R/calculate_prevent_risk_full.R
In JMLuther/tabletools: Helpful Tools to Make html Tables
Defines functions
calculate_prevent_risk_full
Documented in
calculate_prevent_risk_full
#'Calculate AHA PREVENT Risk (full model)
#'
#'Uses the PREVENT Full model incorporating urine albumin, A1C, and SDI if
#'available into risk estimate: PREVENT (AHA Predicting Risk of CVD Events). The
#'PREVENT equations enable 10 and 30-year risk estimates for total CVD
#'(composite of atherosclerotic CVD and heart failure), ASCVD (atherosclerotic
#'CVD), Heart failure (HF), Coronary Artery Disease (CAD), and Stroke. Details
#' provided in \href{https://pubmed.ncbi.nlm.nih.gov/37947085/}{Khan et al.,
#' Circulation. 2024}. This model incorporates urine albumin, A1C and socioeconomic risk (by Zip code) if available. SDI lookup by zipcode is slightly off in SDI estimate- enter the SDI decile (1-10) for most accurate results.
#'
#'
#'@param risk Desired Risk Calculation ("cvd", "ascvd", "hf", "cad", "stroke")
#'@param gender Gender, (Female/Male)
#'@param age Age in years
#'@param Tc Total Cholesterol (mg/dL). converted to mmol/L internally
#'@param HDL HDL Cholesterol (mg/dL). converted to mmol/L internally
#'@param SBP Systolic Blood Pressure, mmHg
#'@param eGFR estimated GFR (ml/min/1.73m2)
#'@param BMI Body Mass Index (kg/m2), used only in HF estimate
#'@param UACR Urine albumin/creatinine ratio (mg/g creatinine)
#'@param HbA1c Hemoglobin A1C (percent)
#'@param SDI Social Deprivation Index (SDI), decile
#'@param zipcode Zipcode to use for SDI lookup, if SDI not provided
#'@param year Year to use for SDI lookup, if SDI not provided
#'@param current_smoker current_smoker, T/F
#'@param using_antihypertensive_medication HTN medication use, T/F
#'@param using_statin Statin use, T/F
#'@param diabetes Diabetes, T/F
#'@param chol_units default = mg/dL; cholesterol units not in mg/dL then define
#' here for conversion
#'
#'@return 10- and 30-year Risk (percent) in Dataframe format
#'@export
#'
#' @examples
#'
#'
#' calculate_prevent_risk_full(risk="cvd",gender="Female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="ascvd",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="hf",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90, BMI=35,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="cad",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' # men
#' calculate_prevent_risk_full(risk="cvd",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="ascvd",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="hf",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90, BMI=35,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="cad",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' # Lookup SDI by zipcode; defaults to year 2019
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = NA, zipcode=37220,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 1,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' library(tidyverse)
#' df <- crossing(
#' gender=c("female", "male"),
#' age=50, Tc=200, HDL=45, eGFR=90, BMI=35,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE,
#' HbA1c = 7.5, SDI = 10,
#' SBP= seq(130,180,by=10),
#' UACR=seq(0,300, by=50)
#' )
#' # seq(0,3000, by=50)
#' df_risk <-
#' df |>
#' rowwise() |>
