medicalcoder

http://www.peteredewitt.com/medicalcoder/
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medicalcoder is a lightweight, dependency-free R package for ICD-based comorbidity assessment that...
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### Load Libraries and Get Package Info

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-multimorbidity.html

Loads necessary R packages and retrieves version and description information for the multimorbidity package. This is a setup step for comparing the packages.

```r
library(data.table)
library(medicalcoder)
library(multimorbidity)
packageVersion("multimorbidity")
packageDescription("multimorbidity")$Title
cat(packageDescription("multimorbidity")$Description)
```

--------------------------------

### Install medicalcoder from Source (Command Line)

Source: http://www.peteredewitt.com/medicalcoder/index.html

Install the medicalcoder package from a local source file (.tar.gz) using the R CMD INSTALL command in your terminal.

```bash
R CMD INSTALL medicalcoder_X.Y.Z.tar.gz
```

--------------------------------

### Install medicalcoder from Source (.tar.gz)

Source: http://www.peteredewitt.com/medicalcoder/index.html

Install the medicalcoder package from a local source file (.tar.gz). This method is useful if you have downloaded the package source or are installing a specific version.

```R
install.packages(pkgs = "medicalcoder_X.Y.Z.tar.gz", repos = NULL, type = "source")
```

--------------------------------

### Get PCCC Conditions

Source: http://www.peteredewitt.com/medicalcoder/reference/get_pccc_conditions.html

This example shows how to call the get_pccc_conditions() function and displays the resulting data frame, which maps conditions to subconditions and provides condition labels.

```R
get_pccc_conditions()
#>          condition                                subcondition
#> 1       malignancy                                   neoplasms
#> 2              cvd                   device_and_technology_use
#> 3             misc                             transplantation
#> 4        neuromusc                  other_neurologic_disorders
#> 5        neuromusc                   device_and_technology_use
#> 6        metabolic                         endocrine_disorders
#> 7               gi                                       other
#> 8      respiratory                                       other
#> 9      respiratory                   device_and_technology_use
#> 10     respiratory                             transplantation
#> 11             cvd        heart_and_great_vessel_malformations
#> 12             cvd                             transplantation
#> 13           renal                   device_and_technology_use
#> 14     hemato_immu                             transplantation
#> 15     hemato_immu                                       other
#> 16              gi                   device_and_technology_use
#> 17              gi                             transplantation
#> 18           renal                                       other
#> 19           renal                             transplantation
#> 20            misc                   device_and_technology_use
#> 21       metabolic                   device_and_technology_use
#> 22     hemato_immu                   acquired_immunodeficiency
#> 23     hemato_immu                                 sarcoidosis
#> 24 congeni_genetic                    bone_and_joint_anomalies
#> 25       metabolic                       amino_acid_metabolism
#> 26       metabolic                     carbohydrate_metabolism
#> 27       metabolic                            lipid_metabolism
#> 28       metabolic                   other_metabolic_disorders
#> 29     respiratory                             cystic_fibrosis
#> 30       metabolic                           storage_disorders
#> 31     hemato_immu                 hereditary_immunodeficiency
#> 32     hemato_immu                          hereditary_anemias
#> 33     hemato_immu                            aplastic_anemias
#> 34     hemato_immu                     coagulation_hemorrhagic
#> 35     hemato_immu                                  leukopenia
#> 36     hemato_immu                    hemophagocytic_syndromes
#> 37       neuromusc                   intellectual_disabilities
#> 38     respiratory                chronic_respiratory_diseases
#> 39       neuromusc               cns_degeneration_and_diseases
#> 40       neuromusc                           movement_diseases
#> 41       neuromusc                    infantile_cerebral_palsy
#> 42           renal                    chronic_bladder_diseases
#> 43       neuromusc                                    epilepsy
#> 44       neuromusc         muscular_dystrophies_and_myopathies
#> 45             cvd                                       other
#> 46             cvd                        endocardium_diseases
#> 47             cvd                            cardiomyopathies
#> 48             cvd                         conduction_disorder
#> 49             cvd                                dysrhythmias
#> 50       neuromusc              occlusion_of_cerebral_arteries
#> 51     hemato_immu polyarteritis_nodosa_and_related_conditions
#> 52 congeni_genetic      diaphragm_and_abdominal_wall_anomalies
#> 53              gi                  inflammatory_bowel_disease
#> 54              gi         chronic_liver_disease_and_cirrhosis
#> 55           renal                       chronic_renal_failure
#> 56     hemato_immu       diffuse_diseases_of_connective_tissue
#> 57       neuromusc         brain_and_spinal_cord_malformations
#> 58     respiratory                   respiratory_malformations
#> 59              gi                        congenital_anomalies
#> 60           renal                        congenital_anomalies
#> 61 congeni_genetic                       chromosomal_anomalies
#> 62 congeni_genetic                  other_congenital_anomalies
#> 63        neonatal                          fetal_malnutrition
#> 64        neonatal                          extreme_immaturity
#> 65        neonatal                cerebral_hemorrhage_at_birth
#> 66        neonatal                 spinal_cord_injury_at_birth
#> 67        neonatal                              birth_asphyxia
#> 68        neonatal             hypoxic_ischemic_encephalopathy
#> 69        neonatal                        respiratory_diseases
#> 70        neonatal                                       other
#> 71      malignancy                             transplantation
#>                            condition_label
#> 1                               Malignancy
#> 2                           Cardiovascular
#> 3  Miscellaneous, Not Elsewhere Classified
```

