---
title: "Lab Grading"
output:
  rmarkdown::html_vignette:
  toc: false
vignette: >
  %\VignetteIndexEntry{Lab Grading}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r setup, include = FALSE}
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**](https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm#ctc_40)

The NCI-CTCAEv5 criteria can be found under the heading
[**Common Terminology Criteria for Adverse Events (CTCAE)v5.0**](https://ctep.cancer.gov/protocoldevelopment/electronic_applications/ctc.htm#ctc_50)

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**](https://rsc.niaid.nih.gov/sites/default/files/daidsgradingcorrectedv21.pdf)


# Grading metadata

`{admiral}` will store two sets of metadata data set for each set of grading criteria in the data folder of 
`{admiral}`, one for SI units, and one for US (conventional) units. These are as follows:

NCI-CTCAEv4
  + `atoxgr_criteria_ctcv4` for SI units.
  + `atoxgr_criteria_ctcv4_uscv` for US (conventional) units.

NCI-CTCAEv5
  + `atoxgr_criteria_ctcv5` for SI units.
  + `atoxgr_criteria_ctcv5_uscv` for US (conventional) units.

DAIDS
  + `atoxgr_criteria_daids` for SI units.
  + `atoxgr_criteria_daids_uscv` for US (conventional) units.

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

Each 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".
**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](https://pharmaverse.github.io/blog/posts/2023-10-30_floating_point/floating_point.html) for more context.

# Creating the Lab Grade

## Mapping `ADLB` to the `TERM` variable in the `{admiral}` metadata data set


```{r message=FALSE}
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)
```
```{r echo=FALSE}
lb <- filter(lb, USUBJID %in% c("01-701-1115", "01-705-1186", "01-705-1349", "01-708-1286", "01-707-1037", "01-716-1024"))
```

<br>
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:
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(!is.na(TERM)) %>%
  dataset_vignette(
    display_vars = exprs(TERM)
  )
```
<br>

<br>
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.
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(!is.na(TERM)) %>%
  dataset_vignette(
    display_vars = exprs(TERM)
  )
```
<br>

<br>
Finally, the list of terms defined in the `{admiral}` metadata to implement DAIDS is below:
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(!is.na(TERM)) %>%
  distinct(TERM) %>%
  dataset_vignette(
    display_vars = exprs(TERM)
  )
```
<br>
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.
<br>

```{r, eval=TRUE}
# 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.

```{r, eval=TRUE}
# 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`

```{r, eval=TRUE}
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 `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}`.
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(!is.na(UNIT_CHECK)) %>%
  dataset_vignette(
    display_vars = exprs(TERM, UNIT_CHECK),
  )
```
<br>

`{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.

```{r, eval=TRUE}
adlb <- adlb %>%
  derive_var_atoxgr()
```
<br>

```{r, eval=TRUE, echo=FALSE}
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 16 CTCAE v4.0 Terms:

  + Acidosis
  + Alkalosis
  + 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:
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "calcemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

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:

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "glycemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

## Assumptions made when grading

For term "INR Increased" there is the following criteria:

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "INR")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1)
  )
```
<br>

`{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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "INR")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "Hyperuricemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT)
  )
```
<br>

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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv4 %>%
  filter(str_detect(TERM, "Hypokalemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1, Grade_2, COMMENT)
  )
```
<br>
Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v4
<br>
```{r, eval=TRUE, echo=FALSE}
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)
  )
```
<br>


# 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 14 CTCAE v5.0 Terms:

  + Acidosis
  + Alkalosis
  + 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:
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "calcemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

## 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:

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "INR")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1)
  )
```
<br>

`{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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "INR")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>
Similarly, for terms "Lipase Increased" and "Serum amylase increased" there is the following criteria:

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_2, Grade_3, Grade_4)
  )
```
<br>

`{admiral}` assumed worst case and implemented highest grade possible. 
The decision made was put in the `COMMENT` field.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "Lipase") | str_detect(TERM, "amylase")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_ctcv5 %>%
  filter(str_detect(TERM, "Hyperuricemia")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1, Grade_3, COMMENT)
  )
```
<br>

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.

<br>
```{r, eval=TRUE, echo=FALSE}
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)
  )
```
<br>

Here is a complete list of all assumptions in the metadata for NCI-CTCAE-v5
<br>
```{r, eval=TRUE, echo=FALSE}
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)
  )
```
<br>


# 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**](https://rsc.niaid.nih.gov/sites/default/files/daidsgradingcorrectedv21.pdf).

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":
<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(TERM, "Cholesterol")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, FILTER)
  )
```
<br>

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.

```{r, eval=TRUE, echo=FALSE}
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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(TERM %in% c("INR, High", "PT, High", "PTT, High")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(COMMENT, "HIV infected")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>


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.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(COMMENT, "lifethreatening")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>
Similarly, for term "Lactate, High", {admiral} only grade 1 and 2, and there is the following criteria:

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(TERM, "Lactate")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, Grade_1, Grade_1)
  )
```
<br>

`{admiral}` assumed worst case and assume "without acidosis". 
The decision made was put in the `COMMENT` field.

<br>
```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(TERM, "Lactate")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>

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.

<br>

```{r, eval=TRUE, echo=FALSE}
atoxgr_criteria_daids %>%
  filter(str_detect(COMMENT, "conver")) %>%
  dataset_vignette(
    display_vars = exprs(TERM, COMMENT)
  )
```
<br>
Here is a complete list of all assumptions in the metadata for DAIDs
<br>
```{r, eval=TRUE, echo=FALSE}
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)
  )
```
<br>

# US (Conventional) unit implementation

The metadata for the US (Conventional) units is the same as the metadata for SI units
except when checking for units, when the criteria is based on numeric values. The `UNIT_CHECK`
variable will then contain the US (Conventional) unit, and if the unit is not a synonym for the
SI unit then the grading criteria is updated accordingly.
There were some lab tests where it made sense to have 2 sets of US (Conventional) units, these
were lab tests related to `"WBC"`, `"Lymphocytes"`, `"Neutrophils"` and `"Platelets"`. We have the US unit
`"10^3/uL"` which is a synonym of the SI unit `"10^9/L"`, and also the US unit `"10^3/ml"` which is a synonym
for `"/mm3"` which is included in the grading criteria documentation.
If companies have lab tests that have units that don't exactly match what is in the `UNIT_CHECK` variable, but
the unit is a one-to-one mapping, then the `UNIT_CHECK` variable can be updated with company-specific code to
reflect this.


# 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.