#' mutate(calculate_prevent_risk_full(risk="cvd", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="ascvd", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="hf", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="cad", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="stroke", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes)
#' )
#' df_risk
calculate_prevent_risk_full <- function(risk, gender,
age, Tc, HDL, SBP, eGFR,
BMI=NA, UACR=NA, HbA1c=NA,
SDI=NA, zipcode=NA, year=2019,
current_smoker=FALSE,
using_antihypertensive_medication=FALSE,
using_statin=FALSE,
diabetes=FALSE,
chol_units = "mg/dL") {
BMI = ifelse(is.na(BMI), 0, BMI) # used only in HF calculations
SDI =ifelse(!is.na(SDI), SDI,
ifelse(!is.na(zipcode), sdi_decile({{zipcode}}, {{year}}), NA))
SDI_missing = ifelse(is.na(SDI) & is.na(zipcode), TRUE, FALSE)
SDI4_6 = SDI>=4 & SDI<7
SDI7_10 = SDI>=7
UACR_missing = ifelse(is.na(UACR), TRUE, FALSE)
HbA1c_missing = ifelse(is.na(HbA1c), TRUE, FALSE)
Tc = ifelse(chol_units == "mg/dL", Tc*0.02586, Tc) # convert to mmol/L
HDL = ifelse(chol_units == "mg/dL", HDL*0.02586, HDL) # convert to mmol/L
# validate risk request
risk = tolower(risk)
risk_query <-
ifelse(risk%in% c("cvd", "cardiovascular disease", "cardiovascular"), "cvd",
ifelse(risk%in% c("ascvd", "atherosclerotic cvd"), "ascvd",
ifelse(risk%in% c("hf", "chf", "heart failure", "ahf"), "hf",
ifelse(risk%in% c("cad", "coronary artery disease", "mi", "myocardial infarction", "heart attack"), "cad",
ifelse(risk%in% c("stroke", "cva"), "stroke", NA)))))
# validate gender
gender_query= tolower(handle_sex(gender))
# 10 YEAR BASE MODEL
cf10 <- cfs_full10yr[cfs_full10yr$risk=={{risk_query}} & cfs_full10yr$gender=={{gender_query}}, ]
# construct the model
log_odds10 =
cf10$constant +
cf10$age*(age-55) /10 + # AGE
cf10$nonHDL*(Tc-HDL-3.5) + # NON-HDL CHOL
cf10$HDL*(HDL-1.3) /0.3 +
cf10$sbp_under110*(min(SBP, 110)-110) /20 +
cf10$sbp_over110*(max(SBP, 110)-130) /20 +
cf10$diabetes*(diabetes) +
cf10$current_smoker*(current_smoker) +
# BMI used in HF calc only
cf10$bmi_under30*(min(BMI, 30)-25) / 5 +
cf10$bmi_over30*(max(BMI, 30)-30) / 5 +
cf10$eGFR_under60*(min(eGFR, 60)-60) / -15 +
cf10$eGFR_over60*(max(eGFR, 60)-90) / -15 +
cf10$using_antihypertensive_medication*(using_antihypertensive_medication)+
cf10$using_statin*(using_statin)+
cf10$sbp_treated_over110*(using_antihypertensive_medication)*(max(SBP, 110)-130) /20 +
cf10$treated_nonHDL*(using_statin)*(Tc-HDL-3.5) +
cf10$age_nonHDL_int*(age-55) /10*(Tc-HDL-3.5) +
cf10$age_HDL_int*(age-55) /10*(HDL-1.3) /0.3 +
cf10$age_sbp_int*(age-55) /10*(max(SBP, 110)-130)/20 +
cf10$age_diabetes_int*(age-55) /10*(diabetes) +
cf10$age_smoking_int*(age-55) /10*(current_smoker) +
cf10$age_bmi_int*(age-55)/10*(max(BMI, 30)-30)/5 +
cf10$age_egfr_int*(age-55) /10*(min(eGFR, 60)-60) / -15 +
ifelse(SDI_missing, cf10$sdi_missing,
ifelse(SDI4_6, cf10$sdi_4_6,
ifelse(SDI7_10, cf10$sdi__10, 0))) +
ifelse(UACR_missing, cf10$acr_missing, cf10$ln_acr*log(UACR)) +
ifelse(HbA1c_missing, cf10$a1c_missing,
ifelse(diabetes, cf10$a1c_dm*(HbA1c-5.3),
ifelse(!diabetes, cf10$a1c_nondm*(HbA1c-5.3))))
# # 30 YEAR BASE MODEL
cf30 <- cfs_full30yr[cfs_full30yr$risk=={{risk_query}} & cfs_full30yr$gender=={{gender_query}}, ]
# construct the model
log_odds30 =
cf30$constant +
cf30$age*(age-55) /10 + # AGE
cf30$age_sq*((age-55) /10)^2 + # AGE
cf30$nonHDL*(Tc-HDL-3.5) + # NON-HDL CHOL
cf30$HDL*(HDL-1.3) /0.3 +
