In Roche-GSK/admiral: ADaM in R Asset Library
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(admiraldev)
Introduction
Within the ADLB ADaM data set there is a concept of lab grading, where there is
a set of criteria for particular lab tests that grade the severity or abnormality of
a lab value. The grades are from 0 to 4, where grade 0 can be viewed generally as a
"NORMAL" value. The higher the grade the more severe or more abnormal the lab value is.
There are several sets of lab grading criteria, currently {admiral} has implemented
NCI-CTCAEv4, NCI-CTCAEv5 and DAIDS grading criteria. In future releases {admiral} may look
to implement further grading criteria.
The NCI-CTCAE version 4 and 5 grading criteria can be found
here: https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm .
The NCI-CTCAEv4 criteria can be found under the heading
Common Terminology Criteria for Adverse Events (CTCAE)v4.0
The NCI-CTCAEv5 criteria can be found under the heading
Common Terminology Criteria for Adverse Events (CTCAE)v5.0
The DAIDS grading criteria can be found
here: https://rsc.niaid.nih.gov/clinical-research-sites/daids-adverse-event-grading-tables .
The DAIDS criteria can be found under the heading
DAIDS Table for Grading the Severity of Adult and Pediatric Adverse Events Corrected Version 2.1
Grading metadata
{admiral} will store a metadata data set for each set of grading criteria in the data folder of
{admiral}. Currently, we have atoxgr_criteria_ctcv4() for NCI-CTCAEv4, atoxgr_criteria_ctcv5()
for NCI-CTCAEv5 and atoxgr_criteria_daids() for DAIDS. Each metadata data set has required
variables and optional variables, the optional variables are purely for transparency, and will
contain detailed information about the grading criteria. The required variables are those used by
{admiral} to create the grade.
Structure of metadata set
The metadata data set has the following structure for the required variables:
Variable | Scope | Type | Example Value
------- | -------- | ------ | --------
TERM | Term describing the criteria applied to a particular lab test.| Character | "Anemia"
DIRECTION | The direction of the abnormality of a particular lab test value| Character | "L" or "H".
SI_UNIT_CHECK | Unit of lab test, to check against input data if criteria is based on absolute values. | Character | "mmol/L"
VAR_CHECK | Comma separated list of variables used in criteria, to check input data that variables exist. | Character | "AVAL, ANRLO"
FILTER | Only required for DAIDS grading. Variable to hold code that filters the lab data based on contents of column SUBGROUP. | Character |R code that is valid within a filter function call.
GRADE_CRITERIA_CODE | Variable to hold code that creates grade based on defined criteria. | Character |R code that is a valid case statement within a mutate function call.
The metadata data set has the following structure for the optional variables:
Variable | Scope | Type | Example Value
------- | -------- | ------ | --------
SOC | System Organ Class the lab test belongs to.| Character | "Investigations"
SUBGROUP | Only required for DAIDS grading. Description of subgroup of lab data.| Character | "> 15 years of age".
GRADE_1 | Grade 1 criteria for lab test, normally straight from source document.| Character | ">ULN - 3.0 x ULN".
GRADE_2 | Grade 2 criteria for lab test, normally straight from source document.| Character | ">3.0 - 5.0 x ULN".
GRADE_3 | Grade 3 criteria for lab test, normally straight from source document.| Character | ">5.0 - 20.0 x ULN".
GRADE_4 | Grade 4 criteria for lab test, normally straight from source document.| Character | ">20.0 x ULN".
DEFINITION | Definition of abnormality, normally from source document.| Character | "A finding based on laboratory test results that indicate an increase in the level of alanine aminotransferase (ALT or SGPT) in the blood specimen.".
COMMENT | Description of any decisions made by {admiral} to implement grading criteria, where grading criteria alone was ambiguous. | Character | "Take worst case and assume on anticoagulation".
Handling floating points when comparing numeric values
When comparing numeric values, for example AVAL > 1.1*ANRHI, unexpected results can occur
due to floating point issues. To solve this issue {admiral} used the signif() function on
both side of the equation, the number of significant digits used to compare is passed into the
function derive_var_atoxgr_dir() via the argument signif_dig. Please see documentation of the
function for more details and the blog post How admiral handles floating points for more context.
Creating the Lab Grade
Mapping ADLB to the TERM variable in the {admiral} metadata data set
library(admiral)
library(pharmaversesdtm)
library(dplyr, warn.conflicts = FALSE)
library(stringr)
library(tibble)
lb <- pharmaversesdtm::lb
adsl <- admiral::admiral_adsl
lb <- convert_blanks_to_na(lb)
lb <- filter(lb, USUBJID %in% c("01-701-1115", "01-705-1186", "01-705-1349", "01-708-1286", "01-707-1037", "01-716-1024"))
Each company needs to map their lab test to a term that describes the criteria being applied.
The list of terms defined in the {admiral} metadata to implement NCI-CTCAEv4 is below:
atoxgr_criteria_ctcv4 %>%
filter(!is.na(TERM)) %>%
dataset_vignette(
display_vars = exprs(TERM)
)
Likewise, the list of terms defined in the {admiral} metadata to implement NCI-CTCAEv5 is below:
(Terms identical to NCI-CTCAEv4, except Hyperglycemia, Hyperglycemia (Fasting) and Hypophosphatemia)
which are not present in NCI-CTCAEv5.
atoxgr_criteria_ctcv5 %>%
filter(!is.na(TERM)) %>%
dataset_vignette(
display_vars = exprs(TERM)
)
Finally, the list of terms defined in the {admiral} metadata to implement DAIDS is below:
atoxgr_criteria_daids %>%
filter(!is.na(TERM)) %>%
distinct(TERM) %>%
dataset_vignette(
display_vars = exprs(TERM)
)
Using CDISC data these lab tests can be mapped to the correct terms, firstly create
PARAMCD, PARAM, AVAL, ANRLO and ANRHI, also some lab grading criteria require BASE
and PCHG, so these would also need to be created before running derive_var_atoxgr_dir()
function.
