, 43526471, 44032735,
44058584, 44123708, 44785831, 45774709, 45774754, 45774893, 46233969,
46234047, 46234234, 46234237, 46287408, 46287689)
sel_med_conceptId <- c(21600712, #DRUGS USED IN DIABETES
#Aquestes insulines no les troba
21076306, 44058584, 21086042, 21036596,
21601238, #C01
21600381, #C02
21601461, #C03
21601664, #C07
21601744, #C08
21601782, #C09
21601853, #C10
21603933, #M01A
# Antiinflamatoris
920458, 1103374, 1115008, 1118084, 1124300, 1125315, 1146810, 1150345,
1153928, 1156378, 1177480, 1178663, 1185922, 1189754, 1518254, 19029393,
19056874, 19110711,
# Antiagregants
1310149, 1325124, 19024063, 1301025, 1301065, 1308473, 1367571, 1436169,
1112807, 1302398, 1322184, 1331270, 1350310, 19042778, 19047423, 19069107,
40241186
)
covDrug <- FeatureExtraction::createCovariateSettings(
useDrugGroupEraMediumTerm = TRUE,
mediumTermStartDays = -365.25,
endDays = 0,
includedCovariateConceptIds = sel_med_conceptId,
addDescendantsToInclude = TRUE)
# smoking_vars <- FeatureExtraction::createAnalysisDetails(
# analysisId = 810,
# sqlFileName = "DomainConcept.sql",
# parameters = list(analysisId = 810,
# analysisName = "Tobacco",
# startDay = "anyTimePrior",
# endDay = 1,
# subType = "last",
# domainId = "Observation",
# domainTable = "observation",
# domainConceptId = "VALUE_AS_CONCEPT_ID",
# domainStartDate = "observation_date",
# domainEndDate = ""),
# includedCovariateConceptIds = c(45879404, 45884037, 45883458),
# addDescendantsToInclude = TRUE)
SmokingCovSet <- createSmokingCovariateSettings(useSmoking = TRUE)
T2DM_TimeCovSet <- createT2DM_TimeCovariateSettings(useT2DM_Time = TRUE)
T1DM_TimeCovSet <- createT1DM_TimeCovariateSettings(useT1DM_Time = TRUE)
T1DM_AgeCovSet <- createT1DM_AgeCovariateSettings(useT1DM_Age = TRUE)
T1Rx_TimeCovSet <- createT1Rx_TimeCovariateSettings(useT1Rx_Time = TRUE)
# T1Rx_AgeCovSet <- createT1Rx_AgeCovariateSettings(useT1Rx_Age = TRUE)
C10_TimeCovSet <- createC10_TimeCovariateSettings(useC10_Time = TRUE)
HTNRx_TimeCovSet <- createHTNRx_TimeCovariateSettings(useHTNRx_Time = TRUE)
covariateSettings <- list(covDemo,
covMeasValueAny,
covMeasValueLong,
# covMeasValue_lipid,
FeatureExtraction::createDetailedCovariateSettings(
list(T2DM_vars,
obesity_vars,
angor_vars,
ami_vars,
stroke_vars,
TIA_vars,
COPD_vars,
CKD_vars,
cancer_vars,
depress_vars,
htn_vars,
hf_vars,
liver_vars,
ra_vars,
sleep_apnea_vars,
pcos_vars,
T1DM_vars,
nephro_vars,
retino_vars,
neuro_vars,
PAD_vars,
dka_vars,
hypoglyc_vars)),
covDrug,
SmokingCovSet,
T2DM_TimeCovSet,
T1DM_TimeCovSet,
T1Rx_TimeCovSet,
T1DM_AgeCovSet,
# T1Rx_AgeCovSet,
C10_TimeCovSet,
HTNRx_TimeCovSet)
covariateData <- FeatureExtraction::getDbCovariateData(
connection = cdm_bbdd,
cdmDatabaseSchema = cdm_schema,
cohortDatabaseSchema = results_sc,
cohortTable = cohortTable,
cohortId = acohortId,
rowIdField = "subject_id",
covariateSettings = covariateSettings)
covariateData_temp <- FeatureExtraction::getDbCovariateData(
connection = cdm_bbdd,
cdmDatabaseSchema = cdm_schema,
cohortDatabaseSchema = results_sc,
cohortTable = cohortTable,
cohortId = acohortId,
rowIdField = "subject_id",
covariateSettings = covMeasValue_lipid)
# T2DM_conceptId <- c(201530111, 201826111, 376065111, 443729111, 443731111, 443733111,
# 4193704111, 4196141111, 4221495111, 36714116111, 37016349111,
# 37017432111, 43530685111, 43530690111, 43531563111, 43531578111,
# 45770830111,
# # Afegits executant el SIDIAP
# 4099651111, 45757363111, 4140466111, 37016768111, 4222876111, 43531616111,
# 45770881111)
# DM_conceptId <- c(442793111, 321822111, 443730111, 192279111, 4048028111, 4226798111,
# 201820111, 4008576111, 443767111,
# # Afegits executant el SIDIAP
# 4044391111, 4009303111, 376112111, 4159742111, 4114427111, 4131908111)
# obesity_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 112),
# .data$covariateId)
# angina_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 113),
# .data$covariateId)
# ami_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 114),
# .data$covariateId)
# stroke_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 115),
# .data$covariateId)
# tia_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 116),
# .data$covariateId)
# COPD_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 117),
# .data$covariateId)
# CKD_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 118),
# .data$covariateId)
# cancer_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 119),
# .data$covariateId)
# depress_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 120),
# .data$covariateId)
# htn_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 121),
# .data$covariateId)
# hf_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 122),
# .data$covariateId)
# liver_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 123),
# .data$covariateId)
# ra_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 124),
# .data$covariateId)
# sleep_apnea_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 125),
# .data$covariateId)
# pcos_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 126),
# .data$covariateId)
# covariateData$covariates <- dplyr::mutate(
# .data = covariateData$covariates,
# covariateId = dplyr::if_else(.data$covariateId %in% T2DM_conceptId, 201826111, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% DM_conceptId, 201820111, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% obesity_conceptId, 433736112, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% angina_conceptId, 321318113, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% ami_conceptId, 312327114, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% stroke_conceptId, 443454115, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% tia_conceptId, 373503116, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% COPD_conceptId, 255841117, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% CKD_conceptId, 46271022118, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% cancer_conceptId, 443392119, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% depress_conceptId, 4282096120, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% htn_conceptId, 320128121, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% hf_conceptId, 316139122, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% liver_conceptId, 194984123, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% ra_conceptId, 80809124, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% sleep_apnea_conceptId, 313459125, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% pcos_conceptId, 40443308126, .data$covariateId))
# covariateData$covariates <- dplyr::distinct(covariateData$covariates)
return(list(covariateData = covariateData, covariateData_temp = covariateData_temp))
}
#' Transform covariateData object into FlatTable
#'
#' Si volem agafar l'últim valor podem canviar-ho
#'
#' @param covariateData A covariateDate object
#' @param covariateData_temp A Temporal covariateDate object
#'
#' @return A data.table with the covariate data
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' #Not yet
transformToFlat <- function(covariateData,
covariateData_temp){
vec_antiinflam <- c(920458, 1103374, 1115008, 1118084, 1124300, 1125315, 1146810, 1150345,
1153928, 1156378, 1177480, 1178663, 1185922, 1189754, 1518254, 19029393,
19056874, 19110711)*1000 + 411
vec_antiagregant <- c(1310149, 1325124, 19024063, 1301025, 1301065, 1308473, 1367571, 1436169,
1112807, 1302398, 1322184, 1331270, 1350310, 19042778, 19047423, 19069107,
40241186)*1000 + 411
bbdd_covar <- dplyr::collect(covariateData$covariates)
bbdd_covar <- dplyr::mutate(
.data = bbdd_covar,
variable = as.character(NA),
variable = dplyr::if_else(.data$covariateId == 1002, 'age', .data$variable),
variable = dplyr::if_else(.data$covariateId == 8507001, 'sex_male', .data$variable),
variable = dplyr::if_else(.data$covariateId == 8532001, 'sex_female', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 111, 'T2DM', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 112, 'obesity', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 113, 'angor', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 114, 'ami', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 115, 'stroke', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 116, 'tia', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 117, 'COPD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 118, 'CKD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 119, 'cancer', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 120, 'depress', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 121, 'htn', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 122, 'hf', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 123, 'liver', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 124, 'ra', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 125, 'sleep_apnea', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 126, 'pcos', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 127, 'T1DM', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 128, 'nephro', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 129, 'retino', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 130, 'neuro', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 131, 'PAD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 132, 'DKA', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 133, 'Hypoglyc', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 134, 'Anaemia', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600712411, 'A10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600713411, 'A10A', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600744411, 'A10B', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601238411, 'C01', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600381411, 'C02', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601461411, 'C03', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601664411, 'C07', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601744411, 'C08', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601782411, 'C09', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601853411, 'C10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21603933411, 'M01A', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% vec_antiinflam, 'antiinflamatori', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% vec_antiagregant, 'antiagregant', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45879404, 'Never', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45884037, 'Current', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45883458, 'Former', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3038553531705, 'BMI', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3036277582705, 'height', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3025315529705, 'weight', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% c(3004249323706, 4152194876706), 'SBP', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% c(3012888323706, 4154790876706), 'DBP', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3010813848706, 'Leukocytes', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3001604554706, 'Monocytes', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3034639554706, 'HbA1c', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3027114840706, 'cT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840706, 'cHDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840706, 'cLDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840706, 'Tg', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3004501840706, 'Glucose', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3006923645706, 'ALT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3020460751706, 'CRP', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3001122748706, 'Ferritin', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3016723, 'Creatinine', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3024561, 'Albumin', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 8) == 40764999, 'CKDEPI', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3010587, 'GOT', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3026910, 'GGT', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 8) == 43055034, 'vitD', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3010084, 'PEPTIDCs', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3000963, 'Hemoglobin', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201820211, 'TimeT2DM', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254212, 'TimeT1DM', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254216, 'TimeT1Rx', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254213, 'AgeT1DM', .data$variable),
# variable = dplyr::if_else(.data$covariateId == 201254217, 'AgeT1Rx', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601853214, 'TimeC10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600381215, 'TimeHTNRx', .data$variable))
bbdd_covar <- dplyr::group_by(.data = bbdd_covar, .data$rowId, .data$variable)
bbdd_covar <- dplyr::summarise(
.data = bbdd_covar,
covariateValue = mean(.data$covariateValue),
.groups = 'keep')
bbdd_covar <- dplyr::ungroup(x = bbdd_covar)
bbdd_covar_temp <- dplyr::collect(covariateData_temp$covariates)
bbdd_covar_temp <- dplyr::mutate(
.data = bbdd_covar_temp,
variable = as.character(NA),
variable = dplyr::if_else(.data$covariateId == 3027114840702 & .data$timeId == 1, 'cT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840702 & .data$timeId == 1, 'cHDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840702 & .data$timeId == 1, 'cLDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840702 & .data$timeId == 1, 'Tg', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3027114840702 & .data$timeId == 2, 'cT_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840702 & .data$timeId == 2, 'cHDL_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840702 & .data$timeId == 2, 'cLDL_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840702 & .data$timeId == 2, 'Tg_1y', .data$variable))