--------------------------------

### Install medicalcoder from GitHub

Source: http://www.peteredewitt.com/medicalcoder/index.html

Install the development version of the medicalcoder package directly from its GitHub repository using the remotes package.

```R
remotes::install_github("dewittpe/medicalcoder")
```

--------------------------------

### Load and Inspect icdcomorbid Package

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-icdcomorbid.html

Loads the icdcomorbid package and displays its version, title, and description. Ensure the package is installed before running.

```R
library(medicalcoder)
library(icdcomorbid)
packageVersion("icdcomorbid")
## [1] '1.0.0'
packageDescription("icdcomorbid")$Title
## [1] "Mapping ICD Codes to Comorbidity"
cat(packageDescription("icdcomorbid")$Description)
## 
##     Provides tools for mapping International Classification of Diseases codes to comorbidity, 
##     enabling the identification and analysis of various medical conditions within healthcare data.

```

--------------------------------

### Load and Inspect R Packages

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-comorbidity.html

Loads the 'medicalcoder' and 'comorbidity' R packages and displays their versions and descriptions. Ensure these packages are installed before running.

```R
library(medicalcoder)
library(comorbidity)
packageVersion("comorbidity")
## [1] '1.1.0'
packageDescription("comorbidity")$Title
## [1] "Computing Comorbidity Scores"
cat(packageDescription("comorbidity")$Description)
## Computing comorbidity indices and scores such as the weighted Charlson
##   score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
##   comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
##   using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
##   Australian and Swedish modifications of the Charlson Comorbidity Index are available
##   as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 
##   <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both
##   the Charlson and Elixhauser comorbidity scores.
```

--------------------------------

### Prepare Data for Benchmarking with data.table

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-comorbidity.html

Copies and converts input data frames to data.tables for performance testing. Ensure the data.table package is installed and loaded.

```R
mdcrDT <- data.table::copy(mdcr)
data.table::setDT(mdcrDT)
mdcr_icd9dxDT <- data.table::copy(mdcr_icd9dx)
mdcr_icd10dxDT <- data.table::copy(mdcr_icd10dx)
data.table::setDT(mdcr_icd9dxDT)
data.table::setDT(mdcr_icd10dxDT)
```

--------------------------------

### Load medicalcoder and check version

Source: http://www.peteredewitt.com/medicalcoder/articles/charlson.html

Load the medicalcoder package and display its version. Ensure you have the correct version installed.

```r
library(medicalcoder)
packageVersion("medicalcoder")
```

--------------------------------

### Open Elixhauser Vignette

Source: http://www.peteredewitt.com/medicalcoder/index.html

Access the vignette for detailed examples and information on Elixhauser comorbidity calculations.

```R
vignette(topic = "elixhauser", package = "medicalcoder")
```

--------------------------------

### Load Example Datasets in medicalcoder

Source: http://www.peteredewitt.com/medicalcoder/index.html

Loads the `mdcr` and `mdcr_longitudinal` datasets into the R environment. These datasets are used for demonstrating the package's functionality.

```r
data(mdcr, mdcr_longitudinal, package = "medicalcoder")
```