# Look-up tables ----
# Assign PARAMCD, PARAM, and PARAMN
param_lookup <- tibble::tribble(
~LBTESTCD, ~PARAMCD, ~PARAM, ~PARAMN,
"ALB", "ALB", "Albumin (g/L)", 1,
"ALP", "ALKPH", "Alkaline Phosphatase (U/L)", 2,
"ALT", "ALT", "Alanine Aminotransferase (U/L)", 3,
"ANISO", "ANISO", "Anisocytes", 4,
"AST", "AST", "Aspartate Aminotransferase (U/L)", 5,
"BASO", "BASO", "Basophils (10^9/L)", 6,
"BASOLE", "BASOLE", "Basophils/Leukocytes (FRACTION)", 7,
"BILI", "BILI", "Bilirubin (umol/L)", 8,
"BUN", "BUN", "Blood Urea Nitrogen (mmol/L)", 9,
"CA", "CA", "Calcium (mmol/L)", 10,
"CHOL", "CHOLES", "Cholesterol (mmol/L)", 11,
"CK", "CK", "Creatinine Kinase (U/L)", 12,
"CL", "CL", "Chloride (mmol/L)", 13,
"COLOR", "COLOR", "Color", 14,
"CREAT", "CREAT", "Creatinine (umol/L)", 15,
"EOS", "EOS", "Eosinophils (10^9/L)", 16,
"EOSLE", "EOSLE", "Eosinophils/Leukocytes (FRACTION)", 17,
"GGT", "GGT", "Gamma Glutamyl Transferase (U/L)", 18,
"GLUC", "GLUC", "Glucose (mmol/L)", 19,
"HBA1C", "HBA1C", "Hemoglobin A1C (1)", 20,
"HCT", "HCT", "Hematocrit (1)", 21,
"HGB", "HGB", "Hemoglobin (mmol/L)", 22,
"K", "POTAS", "Potassium (mmol/L)", 23,
"KETONES", "KETON", "Ketones", 24,
"LYM", "LYMPH", "Lymphocytes (10^9/L)", 25,
"LYMLE", "LYMPHLE", "Lymphocytes/Leukocytes (FRACTION)", 26,
"MACROCY", "MACROC", "Macrocytes", 27,
"MCH", "MCH", "Ery. Mean Corpuscular Hemoglobin (fmol(Fe))", 28,
"MCHC", "MCHC", "Ery. Mean Corpuscular HGB Concentration (mmol/L)", 29,
"MCV", "MCV", "Ery. Mean Corpuscular Volume (f/L)", 30,
"MICROCY", "MICROC", "Microcytes", 31,
"MONO", "MONO", "Monocytes (10^9/L)", 32,
"MONOLE", "MONOLE", "Monocytes/Leukocytes (FRACTION)", 33,
"PH", "PH", "pH", 34,
"PHOS", "PHOS", "Phosphate (mmol/L)", 35,
"PLAT", "PLAT", "Platelet (10^9/L)", 36,
"POIKILO", "POIKIL", "Poikilocytes", 37,
"POLYCHR", "POLYCH", "Polychromasia", 38,
"PROT", "PROT", "Protein (g/L)", 39,
"RBC", "RBC", "Erythrocytes (TI/L)", 40,
"SODIUM", "SODIUM", "Sodium (mmol/L)", 41,
"SPGRAV", "SPGRAV", "Specific Gravity", 42,
"TSH", "TSH", "Thyrotropin (mU/L)", 43,
"URATE", "URATE", "Urate (umol/L)", 44,
"UROBIL", "UROBIL", "Urobilinogen", 45,
"VITB12", "VITB12", "Vitamin B12 (pmol/L)", 46,
"WBC", "WBC", "Leukocytes (10^9/L)", 47
)
adlb <- lb %>%
## Add PARAMCD PARAM and PARAMN - from LOOK-UP table
derive_vars_merged_lookup(
dataset_add = param_lookup,
new_vars = exprs(PARAMCD, PARAM, PARAMN),
by_vars = exprs(LBTESTCD)
) %>%
## Calculate PARCAT1 AVAL AVALC ANRLO ANRHI
## Dummy the values for BASE
mutate(
PARCAT1 = LBCAT,
AVAL = LBSTRESN,
AVALC = ifelse(
is.na(LBSTRESN) | as.character(LBSTRESN) != LBSTRESC,
LBSTRESC,
NA
),
ANRLO = LBSTNRLO,
ANRHI = LBSTNRHI,
BASE = AVAL - 10
)
Another look-up table is used to add on ATOXDSCL and ATOXDSCH using
PARAMCD. ATOXDSCL holds the terms for grading low lab values, and ATOXDSCH holds
the terms for grading high lab values. The names of these variables can be user-defined.
ATOXDSCL and ATOXDSCH are the link from ADLB data to the {admiral} metadata that
holds the grading criteria.
# Assign ATOXDSCL and ATOXDSCH to hold lab grading terms
# ATOXDSCL and ATOXDSCH hold terms defined by NCI-CTCAEv4.
grade_lookup <- tibble::tribble(
~PARAMCD, ~ATOXDSCL, ~ATOXDSCH,
"ALB", "Hypoalbuminemia", NA_character_,
"ALKPH", NA_character_, "Alkaline phosphatase increased",
"ALT", NA_character_, "Alanine aminotransferase increased",
"AST", NA_character_, "Aspartate aminotransferase increased",
"BILI", NA_character_, "Blood bilirubin increased",
"CA", "Hypocalcemia", "Hypercalcemia",
"CHOLES", NA_character_, "Cholesterol high",
"CK", NA_character_, "CPK increased",
"CREAT", NA_character_, "Creatinine increased",
"GGT", NA_character_, "GGT increased",
"GLUC", "Hypoglycemia", "Hyperglycemia",
"HGB", "Anemia", "Hemoglobin increased",
"POTAS", "Hypokalemia", "Hyperkalemia",
"LYMPH", "CD4 lymphocytes decreased", NA_character_,
"PHOS", "Hypophosphatemia", NA_character_,
"PLAT", "Platelet count decreased", NA_character_,
"SODIUM", "Hyponatremia", "Hypernatremia",
"WBC", "White blood cell decreased", "Leukocytosis",
)
adlb <- adlb %>%
derive_vars_merged(
dataset_add = grade_lookup,
by_vars = exprs(PARAMCD),
)
It is now straightforward to create the grade, for low lab values the grade will
be held in ATOXGRL and for high lab values the grade will be held in ATOXGRH.
Note: for NCICTCAEv5 grading, you would update meta_criteria parameter to
atoxgr_criteria_ctcv5 and for DAIDS grading you would update meta_criteria
parameter to atoxgr_criteria_daids
adlb <- adlb %>%
derive_var_atoxgr_dir(
new_var = ATOXGRL,
tox_description_var = ATOXDSCL,
meta_criteria = atoxgr_criteria_ctcv4,
criteria_direction = "L",
get_unit_expr = extract_unit(PARAM)
) %>%
derive_var_atoxgr_dir(
new_var = ATOXGRH,
tox_description_var = ATOXDSCH,
meta_criteria = atoxgr_criteria_ctcv4,
criteria_direction = "H",
get_unit_expr = extract_unit(PARAM)
)
Note: {admiral} does not grade 'Anemia' or 'Hemoglobin Increased' because the metadata is
based on the SI unit of 'g/L', however the CDISC data has SI unit of 'mmol/L'.