cf30$sbp_under110*(min(SBP, 110)-110) /20 +
cf30$sbp_over110*(max(SBP, 110)-130) /20 +
cf30$diabetes*(diabetes) +
cf30$current_smoker*(current_smoker) +
# BMI used in HF calc only
cf30$bmi_under30*(min(BMI, 30)-25) / 5 +
cf30$bmi_over30*(max(BMI, 30)-30) / 5 +
cf30$eGFR_under60*(min(eGFR, 60)-60) / -15 +
cf30$eGFR_over60*(max(eGFR, 60)-90) / -15 +
cf30$using_antihypertensive_medication*(using_antihypertensive_medication)+
cf30$using_statin*(using_statin)+
cf30$sbp_treated_over110*(using_antihypertensive_medication)*(max(SBP, 110)-130) /20 +
cf30$treated_nonHDL*(using_statin)*(Tc-HDL-3.5) +
cf30$age_nonHDL_int*(age-55) /10*(Tc-HDL-3.5) +
cf30$age_HDL_int*(age-55) /10*(HDL-1.3) /0.3 +
cf30$age_sbp_int*(age-55) /10*(max(SBP, 110)-130)/20 +
cf30$age_diabetes_int*(age-55) /10*(diabetes) +
cf30$age_smoking_int*(age-55) /10*(current_smoker) +
cf30$age_bmi_int*(age-55)/10*(max(BMI, 30)-30)/5 +
cf30$age_egfr_int*(age-55) /10*(min(eGFR, 60)-60) / -15 +
ifelse(SDI_missing, cf30$sdi_missing,
ifelse(SDI4_6, cf30$sdi_4_6,
ifelse(SDI7_10, cf30$sdi__10, 0))) +
ifelse(UACR_missing, cf30$acr_missing, cf30$ln_acr*log(UACR)) +
ifelse(HbA1c_missing, cf30$a1c_missing,
ifelse(diabetes, cf30$a1c_dm*(HbA1c-5.3),
ifelse(!diabetes, cf30$a1c_nondm*(HbA1c-5.3))))
Risk10 = exp(log_odds10) / (1 + exp(log_odds10))
Risk30 = exp(log_odds30) / (1 + exp(log_odds30))
res = data.frame(Risk10*100,
Risk30*100)
names(res) <- paste0(risk, c("_10yr", "_30yr"))
return(res)
}
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Defines functions calculate_prevent_risk_full
Documented in calculate_prevent_risk_full
#'Calculate AHA PREVENT Risk (full model)
#'
#'Uses the PREVENT Full model incorporating urine albumin, A1C, and SDI if
#'available into risk estimate: PREVENT (AHA Predicting Risk of CVD Events). The
#'PREVENT equations enable 10 and 30-year risk estimates for total CVD
#'(composite of atherosclerotic CVD and heart failure), ASCVD (atherosclerotic
#'CVD), Heart failure (HF), Coronary Artery Disease (CAD), and Stroke. Details
#' provided in \href{https://pubmed.ncbi.nlm.nih.gov/37947085/}{Khan et al.,
#' Circulation. 2024}. This model incorporates urine albumin, A1C and socioeconomic risk (by Zip code) if available. SDI lookup by zipcode is slightly off in SDI estimate- enter the SDI decile (1-10) for most accurate results.
#'
#'
#'@param risk Desired Risk Calculation ("cvd", "ascvd", "hf", "cad", "stroke")
#'@param gender Gender, (Female/Male)
#'@param age Age in years
#'@param Tc Total Cholesterol (mg/dL). converted to mmol/L internally
#'@param HDL HDL Cholesterol (mg/dL). converted to mmol/L internally
#'@param SBP Systolic Blood Pressure, mmHg
#'@param eGFR estimated GFR (ml/min/1.73m2)
#'@param BMI Body Mass Index (kg/m2), used only in HF estimate
#'@param UACR Urine albumin/creatinine ratio (mg/g creatinine)
#'@param HbA1c Hemoglobin A1C (percent)
#'@param SDI Social Deprivation Index (SDI), decile
#'@param zipcode Zipcode to use for SDI lookup, if SDI not provided
#'@param year Year to use for SDI lookup, if SDI not provided
#'@param current_smoker current_smoker, T/F
#'@param using_antihypertensive_medication HTN medication use, T/F
#'@param using_statin Statin use, T/F
#'@param diabetes Diabetes, T/F
#'@param chol_units default = mg/dL; cholesterol units not in mg/dL then define
#' here for conversion
#'