--------------------------------

### Loading and Checking Package Versions

Source: http://www.peteredewitt.com/medicalcoder/articles/transition-pccc-to-medicalcoder.html

Loads the pccc and medicalcoder R packages and displays their current versions. This is a common setup step before using the functions from these packages.

```R
library(pccc)
packageVersion("pccc")
## [1] '1.0.7'
library(medicalcoder)
```

--------------------------------

### Install medicalcoder from CRAN

Source: http://www.peteredewitt.com/medicalcoder/index.html

Use this command to install the latest stable version of the medicalcoder package from the Comprehensive R Archive Network (CRAN).

```R
install.packages("medicalcoder")
```

--------------------------------

### Example of Identical Comorbidity Comparisons

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-comorbidity.html

Illustrates the output of the comparison loop, showing `identical()` calls and their `TRUE` results for various comorbidity pairs between the 'comorbidity' and 'medicalcoder' packages.

```r
## identical(elixhauser_delta[["aids"]], elixhauser_delta[["AIDS"]])
## [1] TRUE
## identical(elixhauser_delta[["alcohol"]], elixhauser_delta[["ALCOHOL"]])
## [1] TRUE
## identical(elixhauser_delta[["blane"]], elixhauser_delta[["BLDLOSS"]])
## [1] TRUE
## identical(elixhauser_delta[["dane"]], elixhauser_delta[["ANEMDEF"]])
## [1] TRUE
## identical(elixhauser_delta[["carit"]], elixhauser_delta[["CARDIAC_ARRHYTHMIAS"]])
## [1] TRUE
## identical(elixhauser_delta[["solidtum"]], elixhauser_delta[["TUMOR"]])
## [1] TRUE
## identical(elixhauser_delta[["metacanc"]], elixhauser_delta[["METS"]])
## [1] TRUE
## identical(elixhauser_delta[["cpd"]], elixhauser_delta[["CHRNLUNG"]])
## [1] TRUE
## identical(elixhauser_delta[["coag"]], elixhauser_delta[["COAG"]])
## [1] TRUE
## identical(elixhauser_delta[["chf"]], elixhauser_delta[["CHF"]])
## [1] TRUE
## identical(elixhauser_delta[["depre"]], elixhauser_delta[["DEPRESS"]])
## [1] TRUE
## identical(elixhauser_delta[["diabc"]], elixhauser_delta[["DMCX"]])
## [1] TRUE
## identical(elixhauser_delta[["diabunc"]], elixhauser_delta[["DM"]])
## [1] TRUE
## identical(elixhauser_delta[["drug"]], elixhauser_delta[["DRUG"]])
## [1] TRUE
## identical(elixhauser_delta[["fed"]], elixhauser_delta[["LYTES"]])
## [1] TRUE
## identical(elixhauser_delta[["hypc"]], elixhauser_delta[["HTN_CX"]])
## [1] TRUE
## identical(elixhauser_delta[["hypunc"]], elixhauser_delta[["HTN_UNCX"]])
## [1] TRUE
## identical(elixhauser_delta[["hypothy"]], elixhauser_delta[["HYPOTHY"]])
## [1] TRUE
## identical(elixhauser_delta[["ld"]], elixhauser_delta[["LIVER"]])
## [1] TRUE
## identical(elixhauser_delta[["lymph"]], elixhauser_delta[["LYMPH"]])
## [1] TRUE
## identical(elixhauser_delta[["obes"]], elixhauser_delta[["OBESE"]])
## [1] TRUE
## identical(elixhauser_delta[["ond"]], elixhauser_delta[["NEURO"]])
## [1] TRUE
## identical(elixhauser_delta[["para"]], elixhauser_delta[["PARA"]])
## [1] TRUE
## identical(elixhauser_delta[["pud"]], elixhauser_delta[["ULCER"]])
## [1] TRUE
## identical(elixhauser_delta[["pvd"]], elixhauser_delta[["PERIVASC"]])
## [1] TRUE
## identical(elixhauser_delta[["psycho"]], elixhauser_delta[["PSYCH"]])
## [1] TRUE
## identical(elixhauser_delta[["pcd"]], elixhauser_delta[["PULMCIRC"]])
## [1] TRUE
## identical(elixhauser_delta[["rf"]], elixhauser_delta[["RENLFAIL"]])
## [1] TRUE
## identical(elixhauser_delta[["rheumd"]], elixhauser_delta[["ARTH"]])
## [1] TRUE
## identical(elixhauser_delta[["valv"]], elixhauser_delta[["VALVE"]])
## [1] TRUE
## identical(elixhauser_delta[["wloss"]], elixhauser_delta[["WGHTLOSS"]])
## [1] TRUE