Please see SI_UNIT_CHECK variable in {admiral} metadata atoxgr_criteria_ctcv4() or
atoxgr_criteria_ctcv5() or atoxgr_criteria_daids(), the metadata is in the data folder
of {admiral}.
atoxgr_criteria_ctcv4 %>%
filter(!is.na(SI_UNIT_CHECK)) %>%
dataset_vignette(
display_vars = exprs(TERM, SI_UNIT_CHECK),
)
{admiral} also gives the option to combine ATOXGRL and ATOXGRH into one variable,
namely ATOXGR. Grades held in ATOXGRL will be given a negative value in ATOXGR
to distinguish between low and high values.
adlb <- adlb %>%
derive_var_atoxgr()
adlb %>%
filter((ATOXGRL == "1") | (ATOXGRH == "1")) %>%
dataset_vignette(
display_vars = exprs(ATOXDSCL, ATOXDSCH, ATOXGRL, ATOXGRH, ATOXGR)
)
NCI-CTCAEV4 implementation{#implement_ctcv4}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following CTCAE v4.0 SOC values were identified for grading, these are "Investigations",
"Metabolism and nutrition disorders" and "Blood and lymphatic system disorders".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Investigations" there are 21 CTCAE v4.0 Terms:
- Activated partial thromboplastin time prolonged
- Alanine aminotransferase increased
- Alkaline phosphatase increased
- Aspartate aminotransferase increased
- Blood bilirubin increased
- CD4 lymphocytes decreased
- Cholesterol high
- CPK increased
- Creatinine increased
- Fibrinogen decreased
- GGT increased
- Haptoglobin decreased
- Hemoglobin increased
- INR increased
- Lipase increased
- Lymphocyte count decreased
- Lymphocyte count increased
- Neutrophil count decreased
- Platelet count decreased
- Serum amylase increased
- White blood cell decreased
From the SOC = "Metabolism and nutrition disorders" there are 14 CTCAE v4.0 Terms:
- Hypercalcemia
- Hyperglycemia
- Hyperkalemia
- Hypermagnesemia
- Hypernatremia
- Hypertriglyceridemia
- Hyperuricemia
- Hypoalbuminemia
- Hypocalcemia
- Hypoglycemia
- Hypokalemia
- Hypomagnesemia
- Hyponatremia
- Hypophosphatemia
From the SOC = "Blood and lymphatic system disorders" there are 2 CTCAE v4.0 Terms:
- Anemia
- Leukocytosis
Updates made to TERM
For terms "Hypocalcemia" and "Hypercalcemia" the criteria is provided for Calcium and Ionized Calcium,
therefore {admiral} created a row for each in the metadata, this is noted in the COMMENT variable of
the metadata:
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "calcemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Similarly, there is criteria applicable to Fasting Glucose as well as non-Fasting Glucose for "Hyperglycemia"
so again this was split into 2 rows, and noted in the COMMENT variable. Note "Hypoglycemia" does not require to
be split into 2 rows:
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "glycemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Assumptions made when grading
For term "INR Increased" there is the following criteria:
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "INR")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1)
)
{admiral} assumed worst case and used both parts of the criteria for grading, so comparing
lab value against ULN and also BASE. The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "INR")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
For TERM "Hyperuricemia", the criteria for Grade 1 and Grade 3 is the same with respect
to the lab value, so worse case is assumed as grade 3. The decision made was
put in the COMMENT field.
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "Hyperuricemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT)
)
A similar approach was taken for TERM "Hypokalemia" where Grade 1 and Grade 2 criteria
is the same with respect to the lab value, so worse case is assumed as grade 2.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv4 %>%
filter(str_detect(TERM, "Hypokalemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_2, COMMENT)
)
Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v4
atoxgr_criteria_ctcv4 %>%
filter(!is.na(COMMENT) & str_detect(TERM, "glycemia", negate = TRUE) &
str_detect(TERM, "calcemia", negate = TRUE)) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, Grade_4, COMMENT)
)
NCI-CTCAEV5 implementation {#implement_ctcv5}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following CTCAE v5.0 SOC values were identified for grading, these are "Investigations",
"Metabolism and nutrition disorders" and "Blood and lymphatic system disorders".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Investigations" there are 21 CTCAE v5.0 Terms:
- Activated partial thromboplastin time prolonged
- Alanine aminotransferase increased
- Alkaline phosphatase increased
- Aspartate aminotransferase increased
- Blood bilirubin increased
- CD4 lymphocytes decreased
- Cholesterol high
- CPK increased
- Creatinine increased
- Fibrinogen decreased
- GGT increased
- Haptoglobin decreased
- Hemoglobin increased
- INR increased
- Lipase increased
- Lymphocyte count decreased
- Lymphocyte count increased
- Neutrophil count decreased
- Platelet count decreased
- Serum amylase increased
- White blood cell decreased
Note: These are the same terms identified for NCI-CTCAEv4.
From the SOC = "Metabolism and nutrition disorders" there are 12 CTCAE v5.0 Terms:
- Hypercalcemia
- Hyperkalemia
- Hypermagnesemia
- Hypernatremia
- Hypertriglyceridemia
- Hyperuricemia
- Hypoalbuminemia
- Hypocalcemia
- Hypoglycemia
- Hypokalemia
- Hypomagnesemia
- Hyponatremia
Note: These are the same terms identified for NCI-CTCAEv4, except “Hypophosphatemia”
and “Hyperglycemia” which are not gradable by quantitative lab values in NCICTCAEv5
grading criteria.
From the SOC = "Blood and lymphatic system disorders" there are 2 CTCAE v5.0 Terms:
- Anemia
- Leukocytosis
Note: These are the same terms identified for NCI-CTCAEv4.
Updates made to TERM
For terms "Hypocalcemia" and "Hypercalcemia" the criteria is provided for Calcium and Ionized Calcium,
therefore {admiral} created a row for each in the metadata, this is noted in the COMMENT variable of
the metadata:
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "calcemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Criteria dependent on the Baseline Value
For terms "Alanine aminotransferase increased", "Alkaline phosphatase increased",
"Aspartate aminotransferase increased", "Blood bilirubin increased" and "GGT increased" the criteria
is dependent on the Baseline Value being normal or abnormal. We use the variable BNRIND to determine
this, and users can pass in the value(s) of BNRIND that indicate the baseline is abnormal via the argument
abnormal_indicator in the function derive_var_atoxgr_dir().
For example, setting abnormal_indicator = "HIGH" would mean any observations with BNRIND = "HIGH" would
be deemed to have an abnormal Baseline Value.