#'@return 10- and 30-year Risk (percent) in Dataframe format
#'@export
#'
#' @examples
#'
#'
#' calculate_prevent_risk_full(risk="cvd",gender="Female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="ascvd",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="hf",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90, BMI=35,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="cad",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="female", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' # men
#' calculate_prevent_risk_full(risk="cvd",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="ascvd",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="hf",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90, BMI=35,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="cad",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 8,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' # Lookup SDI by zipcode; defaults to year 2019
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = NA, zipcode=37220,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#' calculate_prevent_risk_full(risk="stroke",gender="male", age=50, Tc=200, HDL=45, SBP=160, eGFR=90,
#' UACR = 40, HbA1c = 7.5, SDI = 1,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE)
#'
#' library(tidyverse)
#' df <- crossing(
#' gender=c("female", "male"),
#' age=50, Tc=200, HDL=45, eGFR=90, BMI=35,
#' using_antihypertensive_medication = TRUE, diabetes = TRUE,
#' HbA1c = 7.5, SDI = 10,
#' SBP= seq(130,180,by=10),
#' UACR=seq(0,300, by=50)
#' )
#' # seq(0,3000, by=50)
#' df_risk <-
#' df |>
#' rowwise() |>
#' mutate(calculate_prevent_risk_full(risk="cvd", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="ascvd", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="hf", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="cad", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes),
#' calculate_prevent_risk_full(risk="stroke", gender, age, Tc, HDL, SBP, eGFR, BMI,
#' UACR, HbA1c, SDI,
#' using_antihypertensive_medication = using_antihypertensive_medication,
#' diabetes=diabetes)
#' )
#' df_risk
calculate_prevent_risk_full <- function(risk, gender,
age, Tc, HDL, SBP, eGFR,
BMI=NA, UACR=NA, HbA1c=NA,
SDI=NA, zipcode=NA, year=2019,
current_smoker=FALSE,
using_antihypertensive_medication=FALSE,
using_statin=FALSE,
diabetes=FALSE,
chol_units = "mg/dL") {
BMI = ifelse(is.na(BMI), 0, BMI) # used only in HF calculations
SDI =ifelse(!is.na(SDI), SDI,
ifelse(!is.na(zipcode), sdi_decile({{zipcode}}, {{year}}), NA))
SDI_missing = ifelse(is.na(SDI) & is.na(zipcode), TRUE, FALSE)
SDI4_6 = SDI>=4 & SDI<7
SDI7_10 = SDI>=7
UACR_missing = ifelse(is.na(UACR), TRUE, FALSE)
HbA1c_missing = ifelse(is.na(HbA1c), TRUE, FALSE)
Tc = ifelse(chol_units == "mg/dL", Tc*0.02586, Tc) # convert to mmol/L
HDL = ifelse(chol_units == "mg/dL", HDL*0.02586, HDL) # convert to mmol/L
# validate risk request
risk = tolower(risk)
risk_query <-
ifelse(risk%in% c("cvd", "cardiovascular disease", "cardiovascular"), "cvd",
ifelse(risk%in% c("ascvd", "atherosclerotic cvd"), "ascvd",
ifelse(risk%in% c("hf", "chf", "heart failure", "ahf"), "hf",
ifelse(risk%in% c("cad", "coronary artery disease", "mi", "myocardial infarction", "heart attack"), "cad",
ifelse(risk%in% c("stroke", "cva"), "stroke", NA)))))
# validate gender
gender_query= tolower(handle_sex(gender))