```

--------------------------------

### Comorbidity Flagging with DX Variable

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

This example includes the `dx.var` argument, useful when your data distinguishes between diagnostic and procedure codes. It helps refine comorbidity flagging.

```R
comorbidities(
  data = DF,
  id.vars = "id",
  icd.codes = "code",
  dx.var = "dx",
  poa = 1,
  full.code = FALSE,
  compact.codes = TRUE,
  method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
```

--------------------------------

### pccc Package Data Format Example

Source: http://www.peteredewitt.com/medicalcoder/articles/transition-pccc-to-medicalcoder.html

Illustrates the data structure expected by the pccc::ccc() function, where each row represents a patient/encounter and columns denote diagnostic and procedure codes.

```R
patid    dx1    dx2    dx3    dx4 dx5 dx6     pr1     pr2     pr3     pr4 pr5
  patXX  T8619  E8809   E876  Z7982  NA  NA 02PAX3Z 5A1D70Z 04Q90ZZ 0TS60ZZ  NA
```

--------------------------------

### Open Charlson Vignette

Source: http://www.peteredewitt.com/medicalcoder/index.html

Access the vignette for detailed examples and information on Charlson comorbidity calculations.

```R
vignette(topic = "charlson", package = "medicalcoder")
```

--------------------------------

### Comorbidity Flagging with Full Codes Only

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

This example demonstrates flagging comorbidities when `full.code` is TRUE and `compact.codes` is FALSE, useful for ensuring only full ICD codes are considered.

```R
comorbidities(
  data = DF,
  id.vars = "id",
  icd.codes = "code",
  poa = 1,
  full.code = TRUE,
  compact.codes = FALSE,
  method = "pccc_v3.1"
)[, c("id", "cmrb_flag", "renal_dxpr_or_tech")]
```

--------------------------------

### Open Comorbidities Vignette

Source: http://www.peteredewitt.com/medicalcoder/index.html

Opens the main vignette for the `comorbidities` function, providing general examples and explanations for how conditions are flagged.

```r
vignette(topic = "comorbidities", package = "medicalcoder")
```

--------------------------------

### View First Few Rows of PCCC Codes

Source: http://www.peteredewitt.com/medicalcoder/reference/get_pccc_codes.html

Display the first few rows of the PCCC codes data frame to get a quick overview of the data structure and content.

```R
head(get_pccc_codes())
```

--------------------------------

### Get ICD Codes with Descriptions

Source: http://www.peteredewitt.com/medicalcoder/articles/icd.html

Retrieves the ICD code lookup table including descriptions for each code by setting the 'with.descriptions' argument to TRUE. This provides more detailed information about each ICD code.

```R
str(get_icd_codes(with.descriptions = TRUE))
```

--------------------------------

### Lookup ICD Codes (Compact Codes Only)

Source: http://www.peteredewitt.com/medicalcoder/articles/icd.html

This example shows how to restrict the lookup_icd_codes function to only consider compact ICD codes by setting `full.codes = FALSE`. The output is formatted using knitr::kable.

```r
knitr::kable(
  lookup_icd_codes(codes, full.codes = FALSE),
  row.names = FALSE
)
```

--------------------------------

### Comorbidity Flagging with ICD Version Variable

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

This example uses `icdv.var = "icdv"` to dynamically determine the ICD version from a column in the data. It's a flexible approach for handling mixed ICD versions.

```R
comorbidities(
  data = subset(mdcr, patid == "95471"),
  icd.codes = "code",
  id.vars = 'patid',
  dx.var = "dx",
  icdv.var = "icdv",
  poa = 1,
  method = "pccc_v3.0"
)[, c('patid', 'cmrb_flag')]
```