Note: you can pass in multiple values, abnormal_indicator = c("H", "HIGH")
For these 5 terms, this means if BNRIND is abnormal then the grade is always zero for the baseline observation.
This is the conservative approach, to ensure you don't see big changes in grade for a patient after they take treatment.
Some companies may not want the baseline observation graded at all, so would post-process, if necessary.
Assumptions made when grading
For term "INR Increased" there is the following criteria:
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "INR")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1)
)
{admiral} assumed worst case and used both parts of the criteria for grading, so comparing
lab value against ULN and also BASE. The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "INR")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Similarly, for terms "Lipase Increased" and "Serum amylase increased" there is the following criteria:
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_2, Grade_3, Grade_4)
)
{admiral} assumed worst case and implemented highest grade possible.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
For TERM "Hyperuricemia", the criteria for Grade 1 and Grade 3 is the same with respect
to the lab value, so worse case is assumed as grade 3. The decision made was
put in the COMMENT field.
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "Hyperuricemia")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT)
)
A similar approach was taken for TERM "Hypokalemia" and "Hyponatremia". For "Hypokalemia",
where Grade 1 and Grade 2 criteria is the same with respect to the lab value, then worse case
is assumed as grade 2. For "Hyponatremia", where Grade 2 and Grade 3 criteria is the same with
respect to the lab value, then worse case is assumed as grade 3.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>%
filter(str_detect(TERM, "Hypokalemia") | str_detect(TERM, "Hyponatremia")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, COMMENT)
)
Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v5
atoxgr_criteria_ctcv5 %>%
filter(!is.na(COMMENT) & str_detect(TERM, "calcemia", negate = TRUE)) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, Grade_4, COMMENT)
)
DAIDS implementation {#implement_daids}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following DAIDS SOC values were identified for grading, these are "Chemistries" and
"Hematology".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Chemistries" there are 31 DAIDS Terms:
- Acidosis
- Albumin, Low
- Alkaline Phosphatase, High
- Alkalosis
- ALT, High
- Amylase, High
- AST, High
- Bicarbonate, Low
- Direct Bilirubin, High
- Total Bilirubin, High
- Calcium, High
- Calcium (Ionized), High
- Calcium, Low
- Calcium (Ionized), Low
- Creatine Kinase, High
- Creatinine, High
- Glucose Fasting, High
- Glucose Nonfasting, High
- Glucose, Low
- Lactate, High
- Lipase, High
- Cholesterol, Fasting, High
- LDL, Fasting, High
- Triglycerides, Fasting, High
- Magnesium, Low
- Phosphate, Low
- Potassium, High
- Potassium, Low
- Sodium, High
- Sodium, Low
- Uric Acid, High
Note: {admiral} does not grade for TERM = "Total Bilirubin, High" when AGE <= 28 days,
these criteria are in Appendix of DAIDS Table for Grading the Severity of Adult and Pediatric Adverse Events Corrected Version 2.1.
From the SOC = "Hematology" there are 11 DAIDS Terms:
- Absolute CD4+ Count, Low
- Absolute Lymphocyte Count, Low
- Absolute Neutrophil Count (ANC), Low
- Fibrinogen Decreased
- Hemoglobin, Low
- INR, High
- Methemoglobin
- PTT, High
- Platelets, Decreased
- PT, High
- WBC, Decreased
Terms with age or sex dependent grading criteria
Some terms defined in DAIDS have age or sex dependent grading criteria, {admiral}
handles this in variable FILTER in the metadata. We use {admiral} function
compute_duration to calculate age, see TERM = "Cholesterol, Fasting, High":
atoxgr_criteria_daids %>%
filter(str_detect(TERM, "Cholesterol")) %>%
dataset_vignette(
display_vars = exprs(TERM, FILTER)
)
Note: All possible values must be covered for each TERM defined, for TERM =
"Absolute Lymphocyte Count, Low" and "Absolute CD4+ Count, Low" there is only grading
criteria defined for age > 5 years. Therefore, we add another row with age <= 5 years
and set grade to missing. Similarly, for TERM = "LDL, Fasting, High" there is only grading
criteria defined for age > 2 years. Therefore, we add another row with age <= 2 years and
set grade to missing.
atoxgr_criteria_daids %>%
filter(str_detect(COMMENT, "No criteria given")) %>%
dataset_vignette(
display_vars = exprs(TERM, FILTER, GRADE_CRITERIA_CODE)
)
Assumptions made when grading
For terms "INR, High", "PT, High" and "PTT, High", the criteria is based on subjects
"not on anticoagulation therapy", this is captured in COMMENT field.
atoxgr_criteria_daids %>%
filter(TERM %in% c("INR, High", "PT, High", "PTT, High")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Similarly, for terms "Absolute CD4+ Count, Low" and "Absolute Lymphocyte Count, Low",
the criteria is based on subjects "not HIV infected", this is captured in COMMENT field.
atoxgr_criteria_daids %>%
filter(str_detect(COMMENT, "HIV infected")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
For term "Acidosis", "Alkalosis" and "Direct Bilirubin, High (> 28 days of age)",
{admiral} grades as high as possible, so assumes worst case and subject has
"life-threatening consequences".
This is captured in COMMENT field.
atoxgr_criteria_daids %>%
filter(str_detect(COMMENT, "lifethreatening")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Similarly, for term "Lactate, High", {admiral} only grade 1 and 2, and there is the following criteria:
atoxgr_criteria_daids %>%
filter(str_detect(TERM, "Lactate")) %>%
dataset_vignette(
display_vars = exprs(TERM, Grade_1, Grade_1)
)
{admiral} assumed worst case and assume "without acidosis".
The decision made was put in the COMMENT field.
atoxgr_criteria_daids %>%
filter(str_detect(TERM, "Lactate")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
For TERM "Direct Bilirubin, High (<= 28 days of age)" and "Uric Acid, High" the
criteria is not given in SI unit. The conversion to SI unit is in the comment field.
atoxgr_criteria_daids %>%
filter(str_detect(COMMENT, "conver")) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Here is a complete list of all assumptions in the metadata for DAIDs
atoxgr_criteria_daids %>%
filter(!is.na(COMMENT) & str_detect(COMMENT, "No criteria given", negate = TRUE) &
str_detect(COMMENT, "Did not grade", negate = TRUE)) %>%
arrange(TERM) %>%
dataset_vignette(
display_vars = exprs(TERM, COMMENT)
)
Conclusion
With NCI-CTCAEv4, NCI-CTCAEv5 and DAIDS grading now implemented, {admiral} may look to
implement other industry standard grading criteria. Providing tools for users to easily
interact with the metadata to update criteria, based on their companies needs will also
be looked at. Ideally, users should be able to create their own metadata for company specific
grading schemes.