bbdd_covar_temp <- dplyr::filter(.data = bbdd_covar_temp, !is.na(.data$variable))
bbdd_covar_temp <- dplyr::group_by(.data = bbdd_covar_temp, .data$rowId, .data$variable)
bbdd_covar_temp <- dplyr::summarise(
.data = bbdd_covar_temp,
covariateValue = mean(.data$covariateValue),
.groups = 'keep')
bbdd_covar_temp <- dplyr::ungroup(x = bbdd_covar_temp)
bbdd_covar <- tidyr::pivot_wider(
data = rbind(bbdd_covar, bbdd_covar_temp),
id_cols = 'rowId',
names_from = 'variable',
values_from = 'covariateValue')
bbdd_covar <- dplyr::mutate(
.data = bbdd_covar,
dplyr::across(dplyr::any_of(c('sex_female', 'sex_male', 'T2DM', 'obesity', 'angor', 'tia',
'stroke', 'ami', 'Current', 'Former', 'Never',
'A10', 'A10A', 'A10B','C01', 'C02', 'C03', 'C07', 'C08', 'C09',
'C10', 'M01A',
'COPD', 'CKD', 'cancer', 'depress', 'htn', 'hf', 'liver', 'ra',
'sleep_apnea', 'pcos',
"neuro", "nephro", "retino", "PAD",
'ALT', 'Hemoglobin')),
~ tidyr::replace_na(.x, 0)))
# sex_female = dplyr::if_else(is.na(sex_female), 0, sex_female),
# sex_male = dplyr::if_else(is.na(sex_male), 0, sex_male),
# T2DM = dplyr::if_else(is.na(T2DM), 0, T2DM),
# obesity = dplyr::if_else(is.na(obesity), 0, obesity),
# angor = dplyr::if_else(is.na(angor), 0, angor),
# tia = dplyr::if_else(is.na(tia), 0, tia),
# stroke = dplyr::if_else(is.na(stroke), 0, stroke),
# ami = dplyr::if_else(is.na(ami), 0, ami),
# Current = dplyr::if_else(is.na(Current), 0, Current),
# Former = dplyr::if_else(is.na(Former), 0, Former))
# bbdd_covar <- dplyr::mutate(
# .data = bbdd_covar,
# TimeT1DM_diag = TimeT1DM,
# TimeT1DM = pmax(TimeT1DM, TimeT1Rx, na.rm = T))
return(bbdd_covar)
}
#' Auxiliar function to create Smoking status
#'
#' @param useSmoking Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createSmokingCovariateSettings <- function(useSmoking = TRUE){
covariateSettings <- list(useSmoking = useSmoking)
attr(covariateSettings, "fun") <- "getDbSmokingCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Smokins status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbSmokingCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing Smoking covariates")
if (covariateSettings$useSmoking == FALSE) {
return(NULL)
}
# Some SQL to construct the covariate:
sql <- "SELECT DISTINCT ON (obs2.person_id)
obs2.person_id AS row_id,
obs2.value_as_concept_id AS covariate_id,
1 AS covariate_value
FROM (SELECT *
FROM @cdm_database_schema.OBSERVATION obs
INNER JOIN @cohort_table cohort
ON cohort.subject_id = obs.person_id
WHERE obs.observation_concept_id = 43054909 AND
obs.observation_date <= cohort.cohort_start_date AND
cohort.cohort_definition_id IN (@cohort_definition_id)) obs2
ORDER BY obs2.person_id, obs2.observation_date desc"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(45879404, 45884037, 45883458),
covariateName = c('Never smoker', 'Current some day smoker',
'Former smoker'),
analysisId = 500,
conceptId = rep(43054909, 3))
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 500,
analysisName = "Smoking status",
domainId = "Observation",
startDay = NA,
endDay = 0,
isBinary = "Y",
missingMeansZero = "Y")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T2DM diagnosis
#'
#' @param useT2DM_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT2DM_TimeCovariateSettings <- function(useT2DM_Time = TRUE){
covariateSettings <- list(useT2DM_Time = useT2DM_Time)
attr(covariateSettings, "fun") <- "getDbuseT2DM_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T2DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT2DM_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T2DM_Time covariates")
if (covariateSettings$useT2DM_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201820, 442793, 443238))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# excluded_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 4058243, 40484648,195771, 761051))
# excluded_id <- DatabaseConnector::querySql(connection,
# sql = excluded_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201820211 AS covariate_id,
# DATEDIFF(DAY, cond2.condition_start_date, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id NOT IN (@excluded_concept_table)
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201820211 AS covariate_id,
DATEDIFF(DAY, MIN(cond.condition_start_date), cohort.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT c ON cond.condition_concept_id = c.concept_id
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE
NOT EXISTS(
SELECT 1
FROM @cdm_database_schema.CONCEPT_ANCESTOR ca2
WHERE ca2.descendant_concept_id = cond.condition_concept_id
AND ca2.ancestor_concept_id IN (201254, 435216, 4058243, 40484648, 195771, 761051)
)
AND ca.ancestor_concept_id IN (201820, 442793, 443238)
AND cohort.cohort_definition_id = @cohort_definition_id
AND cond.condition_start_date <= cohort.cohort_start_date
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201820211),
covariateName = c('Time from T2DM'),
analysisId = 211,
conceptId = 201820)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 211,
analysisName = "Time from T2DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T1DM diagnosis
#'
#' @param useT1DM_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1DM_TimeCovariateSettings <- function(useT1DM_Time = TRUE){
covariateSettings <- list(useT1DM_Time = useT1DM_Time)
attr(covariateSettings, "fun") <- "getDbuseT1DM_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T1DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1DM_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1DM_Time covariates")
if (covariateSettings$useT1DM_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 40484648, 40484649))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201254212 AS covariate_id,
# DATEDIFF(DAY, cond2.condition_start_date, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201254212 AS covariate_id,
DATEDIFF(DAY, MIN(cond.condition_start_date), cohort.cohort_start_date) AS covariate_value
FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT c ON cond.condition_concept_id = c.concept_id
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE
ca.ancestor_concept_id IN (201254, 435216, 40484648, 40484649)
AND cohort.cohort_definition_id = @cohort_definition_id
AND cond.condition_start_date <= cohort.cohort_start_date
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254212),
covariateName = c('Time from T1DM'),
analysisId = 212,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 212,
analysisName = "Time from T1DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Age for first T1DM diagnosis
#'
#' @param useT1DM_Age Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1DM_AgeCovariateSettings <- function(useT1DM_Age = TRUE){
covariateSettings <- list(useT1DM_Age = useT1DM_Age)
attr(covariateSettings, "fun") <- "getDbuseT1DM_AgeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Age for T1DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1DM_AgeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1DM_Age covariates")
if (covariateSettings$useT1DM_Age == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 40484648, 40484649))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Falta afegir la data de naixement a la cohort. S'ha de fer un INNER JOIN
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201254213 AS covariate_id,
# DATEDIFF(DAY, DATEFROMPARTS(cond2.year_of_birth, cond2.month_of_birth, cond2.day_of_birth),
# cond2.condition_start_date)/365.25 AS covariate_value
# FROM (SELECT cond.person_id,
# cond.condition_start_date,
# person_table.year_of_birth,
# person_table.month_of_birth,
# person_table.day_of_birth
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# INNER JOIN @cdm_database_schema.PERSON person_table
# ON person_table.person_id = cohort.subject_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201254213 AS covariate_id,
DATEDIFF(DAY, DATEFROMPARTS(person_table.year_of_birth, person_table.month_of_birth, person_table.day_of_birth),
MIN(cond.condition_start_date))/365.25 AS covariate_value
FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
JOIN @cdm_database_schema.PERSON person_table ON person_table.person_id = cond.person_id
WHERE
ca.ancestor_concept_id IN (201254, 435216, 40484648, 40484649)
AND cond.condition_start_date <= cohort.cohort_start_date
AND cohort.cohort_definition_id = @cohort_definition_id
GROUP BY cond.person_id, person_table.year_of_birth, person_table.month_of_birth, person_table.day_of_birth"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254213),
covariateName = c('Age from T1DM'),
analysisId = 213,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 213,
analysisName = "Age from T1DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T1DM diagnosis
#'
#' @param useT1Rx_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1Rx_TimeCovariateSettings <- function(useT1Rx_Time = TRUE){
covariateSettings <- list(useT1Rx_Time = useT1Rx_Time)
attr(covariateSettings, "fun") <- "getDbuseT1Rx_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T1Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1Rx_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1Rx_Time covariates")
if (covariateSettings$useT1Rx_Time == FALSE) {
return(NULL)
}
vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
35602717, 42899447, 46221581)
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = vec_insulin)
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# Some SQL to construct the covariate:
sql <- "SELECT
cond.person_id AS row_id,
201254216 AS covariate_id,
DATEDIFF(DAY, MIN(cond.DRUG_EXPOSURE_START_DATE), cohort.cohort_start_date) AS covariate_value
FROM @cdm_database_schema.DRUG_EXPOSURE cond
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.DRUG_CONCEPT_ID = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE ca.ancestor_concept_id IN (@diab_id)
AND cond.DRUG_EXPOSURE_START_DATE <= cohort.cohort_start_date
AND cohort.cohort_definition_id IN (@cohort_definition_id)
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId,
diab_id = vec_insulin)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254216),
covariateName = c('Time from T1Rx'),
analysisId = 216,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 216,
analysisName = "Time from T1Rx",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Age for first T1Rx drug
#'
#' @param useT1Rx_Age Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1Rx_AgeCovariateSettings <- function(useT1Rx_Age = TRUE){
covariateSettings <- list(useT1Rx_Age = useT1Rx_Age)
attr(covariateSettings, "fun") <- "getDbuseT1Rx_AgeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Age for T1Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1Rx_AgeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1Rx_Age covariates")
if (covariateSettings$useT1Rx_Age == FALSE) {
return(NULL)
}
vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
35602717, 42899447, 46221581)
included_sql <- SqlRender::render(sql = "SELECT *
FROM @schema_cdm.CONCEPT_ANCESTOR
WHERE ancestor_concept_id IN (@diab_id)",
schema_cdm = cdmDatabaseSchema,
diab_id = vec_insulin)
included_id <- DatabaseConnector::querySql(connection,
sql = included_sql)
# Falta afegir la data de naixement a la cohort. S'ha de fer un INNER JOIN
# Some SQL to construct the covariate:
sql <- "SELECT DISTINCT ON (cond2.person_id)
cond2.person_id AS row_id,
201254217 AS covariate_id,
DATEDIFF(DAY, DATEFROMPARTS(cond2.year_of_birth, cond2.month_of_birth, cond2.day_of_birth),
cond2.DRUG_EXPOSURE_START_DATE)/365.25 AS covariate_value
FROM (SELECT cond.person_id,
cond.DRUG_EXPOSURE_START_DATE,
person_table.year_of_birth,
person_table.month_of_birth,
person_table.day_of_birth
FROM @cdm_database_schema.DRUG_EXPOSURE cond
INNER JOIN @cohort_table cohort
ON cohort.subject_id = cond.person_id
INNER JOIN @cdm_database_schema.PERSON person_table
ON person_table.person_id = cohort.subject_id
WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
AND cond.DRUG_CONCEPT_ID != 0
AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId,
included_concept_table = included_id$DESCENDANT_CONCEPT_ID)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254217),
covariateName = c('Age from T1Rx'),
analysisId = 217,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 217,
analysisName = "Age from T1Rx",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Build a Follow-up Data for cohort
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#' @param bbdd_covar A data.table create by transformToFlat function
#'
#' @return A data.frame object
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' # Not yet
buildFollowUp <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId = 1,
bbdd_covar){
obs_per_sql <- "SELECT * FROM @omopSc.OBSERVATION_PERIOD
WHERE person_id IN (SELECT subject_id FROM @resultSc.@cohortTable)"
obs_per <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = obs_per_sql,
omopSc = cdm_schema,
resultSc = results_sc,
cohortTable = cohortTable))
death_sql <- "SELECT * FROM @omopSc.DEATH
WHERE person_id IN (SELECT subject_id FROM @resultSc.@cohortTable)"
death <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = death_sql,
omopSc = cdm_schema,
resultSc = results_sc,
cohortTable = cohortTable))
cohort <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = "SELECT * FROM @resultSc.@cohortTable",
resultSc = results_sc,
cohortTable = cohortTable))
cohort_event <- cohort[cohort$COHORT_DEFINITION_ID %in% c(acohortId, 3:18),]
cohort_event$event <- as.character(NA)