--------------------------------

### Get ICD Codes Function Signature

Source: http://www.peteredewitt.com/medicalcoder/reference/get_icd_codes.html

This is the basic function signature for retrieving ICD codes. Use `with.descriptions = TRUE` to include code descriptions or `with.hierarchy = TRUE` to include the ICD hierarchy.

```R
get_icd_codes(with.descriptions = FALSE, with.hierarchy = FALSE)
```

--------------------------------

### Apply PCCC Methods to MDCR Data

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

Use the `comorbidities` function to flag conditions in the `mdcr` dataset. This example applies both `pccc_v2.1` and `pccc_v3.1` methods, specifying 'code' for ICD codes and 'patid' as the patient identifier. The `poa` argument is set to 1, and `flag.method` is set to 'current'.

```r
mdcr_results_v2.1_01 <-
  comorbidities(
    data = mdcr,
    icd.codes = "code",
    id.vars = "patid",
    poa = 1,
    flag.method = 'current',
    method = "pccc_v2.1"
  )

mdcr_results_v3.1_01 <-
  comorbidities(
    data = mdcr,
    icd.codes = "code",
    id.vars = "patid",
    poa = 1,
    flag.method = 'current',
    method = "pccc_v3.1"
  )
```

--------------------------------

### medicalcoder Package Data Format Example

Source: http://www.peteredewitt.com/medicalcoder/articles/transition-pccc-to-medicalcoder.html

Shows the data structure required by the medicalcoder::comorbidities() function, where each row represents a single ICD code for a patient/encounter, with a column indicating if it's a diagnostic (dx=1) or procedure (dx=0) code.

```R
  patid    code  dx
  patXX   T8619   1
  patXX   E8809   1
  patXX    E876   1
  patXX   Z7982   1
  patXX 02PAX3Z   0
  patXX 5A1D70Z   0
  patXX 04Q90ZZ   0
  patXX 0TS60ZZ   0
```

--------------------------------

### Run MIMIC-IV Charlson Query Locally

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-mimic.html

Connects to an in-memory SQLite database, writes prepared data, executes the modified MIMIC-IV Charlson SQL query, and retrieves the results.

```r
library(odbc)
library(DBI)
library(RSQLite)

con <- dbConnect(drv = RSQLite::SQLite(), dbname = ":memory:")

# add data to the data base
dbWriteTable(conn = con, name = "diagnoses",  value = mdcr_for_mimic[dx == 1L])
dbWriteTable(conn = con, name = "admissions", value = mdcr_for_mimic[, unique(.SD), .SDcols = c("subject_id", "hadm_id")])
dbWriteTable(conn = con, name = "ages",       value = mdcr_for_mimic[, unique(.SD), .SDcols = c("hadm_id", "age")])

# get the charlson results via MIMIC-IV
mimic_charlson_results <- dbGetQuery(con, mimic_charson_query)

# close DB connection
dbDisconnect(conn = con)

setDT(mimic_charlson_results)
```

--------------------------------

### Prepare Data for MIMIC Comparison

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-mimic.html

Prepares the `mdcr` dataset for comparison with MIMIC-IV by renaming columns and creating new identifiers and sequence numbers.

```r
library(medicalcoder)
library(data.table)
mdcr_for_mimic <- data.table::copy(mdcr)
setDT(mdcr_for_mimic)

# create a hospital admission id  (one admission per patient id in mdcr)
setnames(
  mdcr_for_mimic,
  old = c("patid", "code", "icdv"),
  new = c("subject_id", "icd_code", "icd_version")
)

mdcr_for_mimic[, hadm_id := paste0(subject_id, "e1")]
mdcr_for_mimic[, seq_num := 1L:.N, by = .(subject_id, hadm_id)]
mdcr_for_mimic[, age := as.integer(substr(as.character(subject_id), 1L, 2L))]
```

--------------------------------

### Allow Header Codes and Specify Year

Source: http://www.peteredewitt.com/medicalcoder/reference/is_icd.html

Shows how to allow header codes and specify a particular year for validation. This is useful for checking codes that might not be directly assignable but are valid headers.

```R
is_icd(x, icdv = 9, dx = 1, headerok = TRUE)
#> [1] TRUE TRUE TRUE
is_icd(x, icdv = 9, dx = 1, year = 2006)
#> [1] TRUE TRUE TRUE
```