Roche-GSK/admiral documentation built on April 14, 2025, 12:36 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(admiraldev)
Introduction
Within the ADLB ADaM data set there is a concept of lab grading, where there is
a set of criteria for particular lab tests that grade the severity or abnormality of
a lab value. The grades are from 0 to 4, where grade 0 can be viewed generally as a
"NORMAL" value. The higher the grade the more severe or more abnormal the lab value is.
There are several sets of lab grading criteria, currently {admiral} has implemented
NCI-CTCAEv4, NCI-CTCAEv5 and DAIDS grading criteria. In future releases {admiral} may look
to implement further grading criteria.
The NCI-CTCAE version 4 and 5 grading criteria can be found here: https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm .
The NCI-CTCAEv4 criteria can be found under the heading Common Terminology Criteria for Adverse Events (CTCAE)v4.0
The NCI-CTCAEv5 criteria can be found under the heading Common Terminology Criteria for Adverse Events (CTCAE)v5.0
The DAIDS grading criteria can be found here: https://rsc.niaid.nih.gov/clinical-research-sites/daids-adverse-event-grading-tables .
The DAIDS criteria can be found under the heading DAIDS Table for Grading the Severity of Adult and Pediatric Adverse Events Corrected Version 2.1
Grading metadata
{admiral} will store a metadata data set for each set of grading criteria in the data folder of
{admiral}. Currently, we have atoxgr_criteria_ctcv4() for NCI-CTCAEv4, atoxgr_criteria_ctcv5()
for NCI-CTCAEv5 and atoxgr_criteria_daids() for DAIDS. Each metadata data set has required
variables and optional variables, the optional variables are purely for transparency, and will
contain detailed information about the grading criteria. The required variables are those used by
{admiral} to create the grade.
Structure of metadata set
The metadata data set has the following structure for the required variables:
Variable | Scope | Type | Example Value
------- | -------- | ------ | --------
TERM | Term describing the criteria applied to a particular lab test.| Character | "Anemia"
DIRECTION | The direction of the abnormality of a particular lab test value| Character | "L" or "H".
SI_UNIT_CHECK | Unit of lab test, to check against input data if criteria is based on absolute values. | Character | "mmol/L"
VAR_CHECK | Comma separated list of variables used in criteria, to check input data that variables exist. | Character | "AVAL, ANRLO"
FILTER | Only required for DAIDS grading. Variable to hold code that filters the lab data based on contents of column SUBGROUP. | Character |R code that is valid within a filter function call.
GRADE_CRITERIA_CODE | Variable to hold code that creates grade based on defined criteria. | Character |R code that is a valid case statement within a mutate function call.
The metadata data set has the following structure for the optional variables:
Variable | Scope | Type | Example Value
------- | -------- | ------ | --------
SOC | System Organ Class the lab test belongs to.| Character | "Investigations"
SUBGROUP | Only required for DAIDS grading. Description of subgroup of lab data.| Character | "> 15 years of age".
GRADE_1 | Grade 1 criteria for lab test, normally straight from source document.| Character | ">ULN - 3.0 x ULN".
GRADE_2 | Grade 2 criteria for lab test, normally straight from source document.| Character | ">3.0 - 5.0 x ULN".
GRADE_3 | Grade 3 criteria for lab test, normally straight from source document.| Character | ">5.0 - 20.0 x ULN".
GRADE_4 | Grade 4 criteria for lab test, normally straight from source document.| Character | ">20.0 x ULN".
DEFINITION | Definition of abnormality, normally from source document.| Character | "A finding based on laboratory test results that indicate an increase in the level of alanine aminotransferase (ALT or SGPT) in the blood specimen.".
COMMENT | Description of any decisions made by {admiral} to implement grading criteria, where grading criteria alone was ambiguous. | Character | "Take worst case and assume on anticoagulation".
Handling floating points when comparing numeric values
When comparing numeric values, for example AVAL > 1.1*ANRHI, unexpected results can occur
due to floating point issues. To solve this issue {admiral} used the signif() function on
both side of the equation, the number of significant digits used to compare is passed into the
function derive_var_atoxgr_dir() via the argument signif_dig. Please see documentation of the
function for more details and the blog post How admiral handles floating points for more context.
Creating the Lab Grade
Mapping ADLB to the TERM variable in the {admiral} metadata data set
library(admiral) library(pharmaversesdtm) library(dplyr, warn.conflicts = FALSE) library(stringr) library(tibble) lb <- pharmaversesdtm::lb adsl <- admiral::admiral_adsl lb <- convert_blanks_to_na(lb)
lb <- filter(lb, USUBJID %in% c("01-701-1115", "01-705-1186", "01-705-1349", "01-708-1286", "01-707-1037", "01-716-1024"))
Each company needs to map their lab test to a term that describes the criteria being applied.
The list of terms defined in the {admiral} metadata to implement NCI-CTCAEv4 is below:
atoxgr_criteria_ctcv4 %>% filter(!is.na(TERM)) %>% dataset_vignette( display_vars = exprs(TERM) )
Likewise, the list of terms defined in the {admiral} metadata to implement NCI-CTCAEv5 is below:
(Terms identical to NCI-CTCAEv4, except Hyperglycemia, Hyperglycemia (Fasting) and Hypophosphatemia)
which are not present in NCI-CTCAEv5.
atoxgr_criteria_ctcv5 %>% filter(!is.na(TERM)) %>% dataset_vignette( display_vars = exprs(TERM) )
Finally, the list of terms defined in the {admiral} metadata to implement DAIDS is below:
atoxgr_criteria_daids %>% filter(!is.na(TERM)) %>% distinct(TERM) %>% dataset_vignette( display_vars = exprs(TERM) )
Using CDISC data these lab tests can be mapped to the correct terms, firstly create
PARAMCD, PARAM, AVAL, ANRLO and ANRHI, also some lab grading criteria require BASE
and PCHG, so these would also need to be created before running derive_var_atoxgr_dir()
function.