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == acohortId] <- 'dintro'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 3] <- 'AMI'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 4] <- 'Angor'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 5] <- 'StrokeI'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 6] <- 'TIA'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 7] <- 'Nephropathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 8] <- 'Retinopathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 9] <- 'Neuropathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 10] <- 'PAD'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 11] <- 'Angor_unstable'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 12] <- 'AMI_WP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 13] <- 'stroke_WP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 14] <- 'neuroWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 15] <- 'nephroWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 16] <- 'retinoWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 17] <- 'footWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 18] <- 'DKAWP4'
cohort_event_w <- tidyr::pivot_wider(data = cohort_event,
id_cols = "SUBJECT_ID",
names_from = 'event',
names_prefix = 'ep_',
values_from = "COHORT_START_DATE")
names(cohort_event_w)[names(cohort_event_w) == "ep_dintro"] <- 'dintro'
cohort_event_w <- merge(cohort_event_w,
obs_per[, c("PERSON_ID", "OBSERVATION_PERIOD_END_DATE")],
by.x = 'SUBJECT_ID',
by.y = 'PERSON_ID',
all.x = TRUE)
bbdd_covar <- merge(bbdd_covar,
cohort_event_w,
by.x = 'rowId',
by.y = "SUBJECT_ID",
all.x = TRUE)
bbdd_covar <- merge(bbdd_covar,
death,
by.x = 'rowId',
by.y = "PERSON_ID",
all.x = TRUE)
bbdd_covar$i.ep_AMI <- 0
bbdd_covar$i.ep_AMI[bbdd_covar$dintro < bbdd_covar$ep_AMI &
bbdd_covar$ep_AMI <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_AMI <- as.numeric(pmin(bbdd_covar$ep_AMI, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Angor <- 0
bbdd_covar$i.ep_Angor[bbdd_covar$dintro < bbdd_covar$ep_Angor &
bbdd_covar$ep_Angor <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Angor <- as.numeric(pmin(bbdd_covar$ep_Angor, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_StrokeI <- 0
bbdd_covar$i.ep_StrokeI[bbdd_covar$dintro < bbdd_covar$ep_StrokeI &
bbdd_covar$ep_StrokeI <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_StrokeI <- as.numeric(pmin(bbdd_covar$ep_StrokeI, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_TIA <- 0
bbdd_covar$i.ep_TIA[bbdd_covar$dintro < bbdd_covar$ep_TIA &
bbdd_covar$ep_TIA <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_TIA <- as.numeric(pmin(bbdd_covar$ep_TIA, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Nephropathy <- 0
bbdd_covar$i.ep_Nephropathy[bbdd_covar$dintro < bbdd_covar$ep_Nephropathy &
bbdd_covar$ep_Nephropathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Nephropathy <- as.numeric(pmin(bbdd_covar$ep_Nephropathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Retinopathy <- 0
bbdd_covar$i.ep_Retinopathy[bbdd_covar$dintro < bbdd_covar$ep_Retinopathy &
bbdd_covar$ep_Retinopathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Retinopathy <- as.numeric(pmin(bbdd_covar$ep_Retinopathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Neuropathy <- 0
bbdd_covar$i.ep_Neuropathy[bbdd_covar$dintro < bbdd_covar$ep_Neuropathy &
bbdd_covar$ep_Neuropathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Neuropathy <- as.numeric(pmin(bbdd_covar$ep_Neuropathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_PAD <- 0
bbdd_covar$i.ep_PAD[bbdd_covar$dintro < bbdd_covar$ep_PAD &
bbdd_covar$ep_PAD <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_PAD <- as.numeric(pmin(bbdd_covar$ep_PAD, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Angor_unstable <- 0
bbdd_covar$i.ep_Angor_unstable[bbdd_covar$dintro < bbdd_covar$ep_Angor_unstable &
bbdd_covar$ep_Angor_unstable <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Angor_unstable <- as.numeric(pmin(bbdd_covar$ep_Angor_unstable, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_AMI_WP4 <- 0
bbdd_covar$i.ep_AMI_WP4[bbdd_covar$dintro < bbdd_covar$ep_AMI_WP4 &
bbdd_covar$ep_AMI_WP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_AMI_WP4 <- as.numeric(pmin(bbdd_covar$ep_AMI_WP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_stroke_WP4 <- 0
bbdd_covar$i.ep_stroke_WP4[bbdd_covar$dintro < bbdd_covar$ep_stroke_WP4 &
bbdd_covar$ep_stroke_WP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_stroke_WP4 <- as.numeric(pmin(bbdd_covar$ep_stroke_WP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_neuroWP4 <- 0
bbdd_covar$i.ep_neuroWP4[bbdd_covar$dintro < bbdd_covar$ep_neuroWP4 &
bbdd_covar$ep_neuroWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_neuroWP4 <- as.numeric(pmin(bbdd_covar$ep_neuroWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_nephroWP4 <- 0
bbdd_covar$i.ep_nephroWP4[bbdd_covar$dintro < bbdd_covar$ep_nephroWP4 &
bbdd_covar$ep_nephroWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_nephroWP4 <- as.numeric(pmin(bbdd_covar$ep_nephroWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_retinoWP4 <- 0
bbdd_covar$i.ep_retinoWP4[bbdd_covar$dintro < bbdd_covar$ep_retinoWP4 &
bbdd_covar$ep_retinoWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_retinoWP4 <- as.numeric(pmin(bbdd_covar$ep_retinoWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_footWP4 <- 0
bbdd_covar$i.ep_footWP4[bbdd_covar$dintro < bbdd_covar$ep_footWP4 &
bbdd_covar$ep_footWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_footWP4 <- as.numeric(pmin(bbdd_covar$ep_footWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_DKAWP4 <- 0
bbdd_covar$i.ep_DKAWP4[bbdd_covar$dintro < bbdd_covar$ep_DKAWP4 &
bbdd_covar$ep_DKAWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_DKAWP4 <- as.numeric(pmin(bbdd_covar$ep_DKAWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
return(bbdd_covar)
}
#' Auxiliar function to create Time form C10 diagnosis
#'
#' @param useC10_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createC10_TimeCovariateSettings <- function(useC10_Time = TRUE){
covariateSettings <- list(useC10_Time = useC10_Time)
attr(covariateSettings, "fun") <- "getDbuseC10_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from C10 diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseC10_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing C10_Time covariates")
if (covariateSettings$useC10_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(21601853))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 21601853214 AS covariate_id,
# DATEDIFF(DAY, cond2.DRUG_EXPOSURE_START_DATE, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.DRUG_EXPOSURE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.DRUG_CONCEPT_ID != 0
# AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- "SELECT
de.person_id AS row_id,
21601853214 AS covariate_id,
DATEDIFF(day, MIN(de.drug_era_start_date), r.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.drug_era de
JOIN @cdm_database_schema.concept c ON de.drug_concept_id = c.concept_id
JOIN @cdm_database_schema.concept_ancestor ca ON c.concept_id = ca.descendant_concept_id
JOIN @cohort_table r ON de.person_id = r.subject_id
WHERE
ca.ancestor_concept_id = 21601853
AND r.cohort_definition_id = @cohort_definition_id
AND de.drug_era_start_date <= r.cohort_start_date
GROUP BY
de.person_id, r.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(21601853214),
covariateName = c('Time from C10'),
analysisId = 214,
conceptId = 21601853)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 214,
analysisName = "Time from C10",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form HTN medication diagnosis
#'
#' @param useHTNRx_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createHTNRx_TimeCovariateSettings <- function(useHTNRx_Time = TRUE){
covariateSettings <- list(useHTNRx_Time = useHTNRx_Time)
attr(covariateSettings, "fun") <- "getDbuseHTNRx_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from HTN Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseHTNRx_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing HTNRx_Time covariates")
if (covariateSettings$useHTNRx_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(21600381, #C02
# 21601461, #C03
# 21601664, #C07
# 21601744, #C08
# 21601782)) #C09
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 21600381215 AS covariate_id,
# DATEDIFF(DAY, cond2.DRUG_EXPOSURE_START_DATE, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.DRUG_EXPOSURE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.DRUG_CONCEPT_ID != 0
# AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- "SELECT
de.person_id AS row_id,
21600381215 AS covariate_id,
DATEDIFF(day, MIN(de.drug_era_start_date), r.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.drug_era de
JOIN @cdm_database_schema.concept c ON de.drug_concept_id = c.concept_id
JOIN @cdm_database_schema.concept_ancestor ca ON c.concept_id = ca.descendant_concept_id
JOIN @cohort_table r ON de.person_id = r.subject_id
WHERE
ca.ancestor_concept_id IN (21600381, 21601461, 21601664, 21601744, 21601782)
AND r.cohort_definition_id = @cohort_definition_id
AND de.drug_era_start_date <= r.cohort_start_date
GROUP BY
de.person_id, r.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(21600381215),
covariateName = c('Time from HTNRx'),
analysisId = 215,
conceptId = 21600381)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 215,
analysisName = "Time from HTNRX",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Funcion para pasar del servidor a una tabla plana
#' Necesita totes les coses del servidor més quina cohort volem contruir.
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#'
#' @return List with three objects: descriptive for numeric and category and database ready to analysis.
#' @export
#'
#' @examples
#' #Not yet
FunCovar <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId){
covariateData_aux <- buildData(cdm_bbdd = cdm_bbdd,
cdm_schema = cdm_schema,
results_sc = results_sc,
cohortTable = cohortTable,
acohortId = acohortId)
covariateData_aux_temp <- covariateData_aux$covariateData_temp
covariateData_aux <- covariateData_aux$covariateData
# #Calculem el MPR pel T1DM i els eliminem si MPR inferior a 90%
# if (acohortId == 2){
# sql_cohort <- "SELECT * FROM sophia_test.cohorttable WHERE cohort_definition_id = 2"
# cohort <- DatabaseConnector::querySql(connection = cdm_bbdd,
# sql = sql_cohort)
# cohort$time_obs <- with(cohort, as.numeric(COHORT_END_DATE - COHORT_START_DATE + 1))
# vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
# 19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
# 42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
# 42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
# 1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
# 1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
# 19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
# 35602717, 42899447, 46221581)
# sql_insulin <- "SELECT * FROM omop21t2_test.DRUG_ERA WHERE person_id IN (@id) AND DRUG_CONCEPT_ID IN (@insulin)"
# insulin <- DatabaseConnector::querySql(connection = cdm_bbdd,
# sql = SqlRender::render(sql_insulin,
# id = cohort$SUBJECT_ID,
# insulin = vec_insulin))
# insulin_treat <- dplyr::arrange(.data = insulin,
# PERSON_ID, DRUG_ERA_START_DATE)
# n_prev <- pull(count(insulin_treat), n)
# cond <- TRUE
# while(cond){
# insulin_treat <- group_by(insulin_treat, PERSON_ID)
# insulin_treat <- mutate(insulin_treat,
# canvi = lag(DRUG_ERA_END_DATE) < DRUG_ERA_START_DATE,
# canvi = dplyr::if_else(is.na(canvi), FALSE, canvi),
# treat = cumsum(canvi))
# insulin_treat <- group_by(insulin_treat, PERSON_ID, treat)
# insulin_treat <- summarise(insulin_treat,
# DRUG_ERA_START_DATE = min(DRUG_ERA_START_DATE),
# DRUG_ERA_END_DATE = max(DRUG_ERA_END_DATE),
# .groups = 'drop')
# n_act <- pull(count(insulin_treat), n)
# # cat('Prev: ', n_prev, ' Act: ', n_act, '\n')
# cond <- n_prev > n_act
# n_prev <- n_act
# }
# insulin_treat <- mutate(insulin_treat,
# time = as.numeric(DRUG_ERA_END_DATE - DRUG_ERA_START_DATE + 1))
# cohort <- dplyr::left_join(cohort,
# insulin_treat,
# by = c('SUBJECT_ID' = 'PERSON_ID'))
# cohort$MPR <- with(cohort, time/time_obs)
# cohort <- dplyr::filter(cohort, 0.75 < MPR)
# covariateData_aux_temp <- dplyr::filter(covariateData_aux_temp,
# rowId %in% cohort$SUBJECT_ID)
# covariateData_aux <- dplyr::filter(covariateData_aux,
# rowId %in% cohort$SUBJECT_ID)
# }
covariateData2_aux <- FeatureExtraction::aggregateCovariates(covariateData_aux)
sel_med_conceptId <- c(21600712, #DRUGS USED IN DIABETES
#Aquestes insulines no les troba
21076306, 44058584, 21086042, 21036596,
21601238, #C01
21600381, #C02
21601461, #C03
21601664, #C07
21601744, #C08
21601782, #C09
21601853, #C10
21603933 #M01A
)
cov_cate_resum_aux <- dplyr::filter(
.data = covariateData2_aux$covariateRef,
.data$analysisId %in% c(411, 413) & .data$conceptId %in% sel_med_conceptId |
!(.data$analysisId %in% c(411, 413)))
cov_cate_resum_aux <- dplyr::inner_join(
x = cov_cate_resum_aux,
y = covariateData2_aux$covariates)
cov_cate_resum_aux <- dplyr::mutate(
.data = cov_cate_resum_aux,
covariateId = as.character(floor(.data$covariateId)),
analysisId = as.integer(.data$analysisId),
conceptId = as.integer(.data$conceptId),
sumValue = as.integer(.data$sumValue),
averageValue = .data$averageValue*100)
cov_cate_resum_aux <-dplyr::collect(x = cov_cate_resum_aux)
cov_num_resum_aux <- dplyr::inner_join(
x = covariateData2_aux$covariateRef,
y = covariateData2_aux$covariatesContinuous)
cov_num_resum_aux <- dplyr::mutate(
.data = cov_num_resum_aux,
covariateId = as.character(floor(.data$covariateId)),
analysisId = as.integer(.data$analysisId),
conceptId = as.integer(.data$conceptId))
cov_num_resum_aux <- dplyr::select(
.data = cov_num_resum_aux,
-.data$covariateId, -.data$analysisId, -.data$conceptId)
cov_num_resum_aux <- dplyr::collect(x = cov_num_resum_aux)
bbdd_covar_aux <- transformToFlat(covariateData_aux,
covariateData_aux_temp)
bbdd_covar_aux <- buildFollowUp(cdm_bbdd = cdm_bbdd,
cdm_schema = cdm_schema,
results_sc = results_sc,
cohortTable = cohortTable,
acohortId = acohortId,
bbdd_covar = bbdd_covar_aux)
return(list(cov_cate_resum = cov_cate_resum_aux,
cov_num_resum = cov_num_resum_aux,
bbdd_covar = bbdd_covar_aux))
}
Blanch-Font/SOPHIA documentation built on May 12, 2023, 1:09 a.m.