# 10 YEAR BASE MODEL
cf10 <- cfs_full10yr[cfs_full10yr$risk=={{risk_query}} & cfs_full10yr$gender=={{gender_query}}, ]
# construct the model
log_odds10 =
cf10$constant +
cf10$age*(age-55) /10 + # AGE
cf10$nonHDL*(Tc-HDL-3.5) + # NON-HDL CHOL
cf10$HDL*(HDL-1.3) /0.3 +
cf10$sbp_under110*(min(SBP, 110)-110) /20 +
cf10$sbp_over110*(max(SBP, 110)-130) /20 +
cf10$diabetes*(diabetes) +
cf10$current_smoker*(current_smoker) +
# BMI used in HF calc only
cf10$bmi_under30*(min(BMI, 30)-25) / 5 +
cf10$bmi_over30*(max(BMI, 30)-30) / 5 +
cf10$eGFR_under60*(min(eGFR, 60)-60) / -15 +
cf10$eGFR_over60*(max(eGFR, 60)-90) / -15 +
cf10$using_antihypertensive_medication*(using_antihypertensive_medication)+
cf10$using_statin*(using_statin)+
cf10$sbp_treated_over110*(using_antihypertensive_medication)*(max(SBP, 110)-130) /20 +
cf10$treated_nonHDL*(using_statin)*(Tc-HDL-3.5) +
cf10$age_nonHDL_int*(age-55) /10*(Tc-HDL-3.5) +
cf10$age_HDL_int*(age-55) /10*(HDL-1.3) /0.3 +
cf10$age_sbp_int*(age-55) /10*(max(SBP, 110)-130)/20 +
cf10$age_diabetes_int*(age-55) /10*(diabetes) +
cf10$age_smoking_int*(age-55) /10*(current_smoker) +
cf10$age_bmi_int*(age-55)/10*(max(BMI, 30)-30)/5 +
cf10$age_egfr_int*(age-55) /10*(min(eGFR, 60)-60) / -15 +
ifelse(SDI_missing, cf10$sdi_missing,
ifelse(SDI4_6, cf10$sdi_4_6,
ifelse(SDI7_10, cf10$sdi__10, 0))) +
ifelse(UACR_missing, cf10$acr_missing, cf10$ln_acr*log(UACR)) +
ifelse(HbA1c_missing, cf10$a1c_missing,
ifelse(diabetes, cf10$a1c_dm*(HbA1c-5.3),
ifelse(!diabetes, cf10$a1c_nondm*(HbA1c-5.3))))
# # 30 YEAR BASE MODEL
cf30 <- cfs_full30yr[cfs_full30yr$risk=={{risk_query}} & cfs_full30yr$gender=={{gender_query}}, ]
# construct the model
log_odds30 =
cf30$constant +
cf30$age*(age-55) /10 + # AGE
cf30$age_sq*((age-55) /10)^2 + # AGE
cf30$nonHDL*(Tc-HDL-3.5) + # NON-HDL CHOL
cf30$HDL*(HDL-1.3) /0.3 +
cf30$sbp_under110*(min(SBP, 110)-110) /20 +
cf30$sbp_over110*(max(SBP, 110)-130) /20 +
cf30$diabetes*(diabetes) +
cf30$current_smoker*(current_smoker) +
# BMI used in HF calc only
cf30$bmi_under30*(min(BMI, 30)-25) / 5 +
cf30$bmi_over30*(max(BMI, 30)-30) / 5 +
cf30$eGFR_under60*(min(eGFR, 60)-60) / -15 +
cf30$eGFR_over60*(max(eGFR, 60)-90) / -15 +
cf30$using_antihypertensive_medication*(using_antihypertensive_medication)+
cf30$using_statin*(using_statin)+
cf30$sbp_treated_over110*(using_antihypertensive_medication)*(max(SBP, 110)-130) /20 +
cf30$treated_nonHDL*(using_statin)*(Tc-HDL-3.5) +
cf30$age_nonHDL_int*(age-55) /10*(Tc-HDL-3.5) +
cf30$age_HDL_int*(age-55) /10*(HDL-1.3) /0.3 +
cf30$age_sbp_int*(age-55) /10*(max(SBP, 110)-130)/20 +
cf30$age_diabetes_int*(age-55) /10*(diabetes) +
cf30$age_smoking_int*(age-55) /10*(current_smoker) +
cf30$age_bmi_int*(age-55)/10*(max(BMI, 30)-30)/5 +
cf30$age_egfr_int*(age-55) /10*(min(eGFR, 60)-60) / -15 +
ifelse(SDI_missing, cf30$sdi_missing,
ifelse(SDI4_6, cf30$sdi_4_6,
ifelse(SDI7_10, cf30$sdi__10, 0))) +
ifelse(UACR_missing, cf30$acr_missing, cf30$ln_acr*log(UACR)) +
ifelse(HbA1c_missing, cf30$a1c_missing,
ifelse(diabetes, cf30$a1c_dm*(HbA1c-5.3),
ifelse(!diabetes, cf30$a1c_nondm*(HbA1c-5.3))))
Risk10 = exp(log_odds10) / (1 + exp(log_odds10))
Risk30 = exp(log_odds30) / (1 + exp(log_odds30))
res = data.frame(Risk10*100,
Risk30*100)
names(res) <- paste0(risk, c("_10yr", "_30yr"))
return(res)
}
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