# Look-up tables ---- # Assign PARAMCD, PARAM, and PARAMN param_lookup <- tibble::tribble( ~LBTESTCD, ~PARAMCD, ~PARAM, ~PARAMN, "ALB", "ALB", "Albumin (g/L)", 1, "ALP", "ALKPH", "Alkaline Phosphatase (U/L)", 2, "ALT", "ALT", "Alanine Aminotransferase (U/L)", 3, "ANISO", "ANISO", "Anisocytes", 4, "AST", "AST", "Aspartate Aminotransferase (U/L)", 5, "BASO", "BASO", "Basophils (10^9/L)", 6, "BASOLE", "BASOLE", "Basophils/Leukocytes (FRACTION)", 7, "BILI", "BILI", "Bilirubin (umol/L)", 8, "BUN", "BUN", "Blood Urea Nitrogen (mmol/L)", 9, "CA", "CA", "Calcium (mmol/L)", 10, "CHOL", "CHOLES", "Cholesterol (mmol/L)", 11, "CK", "CK", "Creatinine Kinase (U/L)", 12, "CL", "CL", "Chloride (mmol/L)", 13, "COLOR", "COLOR", "Color", 14, "CREAT", "CREAT", "Creatinine (umol/L)", 15, "EOS", "EOS", "Eosinophils (10^9/L)", 16, "EOSLE", "EOSLE", "Eosinophils/Leukocytes (FRACTION)", 17, "GGT", "GGT", "Gamma Glutamyl Transferase (U/L)", 18, "GLUC", "GLUC", "Glucose (mmol/L)", 19, "HBA1C", "HBA1C", "Hemoglobin A1C (1)", 20, "HCT", "HCT", "Hematocrit (1)", 21, "HGB", "HGB", "Hemoglobin (mmol/L)", 22, "K", "POTAS", "Potassium (mmol/L)", 23, "KETONES", "KETON", "Ketones", 24, "LYM", "LYMPH", "Lymphocytes (10^9/L)", 25, "LYMLE", "LYMPHLE", "Lymphocytes/Leukocytes (FRACTION)", 26, "MACROCY", "MACROC", "Macrocytes", 27, "MCH", "MCH", "Ery. Mean Corpuscular Hemoglobin (fmol(Fe))", 28, "MCHC", "MCHC", "Ery. Mean Corpuscular HGB Concentration (mmol/L)", 29, "MCV", "MCV", "Ery. Mean Corpuscular Volume (f/L)", 30, "MICROCY", "MICROC", "Microcytes", 31, "MONO", "MONO", "Monocytes (10^9/L)", 32, "MONOLE", "MONOLE", "Monocytes/Leukocytes (FRACTION)", 33, "PH", "PH", "pH", 34, "PHOS", "PHOS", "Phosphate (mmol/L)", 35, "PLAT", "PLAT", "Platelet (10^9/L)", 36, "POIKILO", "POIKIL", "Poikilocytes", 37, "POLYCHR", "POLYCH", "Polychromasia", 38, "PROT", "PROT", "Protein (g/L)", 39, "RBC", "RBC", "Erythrocytes (TI/L)", 40, "SODIUM", "SODIUM", "Sodium (mmol/L)", 41, "SPGRAV", "SPGRAV", "Specific Gravity", 42, "TSH", "TSH", "Thyrotropin (mU/L)", 43, "URATE", "URATE", "Urate (umol/L)", 44, "UROBIL", "UROBIL", "Urobilinogen", 45, "VITB12", "VITB12", "Vitamin B12 (pmol/L)", 46, "WBC", "WBC", "Leukocytes (10^9/L)", 47 ) adlb <- lb %>% ## Add PARAMCD PARAM and PARAMN - from LOOK-UP table derive_vars_merged_lookup( dataset_add = param_lookup, new_vars = exprs(PARAMCD, PARAM, PARAMN), by_vars = exprs(LBTESTCD) ) %>% ## Calculate PARCAT1 AVAL AVALC ANRLO ANRHI ## Dummy the values for BASE mutate( PARCAT1 = LBCAT, AVAL = LBSTRESN, AVALC = ifelse( is.na(LBSTRESN) | as.character(LBSTRESN) != LBSTRESC, LBSTRESC, NA ), ANRLO = LBSTNRLO, ANRHI = LBSTNRHI, BASE = AVAL - 10 )
Another look-up table is used to add on ATOXDSCL and ATOXDSCH using
PARAMCD. ATOXDSCL holds the terms for grading low lab values, and ATOXDSCH holds
the terms for grading high lab values. The names of these variables can be user-defined.
ATOXDSCL and ATOXDSCH are the link from ADLB data to the {admiral} metadata that
holds the grading criteria.
# Assign ATOXDSCL and ATOXDSCH to hold lab grading terms # ATOXDSCL and ATOXDSCH hold terms defined by NCI-CTCAEv4. grade_lookup <- tibble::tribble( ~PARAMCD, ~ATOXDSCL, ~ATOXDSCH, "ALB", "Hypoalbuminemia", NA_character_, "ALKPH", NA_character_, "Alkaline phosphatase increased", "ALT", NA_character_, "Alanine aminotransferase increased", "AST", NA_character_, "Aspartate aminotransferase increased", "BILI", NA_character_, "Blood bilirubin increased", "CA", "Hypocalcemia", "Hypercalcemia", "CHOLES", NA_character_, "Cholesterol high", "CK", NA_character_, "CPK increased", "CREAT", NA_character_, "Creatinine increased", "GGT", NA_character_, "GGT increased", "GLUC", "Hypoglycemia", "Hyperglycemia", "HGB", "Anemia", "Hemoglobin increased", "POTAS", "Hypokalemia", "Hyperkalemia", "LYMPH", "CD4 lymphocytes decreased", NA_character_, "PHOS", "Hypophosphatemia", NA_character_, "PLAT", "Platelet count decreased", NA_character_, "SODIUM", "Hyponatremia", "Hypernatremia", "WBC", "White blood cell decreased", "Leukocytosis", ) adlb <- adlb %>% derive_vars_merged( dataset_add = grade_lookup, by_vars = exprs(PARAMCD), )
It is now straightforward to create the grade, for low lab values the grade will
be held in ATOXGRL and for high lab values the grade will be held in ATOXGRH.
Note: for NCICTCAEv5 grading, you would update meta_criteria parameter to
atoxgr_criteria_ctcv5 and for DAIDS grading you would update meta_criteria
parameter to atoxgr_criteria_daids
adlb <- adlb %>% derive_var_atoxgr_dir( new_var = ATOXGRL, tox_description_var = ATOXDSCL, meta_criteria = atoxgr_criteria_ctcv4, criteria_direction = "L", get_unit_expr = extract_unit(PARAM) ) %>% derive_var_atoxgr_dir( new_var = ATOXGRH, tox_description_var = ATOXDSCH, meta_criteria = atoxgr_criteria_ctcv4, criteria_direction = "H", get_unit_expr = extract_unit(PARAM) )
Note: {admiral} does not grade 'Anemia' or 'Hemoglobin Increased' because the metadata is
based on the SI unit of 'g/L', however the CDISC data has SI unit of 'mmol/L'.