R Package Documentation
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Defines functions FunCovar getDbuseHTNRx_TimeCovariateData createHTNRx_TimeCovariateSettings getDbuseC10_TimeCovariateData createC10_TimeCovariateSettings buildFollowUp getDbuseT1Rx_AgeCovariateData createT1Rx_AgeCovariateSettings getDbuseT1Rx_TimeCovariateData createT1Rx_TimeCovariateSettings getDbuseT1DM_AgeCovariateData createT1DM_AgeCovariateSettings getDbuseT1DM_TimeCovariateData createT1DM_TimeCovariateSettings getDbuseT2DM_TimeCovariateData createT2DM_TimeCovariateSettings getDbSmokingCovariateData createSmokingCovariateSettings transformToFlat buildData
Documented in buildData buildFollowUp createC10_TimeCovariateSettings createHTNRx_TimeCovariateSettings createSmokingCovariateSettings createT1DM_AgeCovariateSettings createT1DM_TimeCovariateSettings createT1Rx_AgeCovariateSettings createT1Rx_TimeCovariateSettings createT2DM_TimeCovariateSettings FunCovar getDbSmokingCovariateData getDbuseC10_TimeCovariateData getDbuseHTNRx_TimeCovariateData getDbuseT1DM_AgeCovariateData getDbuseT1DM_TimeCovariateData getDbuseT1Rx_AgeCovariateData getDbuseT1Rx_TimeCovariateData getDbuseT2DM_TimeCovariateData transformToFlat
#' Build a Covariate Data for cohort
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#'
#' @return A covariateData object
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' # Not yet
buildData <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId = 1){
covDemo <- FeatureExtraction::createCovariateSettings(
useDemographicsGender = TRUE,
useDemographicsAge = TRUE)
BMI_conceptId <- c(3038553, 40762638) #LOINC
height_conceptId <- c(3036277, 3015514) #LOINC
weight_conceptId <- c(3025315) #LOINC
covMeasValueAny <- FeatureExtraction::createCovariateSettings(
useMeasurementValueAnyTimePrior = TRUE,
includedCovariateConceptIds = c(BMI_conceptId,
height_conceptId,
weight_conceptId),
addDescendantsToInclude = TRUE)
SBP_conceptId <- c(3004249, #LOINC
4152194) #SNOMED
DBP_conceptId <- c(3012888, #LOINC
4154790) #SNOMED
cT_conceptId <- c(3019900, 3027114) #LOINC
cHDL_conceptId <- c(3011884, 3007070, 3023602) #LOINC
cLDL_conceptId <- c(3028437, 3001308) #LOINC
cVLDL_conceptId <- c(3022487) #LOINC
Tg_conceptId <- c(3022192, 42868692) #LOINC
glu_conceptId <- c(3004501,
46235168, 40757523, 3005834, 40757527, 3016567, 40757528, 40757529,
40757627, 40757628,
3018251, 46236948,
3015024, 3036895, 3001022, 3016701, 3018582, 3008799, 3045700,
3020491) #LOINC
alt_conceptId <- c(3006923, 46235106)
CRP_conceptId <- c(3020460) #LOINC
ferritin_conceptId <- c(3001122) #LOINC
WBC_conceptId <- c(3010813) #LOINC
neutrophils_conceptId <- c(3017732, 3046321, 43055364) #LOINC
basophils_conceptId <- c(3006315, 43055368) #LOINC
eosinophils_conceptId <- c(3013115, 43055367) #LOINC
monocytes_conceptId <- c(3001604, 43055365) #LOINC
lymphocytes_conceptId <- c(3019198, 43055366) #LOINC
HbA1c_conceptId <- c(3034639, 3004410) #LOINC
creatinine_conceptId <- c(3016723)
albumin_ser_conceptId <- c(3024561)
CKDEPI_conceptId <- c(40764999)
GOT_conceptId <- c(3010587)
GGT_conceptId <- c(3026910)
vitD_conceptId <- c(43055034)
PEPTIDCs_conceptId <- c(3010084)
hemoglobin_conceptId <- c(3000963)
covMeasValueLong <- FeatureExtraction::createCovariateSettings(
useMeasurementValueLongTerm = TRUE,
longTermStartDays = (-2)*365.25,
endDays = 0*365.25,
includedCovariateConceptIds = c(
SBP_conceptId,
DBP_conceptId,
# cT_conceptId,
# cHDL_conceptId,
# cLDL_conceptId,
# cVLDL_conceptId,
# Tg_conceptId,
glu_conceptId,
alt_conceptId,
CRP_conceptId,
ferritin_conceptId,
WBC_conceptId,
neutrophils_conceptId,
basophils_conceptId,
eosinophils_conceptId,
monocytes_conceptId,
lymphocytes_conceptId,
HbA1c_conceptId,
creatinine_conceptId,
albumin_ser_conceptId,
CKDEPI_conceptId,
GOT_conceptId,
GGT_conceptId,
vitD_conceptId,
PEPTIDCs_conceptId,
hemoglobin_conceptId),
addDescendantsToInclude = TRUE)
covMeasValue_lipid <- FeatureExtraction::createTemporalCovariateSettings(
useMeasurementValue = TRUE,
temporalStartDays = c(-2, 1, 3/12)*365.25,
temporalEndDays = c(0, 2, 1)*365.25,
includedCovariateConceptIds = c(
cT_conceptId,
cHDL_conceptId,
cLDL_conceptId,
cVLDL_conceptId,
Tg_conceptId),
addDescendantsToInclude = TRUE)
T2DM_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 111,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 111,
analysisName = "T2DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(201826, 443732, 40482801, 40485020),
addDescendantsToInclude = TRUE)
obesity_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 112,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 112,
analysisName = "Obesity",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(433736),
addDescendantsToInclude = TRUE)
angor_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 113,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 113,
analysisName = "Angina pectoris",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(#I20
321318, #Angina pectoris
315296, #Preinfarction syndrome
4127089), #Coronary artery spasm,
addDescendantsToInclude = FALSE)
ami_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 114,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 114,
analysisName = "AMI",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(312327, #Acute myocardial infarction
4296653, #Acute ST segment elevation myocardial infarction
45766075, #Acute anterior ST segment elevation myocardial infarction
45766116, #Acute ST segment elevation myocardial infarction of inferior wall
4296653, #Acute ST segment elevation myocardial infarction
4270024, #Acute non-ST segment elevation myocardial infarction
4329847, #Myocardial infarction
#I22
4108217, #Subsequent myocardial infarction
4108677, #Subsequent myocardial infarction of anterior wall
4108218, #Subsequent myocardial infarction of inferior wall
45766241, #Subsequent non-ST segment elevation myocardial infarction
45766114, #Subsequent ST segment elevation myocardial infarction
#I23
4108678, #Hemopericardium due to and following acute myocardial infarction
438172, #Atrial septal defect due to and following acute myocardial infarction
4119953, #Post-infarction ventricular septal defect
4108679, #Rupture of cardiac wall without hemopericardium as current complication following acute myocardial infarction
4108219, #Rupture of chordae tendinae due to and following acute myocardial infarction
4108220, #Rupture of papillary muscle as current complication following acute myocardial infarction
4108680, #Thrombosis of atrium, auricular appendage, and ventricle due to and following acute myocardial infarction
4198141, #Post infarct angina
# Altres
434376, #Acute myocardial infarction of anterior wall
438170, # Acute myocardial infarction of inferior wall
4176969, #Sequelae of cardiovascular disorders
37309626, #Myocardial infarction due to demand ischemia
43020460, #Acute ST segment elevation myocardial infarction involving left anterior descending coronary artery
46270162, # Acute ST segment elevation myocardial infarction due to left coronary artery occlusion
46270163),
addDescendantsToInclude = FALSE)
stroke_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 115,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 115,
analysisName = "Stroke",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(
#I63
443454, #Cerebral infarction
4110189, #Cerebral infarct due to thrombosis of precerebral arteries
4110190, #Cerebral infarction due to embolism of precerebral arteries
4043731, #Infarction - precerebral
4110192, #Cerebral infarction due to thrombosis of cerebral arteries
4108356, #Cerebral infarction due to embolism of cerebral arteries
4111714, #Cerebral infarction due to cerebral venous thrombosis, non-pyogenic
#I64
#I65
43022059, #Disease of non-coronary systemic artery
4153380, #Disorder of carotid artery
4159164, #Disorder of basilar artery
443239, #Precerebral arterial occlusion
#Altres
255919, #Finding of head and neck region
313226, #Carotid artery occlusion
321887, #Disorder of artery
381316, #Cerebrovascular accident
381591, #Cerebrovascular disease
4006294, #Basilar artery embolism
4028073, #Disorder of artery of neck
4213731, #Carotid artery embolism
4273526, #Vertebral artery thrombosis
4274969, #Vertebral artery embolism
4288310, #Carotid artery obstruction
4311124, #Carotid artery thrombosis
4338227, #Basilar artery thrombosis
45767658, #Cerebral infarction due to thrombosis of middle cerebral artery
45772786, #Cerebral infarction due to embolism of middle cerebral artery
46270031, #Cerebral infarction due to occlusion of precerebral artery
46273649 #Cerebral infarction due to occlusion of basilar artery
),
addDescendantsToInclude = FALSE)
TIA_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 116,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 116,
analysisName = "TIA",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(#G45
373503, #Transient cerebral ischemia
437306, #Transient global amnesia
4338523, #Amaurosis fugax
381036, # Multiple AND bilateral precerebral artery stenosis
4112020, #Carotid artery syndrome hemispheric
4048785, #Vertebrobasilar territory transient ischemic attack
#G46
381591, #Cerebrovascular disease
4110194, #Middle cerebral artery syndrome
4108360, #Anterior cerebral artery syndrome
4110195, #Posterior cerebral artery syndrome
4111710, #Brainstem stroke syndrome
4111711, #Cerebellar stroke syndrome
4045737, #Pure motor lacunar infarction
4045738, #Pure sensory lacunar infarction
4046360 #Lacunar infarction
),
addDescendantsToInclude = FALSE)
COPD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 117,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 117,
analysisName = "COPD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(255841, 261325, 255573),
addDescendantsToInclude = TRUE)
CKD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 118,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 118,
analysisName = "CKD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(46271022, 192359),
addDescendantsToInclude = TRUE)
cancer_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 119,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 119,
analysisName = "Cancer",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(443392, 4144289, 439392, 200962, 139750, 4311499, 137809, 197500),
addDescendantsToInclude = TRUE)
depress_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 120,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 120,
analysisName = "Depress",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(4282096, 4282316, 433440),
addDescendantsToInclude = TRUE)
htn_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 121,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 121,
analysisName = "HTN",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(320128, 442604, 444101, 319034, 443919, 44782429, 44784621,
439696, 319826, 317895, 443771, 4110948, 319826),
addDescendantsToInclude = TRUE)
hf_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 122,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 122,
analysisName = "HF",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(316139, 319835, 439846, 443580, 443587, 4229440, 4273632, 40479192,
40479576, 40480602, 40480603, 40481042, 40481043, 40482727, 44782718,
44782733),
addDescendantsToInclude = TRUE)
liver_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 123,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 123,
analysisName = "LiverFailure",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(4098652, 197795, 4211974, 4012113,
192240, 192242, 193693, 196625, 197490, 197494, 198683,
198964, 439673, 439674, 439675,
4245975, 200763, 4267417, 194990, 194984,
192675, 192680, 196455, 199867, 200762,
201901, 377604, 4026125, 4046123, 4058696, 4059290, 4064161,
4135822, 4238978, 4240725, 4313846, 4340390,
4340394, 4340941, 4340948, 40484532, 46269836),
addDescendantsToInclude = TRUE)
ra_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 124,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 124,
analysisName = "RA",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(36684997, 80809), #M05, M06
addDescendantsToInclude = TRUE)
sleep_apnea_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 125,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 125,
analysisName = "SleepApnea",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(313459), #G47.3
addDescendantsToInclude = TRUE)
pcos_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 126,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 126,
analysisName = "PCOS",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(40443308), #E28.2
addDescendantsToInclude = TRUE)
T1DM_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 127,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 127,
analysisName = "T1DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(201254, 435216, 40484648, 40484649),
addDescendantsToInclude = TRUE)
nephro_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 128,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 128,
analysisName = "Nephropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(192279, #Disorder of kidney due to diabetes mellitus
200687, #Renal disorder due to type 1 diabetes mellitus
443731, #Renal disorder due to type 2 diabetes mellitus
43531578 #Chronic kidney disease due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