Please see SI_UNIT_CHECK variable in {admiral} metadata atoxgr_criteria_ctcv4() or
atoxgr_criteria_ctcv5() or atoxgr_criteria_daids(), the metadata is in the data folder
of {admiral}.
atoxgr_criteria_ctcv4 %>% filter(!is.na(SI_UNIT_CHECK)) %>% dataset_vignette( display_vars = exprs(TERM, SI_UNIT_CHECK), )
{admiral} also gives the option to combine ATOXGRL and ATOXGRH into one variable,
namely ATOXGR. Grades held in ATOXGRL will be given a negative value in ATOXGR
to distinguish between low and high values.
adlb <- adlb %>% derive_var_atoxgr()
adlb %>% filter((ATOXGRL == "1") | (ATOXGRH == "1")) %>% dataset_vignette( display_vars = exprs(ATOXDSCL, ATOXDSCH, ATOXGRL, ATOXGRH, ATOXGR) )
NCI-CTCAEV4 implementation{#implement_ctcv4}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following CTCAE v4.0 SOC values were identified for grading, these are "Investigations",
"Metabolism and nutrition disorders" and "Blood and lymphatic system disorders".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Investigations" there are 21 CTCAE v4.0 Terms:
- Activated partial thromboplastin time prolonged
- Alanine aminotransferase increased
- Alkaline phosphatase increased
- Aspartate aminotransferase increased
- Blood bilirubin increased
- CD4 lymphocytes decreased
- Cholesterol high
- CPK increased
- Creatinine increased
- Fibrinogen decreased
- GGT increased
- Haptoglobin decreased
- Hemoglobin increased
- INR increased
- Lipase increased
- Lymphocyte count decreased
- Lymphocyte count increased
- Neutrophil count decreased
- Platelet count decreased
- Serum amylase increased
- White blood cell decreased
From the SOC = "Metabolism and nutrition disorders" there are 14 CTCAE v4.0 Terms:
- Hypercalcemia
- Hyperglycemia
- Hyperkalemia
- Hypermagnesemia
- Hypernatremia
- Hypertriglyceridemia
- Hyperuricemia
- Hypoalbuminemia
- Hypocalcemia
- Hypoglycemia
- Hypokalemia
- Hypomagnesemia
- Hyponatremia
- Hypophosphatemia
From the SOC = "Blood and lymphatic system disorders" there are 2 CTCAE v4.0 Terms:
- Anemia
- Leukocytosis
Updates made to TERM
For terms "Hypocalcemia" and "Hypercalcemia" the criteria is provided for Calcium and Ionized Calcium,
therefore {admiral} created a row for each in the metadata, this is noted in the COMMENT variable of
the metadata:
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "calcemia")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Similarly, there is criteria applicable to Fasting Glucose as well as non-Fasting Glucose for "Hyperglycemia" so again this was split into 2 rows, and noted in the COMMENT variable. Note "Hypoglycemia" does not require to be split into 2 rows:
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "glycemia")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Assumptions made when grading
For term "INR Increased" there is the following criteria:
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "INR")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1) )
{admiral} assumed worst case and used both parts of the criteria for grading, so comparing
lab value against ULN and also BASE. The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "INR")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
For TERM "Hyperuricemia", the criteria for Grade 1 and Grade 3 is the same with respect
to the lab value, so worse case is assumed as grade 3. The decision made was
put in the COMMENT field.
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "Hyperuricemia")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT) )
A similar approach was taken for TERM "Hypokalemia" where Grade 1 and Grade 2 criteria
is the same with respect to the lab value, so worse case is assumed as grade 2.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv4 %>% filter(str_detect(TERM, "Hypokalemia")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_2, COMMENT) )
Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v4
atoxgr_criteria_ctcv4 %>% filter(!is.na(COMMENT) & str_detect(TERM, "glycemia", negate = TRUE) & str_detect(TERM, "calcemia", negate = TRUE)) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, Grade_4, COMMENT) )
NCI-CTCAEV5 implementation {#implement_ctcv5}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following CTCAE v5.0 SOC values were identified for grading, these are "Investigations",
"Metabolism and nutrition disorders" and "Blood and lymphatic system disorders".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Investigations" there are 21 CTCAE v5.0 Terms:
- Activated partial thromboplastin time prolonged
- Alanine aminotransferase increased
- Alkaline phosphatase increased
- Aspartate aminotransferase increased
- Blood bilirubin increased
- CD4 lymphocytes decreased
- Cholesterol high
- CPK increased
- Creatinine increased
- Fibrinogen decreased
- GGT increased
- Haptoglobin decreased
- Hemoglobin increased
- INR increased
- Lipase increased
- Lymphocyte count decreased
- Lymphocyte count increased
- Neutrophil count decreased
- Platelet count decreased
- Serum amylase increased
- White blood cell decreased
Note: These are the same terms identified for NCI-CTCAEv4.
From the SOC = "Metabolism and nutrition disorders" there are 12 CTCAE v5.0 Terms:
- Hypercalcemia
- Hyperkalemia
- Hypermagnesemia
- Hypernatremia
- Hypertriglyceridemia
- Hyperuricemia
- Hypoalbuminemia
- Hypocalcemia
- Hypoglycemia
- Hypokalemia
- Hypomagnesemia
- Hyponatremia
Note: These are the same terms identified for NCI-CTCAEv4, except “Hypophosphatemia” and “Hyperglycemia” which are not gradable by quantitative lab values in NCICTCAEv5 grading criteria.
From the SOC = "Blood and lymphatic system disorders" there are 2 CTCAE v5.0 Terms:
- Anemia
- Leukocytosis
Note: These are the same terms identified for NCI-CTCAEv4.
Updates made to TERM
For terms "Hypocalcemia" and "Hypercalcemia" the criteria is provided for Calcium and Ionized Calcium,
therefore {admiral} created a row for each in the metadata, this is noted in the COMMENT variable of
the metadata:
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "calcemia")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Criteria dependent on the Baseline Value
For terms "Alanine aminotransferase increased", "Alkaline phosphatase increased",
"Aspartate aminotransferase increased", "Blood bilirubin increased" and "GGT increased" the criteria
is dependent on the Baseline Value being normal or abnormal. We use the variable BNRIND to determine
this, and users can pass in the value(s) of BNRIND that indicate the baseline is abnormal via the argument
abnormal_indicator in the function derive_var_atoxgr_dir().
For example, setting abnormal_indicator = "HIGH" would mean any observations with BNRIND = "HIGH" would
be deemed to have an abnormal Baseline Value.
Note: you can pass in multiple values, abnormal_indicator = c("H", "HIGH")
For these 5 terms, this means if BNRIND is abnormal then the grade is always zero for the baseline observation.
This is the conservative approach, to ensure you don't see big changes in grade for a patient after they take treatment.
Some companies may not want the baseline observation graded at all, so would post-process, if necessary.
Assumptions made when grading
For term "INR Increased" there is the following criteria:
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "INR")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1) )
{admiral} assumed worst case and used both parts of the criteria for grading, so comparing
lab value against ULN and also BASE. The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "INR")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Similarly, for terms "Lipase Increased" and "Serum amylase increased" there is the following criteria:
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_2, Grade_3, Grade_4) )
{admiral} assumed worst case and implemented highest grade possible.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
For TERM "Hyperuricemia", the criteria for Grade 1 and Grade 3 is the same with respect
to the lab value, so worse case is assumed as grade 3. The decision made was
put in the COMMENT field.
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "Hyperuricemia")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT) )
A similar approach was taken for TERM "Hypokalemia" and "Hyponatremia". For "Hypokalemia",
where Grade 1 and Grade 2 criteria is the same with respect to the lab value, then worse case
is assumed as grade 2. For "Hyponatremia", where Grade 2 and Grade 3 criteria is the same with
respect to the lab value, then worse case is assumed as grade 3.
The decision made was put in the COMMENT field.
atoxgr_criteria_ctcv5 %>% filter(str_detect(TERM, "Hypokalemia") | str_detect(TERM, "Hyponatremia")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, COMMENT) )
Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v5
atoxgr_criteria_ctcv5 %>% filter(!is.na(COMMENT) & str_detect(TERM, "calcemia", negate = TRUE)) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_2, Grade_3, Grade_4, COMMENT) )
DAIDS implementation {#implement_daids}
Terms graded
Grading is implemented for those lab tests where a lab value is included in the grading definition,
{admiral} does NOT try to read any other data to determine the grade, and only the ADLB dataset is used.
The following DAIDS SOC values were identified for grading, these are "Chemistries" and
"Hematology".
From these SOC values the following terms criteria is implemented in {admiral}
From SOC = "Chemistries" there are 31 DAIDS Terms:
- Acidosis
- Albumin, Low
- Alkaline Phosphatase, High
- Alkalosis
- ALT, High
- Amylase, High
- AST, High
- Bicarbonate, Low
- Direct Bilirubin, High
- Total Bilirubin, High
- Calcium, High
- Calcium (Ionized), High
- Calcium, Low
- Calcium (Ionized), Low
- Creatine Kinase, High
- Creatinine, High
- Glucose Fasting, High
- Glucose Nonfasting, High
- Glucose, Low
- Lactate, High
- Lipase, High
- Cholesterol, Fasting, High
- LDL, Fasting, High
- Triglycerides, Fasting, High
- Magnesium, Low
- Phosphate, Low
- Potassium, High
- Potassium, Low
- Sodium, High
- Sodium, Low
- Uric Acid, High
Note: {admiral} does not grade for TERM = "Total Bilirubin, High" when AGE <= 28 days, these criteria are in Appendix of DAIDS Table for Grading the Severity of Adult and Pediatric Adverse Events Corrected Version 2.1.
From the SOC = "Hematology" there are 11 DAIDS Terms:
- Absolute CD4+ Count, Low
- Absolute Lymphocyte Count, Low
- Absolute Neutrophil Count (ANC), Low
- Fibrinogen Decreased
- Hemoglobin, Low
- INR, High
- Methemoglobin
- PTT, High
- Platelets, Decreased
- PT, High
- WBC, Decreased
Terms with age or sex dependent grading criteria
Some terms defined in DAIDS have age or sex dependent grading criteria, {admiral}
handles this in variable FILTER in the metadata. We use {admiral} function
compute_duration to calculate age, see TERM = "Cholesterol, Fasting, High":
atoxgr_criteria_daids %>% filter(str_detect(TERM, "Cholesterol")) %>% dataset_vignette( display_vars = exprs(TERM, FILTER) )
Note: All possible values must be covered for each TERM defined, for TERM = "Absolute Lymphocyte Count, Low" and "Absolute CD4+ Count, Low" there is only grading criteria defined for age > 5 years. Therefore, we add another row with age <= 5 years and set grade to missing. Similarly, for TERM = "LDL, Fasting, High" there is only grading criteria defined for age > 2 years. Therefore, we add another row with age <= 2 years and set grade to missing.
atoxgr_criteria_daids %>% filter(str_detect(COMMENT, "No criteria given")) %>% dataset_vignette( display_vars = exprs(TERM, FILTER, GRADE_CRITERIA_CODE) )
Assumptions made when grading
For terms "INR, High", "PT, High" and "PTT, High", the criteria is based on subjects "not on anticoagulation therapy", this is captured in COMMENT field.
atoxgr_criteria_daids %>% filter(TERM %in% c("INR, High", "PT, High", "PTT, High")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Similarly, for terms "Absolute CD4+ Count, Low" and "Absolute Lymphocyte Count, Low", the criteria is based on subjects "not HIV infected", this is captured in COMMENT field.
atoxgr_criteria_daids %>% filter(str_detect(COMMENT, "HIV infected")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
For term "Acidosis", "Alkalosis" and "Direct Bilirubin, High (> 28 days of age)", {admiral} grades as high as possible, so assumes worst case and subject has "life-threatening consequences". This is captured in COMMENT field.
atoxgr_criteria_daids %>% filter(str_detect(COMMENT, "lifethreatening")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Similarly, for term "Lactate, High", {admiral} only grade 1 and 2, and there is the following criteria:
atoxgr_criteria_daids %>% filter(str_detect(TERM, "Lactate")) %>% dataset_vignette( display_vars = exprs(TERM, Grade_1, Grade_1) )
{admiral} assumed worst case and assume "without acidosis".
The decision made was put in the COMMENT field.
atoxgr_criteria_daids %>% filter(str_detect(TERM, "Lactate")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
For TERM "Direct Bilirubin, High (<= 28 days of age)" and "Uric Acid, High" the criteria is not given in SI unit. The conversion to SI unit is in the comment field.
atoxgr_criteria_daids %>% filter(str_detect(COMMENT, "conver")) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Here is a complete list of all assumptions in the metadata for DAIDs
atoxgr_criteria_daids %>% filter(!is.na(COMMENT) & str_detect(COMMENT, "No criteria given", negate = TRUE) & str_detect(COMMENT, "Did not grade", negate = TRUE)) %>% arrange(TERM) %>% dataset_vignette( display_vars = exprs(TERM, COMMENT) )
Conclusion
With NCI-CTCAEv4, NCI-CTCAEv5 and DAIDS grading now implemented, {admiral} may look to implement other industry standard grading criteria. Providing tools for users to easily interact with the metadata to update criteria, based on their companies needs will also be looked at. Ideally, users should be able to create their own metadata for company specific grading schemes.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.