retino_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 129,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 129,
analysisName = "Retinopathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(376114, #Severe nonproliferative retinopathy due to diabetes mellitus
376979, #Cataract due to diabetes mellitus
377552, #Moderate nonproliferative retinopathy due to diabetes mellitus
378743, #Mild nonproliferative retinopathy due to diabetes mellitus
380096, # Proliferative retinopathy due to diabetes mellitus
380097, # Macular edema due to diabetes mellitus
443733, #Disorder of eye due to type 2 diabetes mellitus
443767, #Disorder of eye due to diabetes mellitus
4174977, #Retinopathy due to diabetes mellitus
4221495, #Cataract due to diabetes mellitus type 2
4225656, #Cataract due to diabetes mellitus type 1
4227210, #Retinopathy due to type 1 diabetes mellitus
4266637, #Severe nonproliferative retinopathy without macular edema due to diabetes mellitus
4290822, #Severe nonproliferative retinopathy with clinically significant macular edema due to diabetes mellitus
4338896, #Traction retinal detachment involving macula
4338897, #Combined traction and rhegmatogenous retinal detachment
4338901, #Traction detachment of retina due to diabetes mellitus
37016179, #Mild nonproliferative retinopathy due to type 1 diabetes mellitus
37016180, #Moderate nonproliferative retinopathy due to type 1 diabetes mellitus
42538169, #Disorder of eye due to type 1 diabetes mellitus
43530656, #Nonproliferative retinopathy due to type 2 diabetes mellitus
43530685, #Proliferative retinopathy due to type 2 diabetes mellitus
45757435, # Mild nonproliferative retinopathy due to type 2 diabetes mellitus
45763583, #Nonproliferative diabetic retinopathy due to type 1 diabetes mellitus
45763584, #Proliferative retinopathy due to type 1 diabetes mellitus
45769873, #Traction detachment of retina due to type 1 diabetes mellitus
45770830, #Macular edema and retinopathy due to type 2 diabetes mellitus
45770881, #Moderate nonproliferative retinopathy due to type 2 diabetes mellitus
45773064 #Traction detachment of retina due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
neuro_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 130,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 130,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(376065, #Disorder of nervous system due to type 2 diabetes mellitus
376112, #Polyneuropathy due to diabetes mellitus
377821, #Disorder of nervous system due to type 1 diabetes mellitus
443730, #Disorder of nervous system due to diabetes mellitus
4044391, #Neuropathy due to diabetes mellitus
4048028, #Diabetic mononeuropathy
4140466, #Lumbosacral radiculoplexus neuropathy due to type 2 diabetes mellitus
4143857, #Lumbosacral radiculoplexus neuropathy due to type 1 diabetes mellitus
4175440, #Autonomic neuropathy due to diabetes mellitus
4191611, #Lumbosacral radiculoplexus neuropathy due to diabetes mellitus
4222415, #Mononeuropathy due to type 2 diabetes mellitus
4225055, #Mononeuropathy due to type 1 diabetes mellitus
37016767, #Autonomic neuropathy due to type 1 diabetes mellitus
37016768, #Autonomic neuropathy due to type 2 diabetes mellitus
37017431, #Polyneuropathy due to type 1 diabetes mellitus
37017432 #Polyneuropathy due to type 2 diabetes mellitus
),
addDescendantsToInclude = FALSE)
PAD_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 131,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 131,
analysisName = "PAD",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(
74719, #Ulcer of foot
134380, #Erythromelalgia
# 138525, #Pain in limb
195834, #Atherosclerosis of renal artery
197304, #Ulcer of lower extremity
312934, #Atherosclerosis of aorta
314965, #Embolism and thrombosis of an arm or leg artery
315558, #Atherosclerosis of arteries of the extremities
317577, #Arteriosclerotic gangrene
318443, #Arteriosclerotic vascular disease
321052, #Peripheral vascular disease
321882, #Generalized atherosclerosis
442287, #Rest pain
442774, #Intermittent claudication
443358, #Ulcer of heel
443593, #Ulcer of thigh
4171556, #Ankle ulcer
4177703, #Ulcer
4316222, #Venous intermittent claudication
4325344, #Pain at rest due to peripheral vascular disease
35611566, #Bilateral lower limb atherosclerosis pain at rest co-occurrent and due to atherosclerosis
35615028, #Bilateral atherosclerosis of lower limbs with gangrene
36712805, #Pain at rest of left lower limb co-occurrent and due to atherosclerosis
36712806, #Intermittent claudication of right lower limb co-occurrent and due to atherosclerosis
36712807, #Pain at rest of right lower limb co-occurrent and due to atherosclerosis
36712963, #Gangrene of left lower limb due to atherosclerosis
36717006, #Intermittent claudication of bilateral lower limbs co-occurrent and due to atherosclerosis
36717279, #Gangrene of right lower limb due to atherosclerosis
36717286, #Intermittent claudication of left lower limb co-occurrent and due to atherosclerosis
37110250, #Atherosclerosis of artery of lower limb
37312519, #Ulcer of calf due to atherosclerosis of artery of lower limb
37312520, #Ischemic foot ulcer due to atherosclerosis of artery of lower limb
37312524, #Ulcer of ankle due to atherosclerosis of artery of lower limb
37312529, #Intermittent claudication due to atherosclerosis of artery of limb
37312531, #Gangrene of limb due to atherosclerosis of artery of limb
40479625, #Atherosclerosis of artery
40483538, #Atherosclerosis of bypass graft of limb
40484541, #Atherosclerosis of autologous vein bypass graft of limb
40484551, #Atherosclerosis of nonautologous biological bypass graft of limb
44782819, #Chronic occlusion of artery of extremity
46271459 #Atherosclerosis of bypass graft of lower limb
),
addDescendantsToInclude = FALSE)
dka_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 132,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 132,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(439770, #Ketoacidosis due to type 1 diabetes mellitus
4009303, #Diabetic ketoacidosis without coma
4224254 #Ketoacidotic coma due to type 1 diabetes mellitus
),
addDescendantsToInclude = FALSE)
hypoglyc_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 133,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 133,
analysisName = "Neuropathy due to DM",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(45769876, #Hypoglycemia due to type 1 diabetes mellitus
4228112 #Hypoglycemic coma due to type 1 diabetes mellitus
),
addDescendantsToInclude = FALSE)
hypoglyc_vars <- FeatureExtraction::createAnalysisDetails(
analysisId = 134,
sqlFileName = "DomainConcept.sql",
parameters = list(analysisId = 134,
analysisName = "Anaemia",
startDay = "anyTimePrior",
endDay = 0,
subType = "all",
domainId = "Condition",
domainTable = "condition_occurrence",
domainConceptId = "condition_concept_id",
domainStartDate = "condition_start_date",
domainEndDate = "condition_start_date"),
includedCovariateConceptIds = c(439777 #Anemia
),
addDescendantsToInclude = FALSE)
A10_conceptId <- c(21600712,
782681, 793321, 1502829, 1502830, 1503327, 1525221, 1529352,
1547554, 1596977, 1597761, 1597772, 1597773, 1597781, 1597792,
19006931, 19021312, 19023424, 19023425, 19023426, 19029030, 19029061,
19058398, 19059800, 19077638, 19077682, 19078552, 19078559, 19079293,
19079465, 19095211, 19095212, 19099055, 19101729, 19112791, 19125041,
19125045, 19125049, 19129179, 19133793, 19135264, 21022404, 21036596,
21061594, 21061613, 21076306, 21081251, 21086042, 21100924, 21114195,
21133671, 21169719, 35408233, 35410536, 35412102, 35412890, 35412958,
36403507, 36403509, 36884964, 40044221, 40051377, 40052768, 40054707,
40139098, 40164885, 40164888, 40164891, 40164897, 40164913, 40164916,
40164942, 40164943, 40164946, 40166037, 40166041, 40239218, 42479624,
42479783, 42481504, 42481541, 42482012, 42482588, 42656231, 42656236,
42656240, 42708086, 42708090, 42899447, 42902356, 42902587, 42902742,
42902821, 42902945, 42902992, 42903059, 42903341, 43013885, 43013905,
43013911, 43013918, 43013924, 43013928, 43526467, 43526471, 44032735,
44058584, 44123708, 44785831, 45774709, 45774754, 45774893, 46233969,
46234047, 46234234, 46234237, 46287408, 46287689)
sel_med_conceptId <- c(21600712, #DRUGS USED IN DIABETES
#Aquestes insulines no les troba
21076306, 44058584, 21086042, 21036596,
21601238, #C01
21600381, #C02
21601461, #C03
21601664, #C07
21601744, #C08
21601782, #C09
21601853, #C10
21603933, #M01A
# Antiinflamatoris
920458, 1103374, 1115008, 1118084, 1124300, 1125315, 1146810, 1150345,
1153928, 1156378, 1177480, 1178663, 1185922, 1189754, 1518254, 19029393,
19056874, 19110711,
# Antiagregants
1310149, 1325124, 19024063, 1301025, 1301065, 1308473, 1367571, 1436169,
1112807, 1302398, 1322184, 1331270, 1350310, 19042778, 19047423, 19069107,
40241186
)
covDrug <- FeatureExtraction::createCovariateSettings(
useDrugGroupEraMediumTerm = TRUE,
mediumTermStartDays = -365.25,
endDays = 0,
includedCovariateConceptIds = sel_med_conceptId,
addDescendantsToInclude = TRUE)
# smoking_vars <- FeatureExtraction::createAnalysisDetails(
# analysisId = 810,
# sqlFileName = "DomainConcept.sql",
# parameters = list(analysisId = 810,
# analysisName = "Tobacco",
# startDay = "anyTimePrior",
# endDay = 1,
# subType = "last",
# domainId = "Observation",
# domainTable = "observation",
# domainConceptId = "VALUE_AS_CONCEPT_ID",
# domainStartDate = "observation_date",
# domainEndDate = ""),
# includedCovariateConceptIds = c(45879404, 45884037, 45883458),
# addDescendantsToInclude = TRUE)
SmokingCovSet <- createSmokingCovariateSettings(useSmoking = TRUE)
T2DM_TimeCovSet <- createT2DM_TimeCovariateSettings(useT2DM_Time = TRUE)
T1DM_TimeCovSet <- createT1DM_TimeCovariateSettings(useT1DM_Time = TRUE)
T1DM_AgeCovSet <- createT1DM_AgeCovariateSettings(useT1DM_Age = TRUE)
T1Rx_TimeCovSet <- createT1Rx_TimeCovariateSettings(useT1Rx_Time = TRUE)
# T1Rx_AgeCovSet <- createT1Rx_AgeCovariateSettings(useT1Rx_Age = TRUE)
C10_TimeCovSet <- createC10_TimeCovariateSettings(useC10_Time = TRUE)
HTNRx_TimeCovSet <- createHTNRx_TimeCovariateSettings(useHTNRx_Time = TRUE)
covariateSettings <- list(covDemo,
covMeasValueAny,
covMeasValueLong,
# covMeasValue_lipid,
FeatureExtraction::createDetailedCovariateSettings(
list(T2DM_vars,
obesity_vars,
angor_vars,
ami_vars,
stroke_vars,
TIA_vars,
COPD_vars,
CKD_vars,
cancer_vars,
depress_vars,
htn_vars,
hf_vars,
liver_vars,
ra_vars,
sleep_apnea_vars,
pcos_vars,
T1DM_vars,
nephro_vars,
retino_vars,
neuro_vars,
PAD_vars,
dka_vars,
hypoglyc_vars)),
covDrug,
SmokingCovSet,
T2DM_TimeCovSet,
T1DM_TimeCovSet,
T1Rx_TimeCovSet,
T1DM_AgeCovSet,
# T1Rx_AgeCovSet,
C10_TimeCovSet,
HTNRx_TimeCovSet)
covariateData <- FeatureExtraction::getDbCovariateData(
connection = cdm_bbdd,
cdmDatabaseSchema = cdm_schema,
cohortDatabaseSchema = results_sc,
cohortTable = cohortTable,
cohortId = acohortId,
rowIdField = "subject_id",
covariateSettings = covariateSettings)
covariateData_temp <- FeatureExtraction::getDbCovariateData(
connection = cdm_bbdd,
cdmDatabaseSchema = cdm_schema,
cohortDatabaseSchema = results_sc,
cohortTable = cohortTable,
cohortId = acohortId,
rowIdField = "subject_id",
covariateSettings = covMeasValue_lipid)
# T2DM_conceptId <- c(201530111, 201826111, 376065111, 443729111, 443731111, 443733111,
# 4193704111, 4196141111, 4221495111, 36714116111, 37016349111,
# 37017432111, 43530685111, 43530690111, 43531563111, 43531578111,
# 45770830111,
# # Afegits executant el SIDIAP
# 4099651111, 45757363111, 4140466111, 37016768111, 4222876111, 43531616111,
# 45770881111)
# DM_conceptId <- c(442793111, 321822111, 443730111, 192279111, 4048028111, 4226798111,
# 201820111, 4008576111, 443767111,
# # Afegits executant el SIDIAP
# 4044391111, 4009303111, 376112111, 4159742111, 4114427111, 4131908111)
# obesity_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 112),
# .data$covariateId)
# angina_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 113),
# .data$covariateId)
# ami_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 114),
# .data$covariateId)
# stroke_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 115),
# .data$covariateId)
# tia_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 116),
# .data$covariateId)
# COPD_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 117),
# .data$covariateId)
# CKD_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 118),
# .data$covariateId)
# cancer_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 119),
# .data$covariateId)
# depress_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 120),
# .data$covariateId)
# htn_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 121),
# .data$covariateId)
# hf_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 122),
# .data$covariateId)
# liver_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 123),
# .data$covariateId)
# ra_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 124),
# .data$covariateId)
# sleep_apnea_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 125),
# .data$covariateId)
# pcos_conceptId <- dplyr::pull(
# dplyr::filter(covariateData$covariateRef,
# .data$analysisId == 126),
# .data$covariateId)
# covariateData$covariates <- dplyr::mutate(
# .data = covariateData$covariates,
# covariateId = dplyr::if_else(.data$covariateId %in% T2DM_conceptId, 201826111, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% DM_conceptId, 201820111, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% obesity_conceptId, 433736112, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% angina_conceptId, 321318113, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% ami_conceptId, 312327114, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% stroke_conceptId, 443454115, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% tia_conceptId, 373503116, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% COPD_conceptId, 255841117, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% CKD_conceptId, 46271022118, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% cancer_conceptId, 443392119, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% depress_conceptId, 4282096120, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% htn_conceptId, 320128121, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% hf_conceptId, 316139122, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% liver_conceptId, 194984123, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% ra_conceptId, 80809124, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% sleep_apnea_conceptId, 313459125, .data$covariateId),
# covariateId = dplyr::if_else(.data$covariateId %in% pcos_conceptId, 40443308126, .data$covariateId))
# covariateData$covariates <- dplyr::distinct(covariateData$covariates)
return(list(covariateData = covariateData, covariateData_temp = covariateData_temp))
}
#' Transform covariateData object into FlatTable
#'
#' Si volem agafar l'últim valor podem canviar-ho
#'
#' @param covariateData A covariateDate object
#' @param covariateData_temp A Temporal covariateDate object
#'
#' @return A data.table with the covariate data
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' #Not yet
transformToFlat <- function(covariateData,
covariateData_temp){
vec_antiinflam <- c(920458, 1103374, 1115008, 1118084, 1124300, 1125315, 1146810, 1150345,
1153928, 1156378, 1177480, 1178663, 1185922, 1189754, 1518254, 19029393,
19056874, 19110711)*1000 + 411
vec_antiagregant <- c(1310149, 1325124, 19024063, 1301025, 1301065, 1308473, 1367571, 1436169,
1112807, 1302398, 1322184, 1331270, 1350310, 19042778, 19047423, 19069107,
40241186)*1000 + 411
bbdd_covar <- dplyr::collect(covariateData$covariates)
bbdd_covar <- dplyr::mutate(
.data = bbdd_covar,
variable = as.character(NA),
variable = dplyr::if_else(.data$covariateId == 1002, 'age', .data$variable),
variable = dplyr::if_else(.data$covariateId == 8507001, 'sex_male', .data$variable),
variable = dplyr::if_else(.data$covariateId == 8532001, 'sex_female', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 111, 'T2DM', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 112, 'obesity', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 113, 'angor', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 114, 'ami', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 115, 'stroke', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 116, 'tia', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 117, 'COPD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 118, 'CKD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 119, 'cancer', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 120, 'depress', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 121, 'htn', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 122, 'hf', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 123, 'liver', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 124, 'ra', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 125, 'sleep_apnea', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 126, 'pcos', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 127, 'T1DM', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 128, 'nephro', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 129, 'retino', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 130, 'neuro', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 131, 'PAD', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 132, 'DKA', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 133, 'Hypoglyc', .data$variable),
variable = dplyr::if_else(stringr::str_sub(.data$covariateId, start = -3L) == 134, 'Anaemia', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600712411, 'A10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600713411, 'A10A', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600744411, 'A10B', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601238411, 'C01', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600381411, 'C02', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601461411, 'C03', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601664411, 'C07', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601744411, 'C08', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601782411, 'C09', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601853411, 'C10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21603933411, 'M01A', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% vec_antiinflam, 'antiinflamatori', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% vec_antiagregant, 'antiagregant', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45879404, 'Never', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45884037, 'Current', .data$variable),
variable = dplyr::if_else(.data$covariateId == 45883458, 'Former', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3038553531705, 'BMI', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3036277582705, 'height', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3025315529705, 'weight', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% c(3004249323706, 4152194876706), 'SBP', .data$variable),
variable = dplyr::if_else(.data$covariateId %in% c(3012888323706, 4154790876706), 'DBP', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3010813848706, 'Leukocytes', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3001604554706, 'Monocytes', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3034639554706, 'HbA1c', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3027114840706, 'cT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840706, 'cHDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840706, 'cLDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840706, 'Tg', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3004501840706, 'Glucose', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3006923645706, 'ALT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3020460751706, 'CRP', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3001122748706, 'Ferritin', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3016723, 'Creatinine', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3024561, 'Albumin', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 8) == 40764999, 'CKDEPI', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3010587, 'GOT', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3026910, 'GGT', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 8) == 43055034, 'vitD', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3010084, 'PEPTIDCs', .data$variable),
variable = dplyr::if_else(substr(.data$covariateId, 1, 7) == 3000963, 'Hemoglobin', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201820211, 'TimeT2DM', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254212, 'TimeT1DM', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254216, 'TimeT1Rx', .data$variable),
variable = dplyr::if_else(.data$covariateId == 201254213, 'AgeT1DM', .data$variable),
# variable = dplyr::if_else(.data$covariateId == 201254217, 'AgeT1Rx', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21601853214, 'TimeC10', .data$variable),
variable = dplyr::if_else(.data$covariateId == 21600381215, 'TimeHTNRx', .data$variable))
bbdd_covar <- dplyr::group_by(.data = bbdd_covar, .data$rowId, .data$variable)
bbdd_covar <- dplyr::summarise(
.data = bbdd_covar,
covariateValue = mean(.data$covariateValue),
.groups = 'keep')
bbdd_covar <- dplyr::ungroup(x = bbdd_covar)
bbdd_covar_temp <- dplyr::collect(covariateData_temp$covariates)
bbdd_covar_temp <- dplyr::mutate(
.data = bbdd_covar_temp,
variable = as.character(NA),
variable = dplyr::if_else(.data$covariateId == 3027114840702 & .data$timeId == 1, 'cT', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840702 & .data$timeId == 1, 'cHDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840702 & .data$timeId == 1, 'cLDL', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840702 & .data$timeId == 1, 'Tg', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3027114840702 & .data$timeId == 2, 'cT_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3011884840702 & .data$timeId == 2, 'cHDL_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3028437840702 & .data$timeId == 2, 'cLDL_1y', .data$variable),
variable = dplyr::if_else(.data$covariateId == 3022192840702 & .data$timeId == 2, 'Tg_1y', .data$variable))
bbdd_covar_temp <- dplyr::filter(.data = bbdd_covar_temp, !is.na(.data$variable))
bbdd_covar_temp <- dplyr::group_by(.data = bbdd_covar_temp, .data$rowId, .data$variable)
bbdd_covar_temp <- dplyr::summarise(
.data = bbdd_covar_temp,
covariateValue = mean(.data$covariateValue),
.groups = 'keep')
bbdd_covar_temp <- dplyr::ungroup(x = bbdd_covar_temp)
bbdd_covar <- tidyr::pivot_wider(
data = rbind(bbdd_covar, bbdd_covar_temp),
id_cols = 'rowId',
names_from = 'variable',
values_from = 'covariateValue')
bbdd_covar <- dplyr::mutate(
.data = bbdd_covar,
dplyr::across(dplyr::any_of(c('sex_female', 'sex_male', 'T2DM', 'obesity', 'angor', 'tia',
'stroke', 'ami', 'Current', 'Former', 'Never',
'A10', 'A10A', 'A10B','C01', 'C02', 'C03', 'C07', 'C08', 'C09',
'C10', 'M01A',
'COPD', 'CKD', 'cancer', 'depress', 'htn', 'hf', 'liver', 'ra',
'sleep_apnea', 'pcos',
"neuro", "nephro", "retino", "PAD",
'ALT', 'Hemoglobin')),
~ tidyr::replace_na(.x, 0)))
# sex_female = dplyr::if_else(is.na(sex_female), 0, sex_female),
# sex_male = dplyr::if_else(is.na(sex_male), 0, sex_male),
# T2DM = dplyr::if_else(is.na(T2DM), 0, T2DM),
# obesity = dplyr::if_else(is.na(obesity), 0, obesity),
# angor = dplyr::if_else(is.na(angor), 0, angor),
# tia = dplyr::if_else(is.na(tia), 0, tia),
# stroke = dplyr::if_else(is.na(stroke), 0, stroke),
# ami = dplyr::if_else(is.na(ami), 0, ami),
# Current = dplyr::if_else(is.na(Current), 0, Current),
# Former = dplyr::if_else(is.na(Former), 0, Former))
# bbdd_covar <- dplyr::mutate(
# .data = bbdd_covar,
# TimeT1DM_diag = TimeT1DM,
# TimeT1DM = pmax(TimeT1DM, TimeT1Rx, na.rm = T))
return(bbdd_covar)
}
#' Auxiliar function to create Smoking status
#'
#' @param useSmoking Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createSmokingCovariateSettings <- function(useSmoking = TRUE){
covariateSettings <- list(useSmoking = useSmoking)
attr(covariateSettings, "fun") <- "getDbSmokingCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Smokins status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbSmokingCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing Smoking covariates")
if (covariateSettings$useSmoking == FALSE) {
return(NULL)
}
# Some SQL to construct the covariate:
sql <- "SELECT DISTINCT ON (obs2.person_id)
obs2.person_id AS row_id,
obs2.value_as_concept_id AS covariate_id,
1 AS covariate_value
FROM (SELECT *
FROM @cdm_database_schema.OBSERVATION obs
INNER JOIN @cohort_table cohort
ON cohort.subject_id = obs.person_id
WHERE obs.observation_concept_id = 43054909 AND
obs.observation_date <= cohort.cohort_start_date AND
cohort.cohort_definition_id IN (@cohort_definition_id)) obs2
ORDER BY obs2.person_id, obs2.observation_date desc"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(45879404, 45884037, 45883458),
covariateName = c('Never smoker', 'Current some day smoker',
'Former smoker'),
analysisId = 500,
conceptId = rep(43054909, 3))
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 500,
analysisName = "Smoking status",
domainId = "Observation",
startDay = NA,
endDay = 0,
isBinary = "Y",
missingMeansZero = "Y")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T2DM diagnosis
#'
#' @param useT2DM_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT2DM_TimeCovariateSettings <- function(useT2DM_Time = TRUE){
covariateSettings <- list(useT2DM_Time = useT2DM_Time)
attr(covariateSettings, "fun") <- "getDbuseT2DM_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T2DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT2DM_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T2DM_Time covariates")
if (covariateSettings$useT2DM_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201820, 442793, 443238))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# excluded_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 4058243, 40484648,195771, 761051))
# excluded_id <- DatabaseConnector::querySql(connection,
# sql = excluded_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201820211 AS covariate_id,
# DATEDIFF(DAY, cond2.condition_start_date, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id NOT IN (@excluded_concept_table)
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201820211 AS covariate_id,
DATEDIFF(DAY, MIN(cond.condition_start_date), cohort.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT c ON cond.condition_concept_id = c.concept_id
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE
NOT EXISTS(
SELECT 1
FROM @cdm_database_schema.CONCEPT_ANCESTOR ca2
WHERE ca2.descendant_concept_id = cond.condition_concept_id
AND ca2.ancestor_concept_id IN (201254, 435216, 4058243, 40484648, 195771, 761051)
)
AND ca.ancestor_concept_id IN (201820, 442793, 443238)
AND cohort.cohort_definition_id = @cohort_definition_id
AND cond.condition_start_date <= cohort.cohort_start_date
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201820211),
covariateName = c('Time from T2DM'),
analysisId = 211,
conceptId = 201820)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 211,
analysisName = "Time from T2DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T1DM diagnosis
#'
#' @param useT1DM_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1DM_TimeCovariateSettings <- function(useT1DM_Time = TRUE){
covariateSettings <- list(useT1DM_Time = useT1DM_Time)
attr(covariateSettings, "fun") <- "getDbuseT1DM_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T1DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1DM_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1DM_Time covariates")
if (covariateSettings$useT1DM_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 40484648, 40484649))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201254212 AS covariate_id,
# DATEDIFF(DAY, cond2.condition_start_date, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201254212 AS covariate_id,
DATEDIFF(DAY, MIN(cond.condition_start_date), cohort.cohort_start_date) AS covariate_value
FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT c ON cond.condition_concept_id = c.concept_id
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE
ca.ancestor_concept_id IN (201254, 435216, 40484648, 40484649)
AND cohort.cohort_definition_id = @cohort_definition_id
AND cond.condition_start_date <= cohort.cohort_start_date
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254212),
covariateName = c('Time from T1DM'),
analysisId = 212,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 212,
analysisName = "Time from T1DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Age for first T1DM diagnosis
#'
#' @param useT1DM_Age Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1DM_AgeCovariateSettings <- function(useT1DM_Age = TRUE){
covariateSettings <- list(useT1DM_Age = useT1DM_Age)
attr(covariateSettings, "fun") <- "getDbuseT1DM_AgeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Age for T1DM diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1DM_AgeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1DM_Age covariates")
if (covariateSettings$useT1DM_Age == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(201254, 435216, 40484648, 40484649))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Falta afegir la data de naixement a la cohort. S'ha de fer un INNER JOIN
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 201254213 AS covariate_id,
# DATEDIFF(DAY, DATEFROMPARTS(cond2.year_of_birth, cond2.month_of_birth, cond2.day_of_birth),
# cond2.condition_start_date)/365.25 AS covariate_value
# FROM (SELECT cond.person_id,
# cond.condition_start_date,
# person_table.year_of_birth,
# person_table.month_of_birth,
# person_table.day_of_birth
# FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# INNER JOIN @cdm_database_schema.PERSON person_table
# ON person_table.person_id = cohort.subject_id
# WHERE cond.condition_start_date <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.condition_concept_id != 0
# AND cond.condition_concept_id IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.condition_start_date"
sql <- "SELECT
cond.person_id AS row_id,
201254213 AS covariate_id,
DATEDIFF(DAY, DATEFROMPARTS(person_table.year_of_birth, person_table.month_of_birth, person_table.day_of_birth),
MIN(cond.condition_start_date))/365.25 AS covariate_value
FROM @cdm_database_schema.CONDITION_OCCURRENCE cond
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.condition_concept_id = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
JOIN @cdm_database_schema.PERSON person_table ON person_table.person_id = cond.person_id
WHERE
ca.ancestor_concept_id IN (201254, 435216, 40484648, 40484649)
AND cond.condition_start_date <= cohort.cohort_start_date
AND cohort.cohort_definition_id = @cohort_definition_id
GROUP BY cond.person_id, person_table.year_of_birth, person_table.month_of_birth, person_table.day_of_birth"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254213),
covariateName = c('Age from T1DM'),
analysisId = 213,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 213,
analysisName = "Age from T1DM",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form T1DM diagnosis
#'
#' @param useT1Rx_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1Rx_TimeCovariateSettings <- function(useT1Rx_Time = TRUE){
covariateSettings <- list(useT1Rx_Time = useT1Rx_Time)
attr(covariateSettings, "fun") <- "getDbuseT1Rx_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from T1Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1Rx_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1Rx_Time covariates")
if (covariateSettings$useT1Rx_Time == FALSE) {
return(NULL)
}
vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
35602717, 42899447, 46221581)
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = vec_insulin)
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# Some SQL to construct the covariate:
sql <- "SELECT
cond.person_id AS row_id,
201254216 AS covariate_id,
DATEDIFF(DAY, MIN(cond.DRUG_EXPOSURE_START_DATE), cohort.cohort_start_date) AS covariate_value
FROM @cdm_database_schema.DRUG_EXPOSURE cond
JOIN @cdm_database_schema.CONCEPT_ANCESTOR ca ON cond.DRUG_CONCEPT_ID = ca.descendant_concept_id
JOIN @cohort_table cohort ON cohort.subject_id = cond.person_id
WHERE ca.ancestor_concept_id IN (@diab_id)
AND cond.DRUG_EXPOSURE_START_DATE <= cohort.cohort_start_date
AND cohort.cohort_definition_id IN (@cohort_definition_id)
GROUP BY cond.person_id, cohort.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId,
diab_id = vec_insulin)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254216),
covariateName = c('Time from T1Rx'),
analysisId = 216,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 216,
analysisName = "Time from T1Rx",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Age for first T1Rx drug
#'
#' @param useT1Rx_Age Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createT1Rx_AgeCovariateSettings <- function(useT1Rx_Age = TRUE){
covariateSettings <- list(useT1Rx_Age = useT1Rx_Age)
attr(covariateSettings, "fun") <- "getDbuseT1Rx_AgeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Age for T1Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseT1Rx_AgeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing T1Rx_Age covariates")
if (covariateSettings$useT1Rx_Age == FALSE) {
return(NULL)
}
vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
35602717, 42899447, 46221581)
included_sql <- SqlRender::render(sql = "SELECT *
FROM @schema_cdm.CONCEPT_ANCESTOR
WHERE ancestor_concept_id IN (@diab_id)",
schema_cdm = cdmDatabaseSchema,
diab_id = vec_insulin)
included_id <- DatabaseConnector::querySql(connection,
sql = included_sql)
# Falta afegir la data de naixement a la cohort. S'ha de fer un INNER JOIN
# Some SQL to construct the covariate:
sql <- "SELECT DISTINCT ON (cond2.person_id)
cond2.person_id AS row_id,
201254217 AS covariate_id,
DATEDIFF(DAY, DATEFROMPARTS(cond2.year_of_birth, cond2.month_of_birth, cond2.day_of_birth),
cond2.DRUG_EXPOSURE_START_DATE)/365.25 AS covariate_value
FROM (SELECT cond.person_id,
cond.DRUG_EXPOSURE_START_DATE,
person_table.year_of_birth,
person_table.month_of_birth,
person_table.day_of_birth
FROM @cdm_database_schema.DRUG_EXPOSURE cond
INNER JOIN @cohort_table cohort
ON cohort.subject_id = cond.person_id
INNER JOIN @cdm_database_schema.PERSON person_table
ON person_table.person_id = cohort.subject_id
WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
AND cond.DRUG_CONCEPT_ID != 0
AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId,
included_concept_table = included_id$DESCENDANT_CONCEPT_ID)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(201254217),
covariateName = c('Age from T1Rx'),
analysisId = 217,
conceptId = 201254)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 217,
analysisName = "Age from T1Rx",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Build a Follow-up Data for cohort
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#' @param bbdd_covar A data.table create by transformToFlat function
#'
#' @return A data.frame object
#' @export
#'
#' @importFrom rlang .data
#'
#' @examples
#' # Not yet
buildFollowUp <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId = 1,
bbdd_covar){
obs_per_sql <- "SELECT * FROM @omopSc.OBSERVATION_PERIOD
WHERE person_id IN (SELECT subject_id FROM @resultSc.@cohortTable)"
obs_per <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = obs_per_sql,
omopSc = cdm_schema,
resultSc = results_sc,
cohortTable = cohortTable))
death_sql <- "SELECT * FROM @omopSc.DEATH
WHERE person_id IN (SELECT subject_id FROM @resultSc.@cohortTable)"
death <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = death_sql,
omopSc = cdm_schema,
resultSc = results_sc,
cohortTable = cohortTable))
cohort <- DatabaseConnector::querySql(connection = cdm_bbdd,
sql = SqlRender::render(sql = "SELECT * FROM @resultSc.@cohortTable",
resultSc = results_sc,
cohortTable = cohortTable))
cohort_event <- cohort[cohort$COHORT_DEFINITION_ID %in% c(acohortId, 3:18),]
cohort_event$event <- as.character(NA)
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == acohortId] <- 'dintro'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 3] <- 'AMI'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 4] <- 'Angor'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 5] <- 'StrokeI'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 6] <- 'TIA'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 7] <- 'Nephropathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 8] <- 'Retinopathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 9] <- 'Neuropathy'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 10] <- 'PAD'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 11] <- 'Angor_unstable'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 12] <- 'AMI_WP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 13] <- 'stroke_WP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 14] <- 'neuroWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 15] <- 'nephroWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 16] <- 'retinoWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 17] <- 'footWP4'
cohort_event$event[cohort_event$COHORT_DEFINITION_ID == 18] <- 'DKAWP4'
cohort_event_w <- tidyr::pivot_wider(data = cohort_event,
id_cols = "SUBJECT_ID",
names_from = 'event',
names_prefix = 'ep_',
values_from = "COHORT_START_DATE")
names(cohort_event_w)[names(cohort_event_w) == "ep_dintro"] <- 'dintro'
cohort_event_w <- merge(cohort_event_w,
obs_per[, c("PERSON_ID", "OBSERVATION_PERIOD_END_DATE")],
by.x = 'SUBJECT_ID',
by.y = 'PERSON_ID',
all.x = TRUE)
bbdd_covar <- merge(bbdd_covar,
cohort_event_w,
by.x = 'rowId',
by.y = "SUBJECT_ID",
all.x = TRUE)
bbdd_covar <- merge(bbdd_covar,
death,
by.x = 'rowId',
by.y = "PERSON_ID",
all.x = TRUE)
bbdd_covar$i.ep_AMI <- 0
bbdd_covar$i.ep_AMI[bbdd_covar$dintro < bbdd_covar$ep_AMI &
bbdd_covar$ep_AMI <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_AMI <- as.numeric(pmin(bbdd_covar$ep_AMI, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Angor <- 0
bbdd_covar$i.ep_Angor[bbdd_covar$dintro < bbdd_covar$ep_Angor &
bbdd_covar$ep_Angor <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Angor <- as.numeric(pmin(bbdd_covar$ep_Angor, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_StrokeI <- 0
bbdd_covar$i.ep_StrokeI[bbdd_covar$dintro < bbdd_covar$ep_StrokeI &
bbdd_covar$ep_StrokeI <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_StrokeI <- as.numeric(pmin(bbdd_covar$ep_StrokeI, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_TIA <- 0
bbdd_covar$i.ep_TIA[bbdd_covar$dintro < bbdd_covar$ep_TIA &
bbdd_covar$ep_TIA <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_TIA <- as.numeric(pmin(bbdd_covar$ep_TIA, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Nephropathy <- 0
bbdd_covar$i.ep_Nephropathy[bbdd_covar$dintro < bbdd_covar$ep_Nephropathy &
bbdd_covar$ep_Nephropathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Nephropathy <- as.numeric(pmin(bbdd_covar$ep_Nephropathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Retinopathy <- 0
bbdd_covar$i.ep_Retinopathy[bbdd_covar$dintro < bbdd_covar$ep_Retinopathy &
bbdd_covar$ep_Retinopathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Retinopathy <- as.numeric(pmin(bbdd_covar$ep_Retinopathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Neuropathy <- 0
bbdd_covar$i.ep_Neuropathy[bbdd_covar$dintro < bbdd_covar$ep_Neuropathy &
bbdd_covar$ep_Neuropathy <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Neuropathy <- as.numeric(pmin(bbdd_covar$ep_Neuropathy, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_PAD <- 0
bbdd_covar$i.ep_PAD[bbdd_covar$dintro < bbdd_covar$ep_PAD &
bbdd_covar$ep_PAD <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_PAD <- as.numeric(pmin(bbdd_covar$ep_PAD, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_Angor_unstable <- 0
bbdd_covar$i.ep_Angor_unstable[bbdd_covar$dintro < bbdd_covar$ep_Angor_unstable &
bbdd_covar$ep_Angor_unstable <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_Angor_unstable <- as.numeric(pmin(bbdd_covar$ep_Angor_unstable, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_AMI_WP4 <- 0
bbdd_covar$i.ep_AMI_WP4[bbdd_covar$dintro < bbdd_covar$ep_AMI_WP4 &
bbdd_covar$ep_AMI_WP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_AMI_WP4 <- as.numeric(pmin(bbdd_covar$ep_AMI_WP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_stroke_WP4 <- 0
bbdd_covar$i.ep_stroke_WP4[bbdd_covar$dintro < bbdd_covar$ep_stroke_WP4 &
bbdd_covar$ep_stroke_WP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_stroke_WP4 <- as.numeric(pmin(bbdd_covar$ep_stroke_WP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_neuroWP4 <- 0
bbdd_covar$i.ep_neuroWP4[bbdd_covar$dintro < bbdd_covar$ep_neuroWP4 &
bbdd_covar$ep_neuroWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_neuroWP4 <- as.numeric(pmin(bbdd_covar$ep_neuroWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_nephroWP4 <- 0
bbdd_covar$i.ep_nephroWP4[bbdd_covar$dintro < bbdd_covar$ep_nephroWP4 &
bbdd_covar$ep_nephroWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_nephroWP4 <- as.numeric(pmin(bbdd_covar$ep_nephroWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_retinoWP4 <- 0
bbdd_covar$i.ep_retinoWP4[bbdd_covar$dintro < bbdd_covar$ep_retinoWP4 &
bbdd_covar$ep_retinoWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_retinoWP4 <- as.numeric(pmin(bbdd_covar$ep_retinoWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_footWP4 <- 0
bbdd_covar$i.ep_footWP4[bbdd_covar$dintro < bbdd_covar$ep_footWP4 &
bbdd_covar$ep_footWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_footWP4 <- as.numeric(pmin(bbdd_covar$ep_footWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
bbdd_covar$i.ep_DKAWP4 <- 0
bbdd_covar$i.ep_DKAWP4[bbdd_covar$dintro < bbdd_covar$ep_DKAWP4 &
bbdd_covar$ep_DKAWP4 <= bbdd_covar$OBSERVATION_PERIOD_END_DATE] <- 1
bbdd_covar$t.ep_DKAWP4 <- as.numeric(pmin(bbdd_covar$ep_DKAWP4, bbdd_covar$OBSERVATION_PERIOD_END_DATE, na.rm = T) - bbdd_covar$dintro)/365.25
return(bbdd_covar)
}
#' Auxiliar function to create Time form C10 diagnosis
#'
#' @param useC10_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createC10_TimeCovariateSettings <- function(useC10_Time = TRUE){
covariateSettings <- list(useC10_Time = useC10_Time)
attr(covariateSettings, "fun") <- "getDbuseC10_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from C10 diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseC10_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing C10_Time covariates")
if (covariateSettings$useC10_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(21601853))
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 21601853214 AS covariate_id,
# DATEDIFF(DAY, cond2.DRUG_EXPOSURE_START_DATE, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.DRUG_EXPOSURE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.DRUG_CONCEPT_ID != 0
# AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- "SELECT
de.person_id AS row_id,
21601853214 AS covariate_id,
DATEDIFF(day, MIN(de.drug_era_start_date), r.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.drug_era de
JOIN @cdm_database_schema.concept c ON de.drug_concept_id = c.concept_id
JOIN @cdm_database_schema.concept_ancestor ca ON c.concept_id = ca.descendant_concept_id
JOIN @cohort_table r ON de.person_id = r.subject_id
WHERE
ca.ancestor_concept_id = 21601853
AND r.cohort_definition_id = @cohort_definition_id
AND de.drug_era_start_date <= r.cohort_start_date
GROUP BY
de.person_id, r.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(21601853214),
covariateName = c('Time from C10'),
analysisId = 214,
conceptId = 21601853)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 214,
analysisName = "Time from C10",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Auxiliar function to create Time form HTN medication diagnosis
#'
#' @param useHTNRx_Time Logical valor
#'
#' @return covariateSettings object
#' @export
#'
#' @examples
#' #Not yet
createHTNRx_TimeCovariateSettings <- function(useHTNRx_Time = TRUE){
covariateSettings <- list(useHTNRx_Time = useHTNRx_Time)
attr(covariateSettings, "fun") <- "getDbuseHTNRx_TimeCovariateData"
class(covariateSettings) <- "covariateSettings"
return(covariateSettings)
}
#' Auxiliar function to create Time from HTN Rx diagnosis status SQL implementation
#'
#' @param connection A connection for a OMOP database via DatabaseConnector
#' @param oracleTempSchema Only for Oracle Database
#' @param cdmDatabaseSchema A name for OMOP schema
#' @param cohortTable A name of the result cohort
#' @param cohortId A Cohort number
#' @param cdmVersion CDM version
#' @param rowIdField Column with the subject identification
#' @param covariateSettings covariateSettings object generetad via FeatureExtraction
#' @param aggregated Logical value
#'
#' @return Function to use with FeatureExtraction to build covariateData
#' @export
#'
#' @examples
#' #Not yet
getDbuseHTNRx_TimeCovariateData <- function(connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cohortTable = "#cohort_person",
cohortId = -1,
cdmVersion = "5",
rowIdField = "subject_id",
covariateSettings,
aggregated = FALSE){
writeLines("Constructing HTNRx_Time covariates")
if (covariateSettings$useHTNRx_Time == FALSE) {
return(NULL)
}
# included_sql <- SqlRender::render(sql = "SELECT *
# FROM @schema_cdm.CONCEPT_ANCESTOR
# WHERE ancestor_concept_id IN (@diab_id)",
# schema_cdm = cdmDatabaseSchema,
# diab_id = c(21600381, #C02
# 21601461, #C03
# 21601664, #C07
# 21601744, #C08
# 21601782)) #C09
# included_id <- DatabaseConnector::querySql(connection,
# sql = included_sql)
# # Some SQL to construct the covariate:
# sql <- "SELECT DISTINCT ON (cond2.person_id)
# cond2.person_id AS row_id,
# 21600381215 AS covariate_id,
# DATEDIFF(DAY, cond2.DRUG_EXPOSURE_START_DATE, cond2.cohort_start_date) AS covariate_value
# FROM (SELECT *
# FROM @cdm_database_schema.DRUG_EXPOSURE cond
# INNER JOIN @cohort_table cohort
# ON cohort.subject_id = cond.person_id
# WHERE cond.DRUG_EXPOSURE_START_DATE <= DATEADD(DAY, 0, cohort.cohort_start_date)
# AND cond.DRUG_CONCEPT_ID != 0
# AND cond.DRUG_CONCEPT_ID IN (@included_concept_table)
# AND cohort.cohort_definition_id IN (@cohort_definition_id)) cond2
# ORDER BY cond2.person_id, cond2.DRUG_EXPOSURE_START_DATE"
sql <- "SELECT
de.person_id AS row_id,
21600381215 AS covariate_id,
DATEDIFF(day, MIN(de.drug_era_start_date), r.cohort_start_date) AS covariate_value
FROM
@cdm_database_schema.drug_era de
JOIN @cdm_database_schema.concept c ON de.drug_concept_id = c.concept_id
JOIN @cdm_database_schema.concept_ancestor ca ON c.concept_id = ca.descendant_concept_id
JOIN @cohort_table r ON de.person_id = r.subject_id
WHERE
ca.ancestor_concept_id IN (21600381, 21601461, 21601664, 21601744, 21601782)
AND r.cohort_definition_id = @cohort_definition_id
AND de.drug_era_start_date <= r.cohort_start_date
GROUP BY
de.person_id, r.cohort_start_date"
sql <- SqlRender::render(sql,
cdm_database_schema = cdmDatabaseSchema,
cohort_table = cohortTable, # ha de ser results_sc.cohortTable
cohort_definition_id = cohortId)
sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms"))
# Retrieve the covariate:
covariates <- DatabaseConnector::querySql(connection, sql, snakeCaseToCamelCase = TRUE)
# Construct covariate reference:
covariateRef <- data.frame(covariateId = c(21600381215),
covariateName = c('Time from HTNRx'),
analysisId = 215,
conceptId = 21600381)
# Construct analysis reference:
analysisRef <- data.frame(analysisId = 215,
analysisName = "Time from HTNRX",
domainId = "Condition",
startDay = NA,
endDay = 0,
isBinary = "N",
missingMeansZero = "N")
# Construct analysis reference:
metaData <- list(sql = sql, call = match.call())
result <- Andromeda::andromeda(covariates = covariates,
covariateRef = covariateRef,
analysisRef = analysisRef)
attr(result, "metaData") <- metaData
class(result) <- "CovariateData"
return(result)
}
#' Funcion para pasar del servidor a una tabla plana
#' Necesita totes les coses del servidor més quina cohort volem contruir.
#'
#' @param cdm_bbdd A connection for a OMOP database via DatabaseConnector
#' @param cdm_schema A name for OMOP schema
#' @param results_sc A name for result schema
#' @param cohortTable A name of the result cohort
#' @param acohortId A Cohort number
#'
#' @return List with three objects: descriptive for numeric and category and database ready to analysis.
#' @export
#'
#' @examples
#' #Not yet
FunCovar <- function(cdm_bbdd,
cdm_schema,
results_sc,
cohortTable,
acohortId){
covariateData_aux <- buildData(cdm_bbdd = cdm_bbdd,
cdm_schema = cdm_schema,
results_sc = results_sc,
cohortTable = cohortTable,
acohortId = acohortId)
covariateData_aux_temp <- covariateData_aux$covariateData_temp
covariateData_aux <- covariateData_aux$covariateData
# #Calculem el MPR pel T1DM i els eliminem si MPR inferior a 90%
# if (acohortId == 2){
# sql_cohort <- "SELECT * FROM sophia_test.cohorttable WHERE cohort_definition_id = 2"
# cohort <- DatabaseConnector::querySql(connection = cdm_bbdd,
# sql = sql_cohort)
# cohort$time_obs <- with(cohort, as.numeric(COHORT_END_DATE - COHORT_START_DATE + 1))
# vec_insulin <- c(1596977, 19058398, 19078552, 19078559, 19095211, 19095212, 19112791, 19133793,
# 19135264, 21076306, 21086042, 35410536, 35412958, 40051377, 40052768, 42479783,
# 42481504, 42481541, 42899447, 42902356, 42902587, 42902742, 42902821, 42902945,
# 42903059, 44058584, 46233969, 46234047, 46234234, 46234237, 1502905, 1513843,
# 1513876, 1516976, 1531601, 1544838, 1550023, 1562586, 1567198, 1586346, 1586369,
# 1588986, 1590165, 1596977, 19013926, 19013951, 19090180, 19090187, 19090204,
# 19090221, 19090226, 19090229, 19090244, 19090247, 19090249, 19091621, 35198096,
# 35602717, 42899447, 46221581)
# sql_insulin <- "SELECT * FROM omop21t2_test.DRUG_ERA WHERE person_id IN (@id) AND DRUG_CONCEPT_ID IN (@insulin)"
# insulin <- DatabaseConnector::querySql(connection = cdm_bbdd,
# sql = SqlRender::render(sql_insulin,
# id = cohort$SUBJECT_ID,
# insulin = vec_insulin))
# insulin_treat <- dplyr::arrange(.data = insulin,
# PERSON_ID, DRUG_ERA_START_DATE)
# n_prev <- pull(count(insulin_treat), n)
# cond <- TRUE
# while(cond){
# insulin_treat <- group_by(insulin_treat, PERSON_ID)
# insulin_treat <- mutate(insulin_treat,
# canvi = lag(DRUG_ERA_END_DATE) < DRUG_ERA_START_DATE,
# canvi = dplyr::if_else(is.na(canvi), FALSE, canvi),
# treat = cumsum(canvi))
# insulin_treat <- group_by(insulin_treat, PERSON_ID, treat)
# insulin_treat <- summarise(insulin_treat,
# DRUG_ERA_START_DATE = min(DRUG_ERA_START_DATE),
# DRUG_ERA_END_DATE = max(DRUG_ERA_END_DATE),
# .groups = 'drop')
# n_act <- pull(count(insulin_treat), n)
# # cat('Prev: ', n_prev, ' Act: ', n_act, '\n')
# cond <- n_prev > n_act
# n_prev <- n_act
# }
# insulin_treat <- mutate(insulin_treat,
# time = as.numeric(DRUG_ERA_END_DATE - DRUG_ERA_START_DATE + 1))
# cohort <- dplyr::left_join(cohort,
# insulin_treat,
# by = c('SUBJECT_ID' = 'PERSON_ID'))
# cohort$MPR <- with(cohort, time/time_obs)
# cohort <- dplyr::filter(cohort, 0.75 < MPR)
# covariateData_aux_temp <- dplyr::filter(covariateData_aux_temp,
# rowId %in% cohort$SUBJECT_ID)
# covariateData_aux <- dplyr::filter(covariateData_aux,
# rowId %in% cohort$SUBJECT_ID)
# }
covariateData2_aux <- FeatureExtraction::aggregateCovariates(covariateData_aux)
sel_med_conceptId <- c(21600712, #DRUGS USED IN DIABETES
#Aquestes insulines no les troba
21076306, 44058584, 21086042, 21036596,
21601238, #C01
21600381, #C02
21601461, #C03
21601664, #C07
21601744, #C08
21601782, #C09
21601853, #C10
21603933 #M01A
)
cov_cate_resum_aux <- dplyr::filter(
.data = covariateData2_aux$covariateRef,
.data$analysisId %in% c(411, 413) & .data$conceptId %in% sel_med_conceptId |
!(.data$analysisId %in% c(411, 413)))
cov_cate_resum_aux <- dplyr::inner_join(
x = cov_cate_resum_aux,
y = covariateData2_aux$covariates)
cov_cate_resum_aux <- dplyr::mutate(
.data = cov_cate_resum_aux,
covariateId = as.character(floor(.data$covariateId)),
analysisId = as.integer(.data$analysisId),
conceptId = as.integer(.data$conceptId),
sumValue = as.integer(.data$sumValue),
averageValue = .data$averageValue*100)
cov_cate_resum_aux <-dplyr::collect(x = cov_cate_resum_aux)
cov_num_resum_aux <- dplyr::inner_join(
x = covariateData2_aux$covariateRef,
y = covariateData2_aux$covariatesContinuous)
cov_num_resum_aux <- dplyr::mutate(
.data = cov_num_resum_aux,
covariateId = as.character(floor(.data$covariateId)),
analysisId = as.integer(.data$analysisId),
conceptId = as.integer(.data$conceptId))
cov_num_resum_aux <- dplyr::select(
.data = cov_num_resum_aux,
-.data$covariateId, -.data$analysisId, -.data$conceptId)
cov_num_resum_aux <- dplyr::collect(x = cov_num_resum_aux)
bbdd_covar_aux <- transformToFlat(covariateData_aux,
covariateData_aux_temp)
bbdd_covar_aux <- buildFollowUp(cdm_bbdd = cdm_bbdd,
cdm_schema = cdm_schema,
results_sc = results_sc,
cohortTable = cohortTable,
acohortId = acohortId,
bbdd_covar = bbdd_covar_aux)
return(list(cov_cate_resum = cov_cate_resum_aux,
cov_num_resum = cov_num_resum_aux,
bbdd_covar = bbdd_covar_aux))
}
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