--------------------------------

### Get and Inspect ICD Codes

Source: http://www.peteredewitt.com/medicalcoder/reference/get_icd_codes.html

Retrieve all available ICD codes and inspect their structure. This is useful for understanding the dataset.

```r
icd_codes <- get_icd_codes()
str(icd_codes)
#> 'data.frame':	249819 obs. of  9 variables:
#>  $ icdv            : int  9 9 9 9 9 9 9 9 9 9 ...
#>  $ dx              : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ full_code       : chr  "00" "00" "00.0" "00.0" ...
#>  $ code            : chr  "00" "00" "000" "000" ...
#>  $ src             : chr  "cms" "cdc" "cms" "cdc" ...
#>  $ known_start     : int  2006 2003 2006 2003 2006 2003 2006 2003 2006 2003 ...
#>  $ known_end       : int  2015 2012 2015 2012 2015 2012 2015 2012 2015 2012 ...
#>  $ assignable_start: int  NA NA NA NA 2006 2003 2006 2003 2006 2003 ...
#>  $ assignable_end  : int  NA NA NA NA 2015 2012 2015 2012 2015 2012 2015 2012 ...
```

--------------------------------

### Open ICD Vignette

Source: http://www.peteredewitt.com/medicalcoder/index.html

Opens the vignette for the 'icd' topic in the medicalcoder package, providing more details and examples on working with ICD codes.

```r
vignette(topic = "icd", package = "medicalcoder")
```

--------------------------------

### Download and Modify MIMIC-IV Charlson SQL Query

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-mimic.html

Downloads the Charlson SQL query from GitHub and modifies it to be compatible with RSQLite.

```r
mimic_charson_query <-
  scan(
    file = "https://raw.githubusercontent.com/MIT-LCP/mimic-code/278df75ec30991ff3a6f5ceb6d2221635a085e9f/mimic-iv/concepts/comorbidity/charlson.sql",
    what = character(),
    sep = "\n"
  )

# modify the query to work in SQLite
mimic_charson_query <-
  gsub(pattern = "physionet-data.mimiciv_hosp.admissions",
       replacement = "admissions",
       x = mimic_charson_query,
       fixed = TRUE)
mimic_charson_query <-
  gsub(pattern = "physionet-data.mimiciv_hosp.diagnoses_icd",
       replacement = "diagnoses",
       x = mimic_charson_query,
       fixed = TRUE)
mimic_charson_query <-
  gsub(pattern = "physionet-data.mimiciv_derived.age",
       replacement = "ages",
       x = mimic_charson_query,
       fixed = TRUE)
mimic_charson_query <-
  gsub(pattern = "GREATEST",
       replacement = "MAX",
       x = mimic_charson_query,
       fixed = TRUE)

mimic_charson_query <- paste(mimic_charson_query, collapse = "\n")
```

--------------------------------

### Open PCCC Vignette

Source: http://www.peteredewitt.com/medicalcoder/index.html

Opens the vignette specific to the Pediatric Complex Chronic Conditions (PCCC) algorithm, offering further details and examples.

```r
vignette(topic = "pccc", package = "medicalcoder")
```

--------------------------------

### Get Elixhauser Codes Function

Source: http://www.peteredewitt.com/medicalcoder/reference/get_elixhauser_codes.html

Retrieves the internal lookup tables for Elixhauser comorbidity ICD codes. This function does not require any arguments.

```R
get_elixhauser_codes()
```

--------------------------------

### Format Condition Summaries with kableExtra

Source: http://www.peteredewitt.com/medicalcoder/articles/transition-pccc-to-medicalcoder.html

Use this code to subset and format summary data for specific conditions using kableExtra. Ensure the 'with_subconditions' data is available and properly structured.

```r
cvd_and_metabolic <- subset(summary(with_subconditions), condition %in% c("cvd", "metabolic"))
cvd_and_metabolic$subcondition[is.na(cvd_and_metabolic$subcondition)] <- "Any subcondition"

kableExtra::kbl(
  x = cvd_and_metabolic[, c("subcondition", "count", "percent_of_cohort", "percent_of_those_with_condition")],
  caption = "Patients with cardiovascular and/or metabolic conditions and the associated with_subconditions.",
  row.names = FALSE,
  digits = 2,
  col.names = c("Subcondition", "Patients", "% of chort", "% of those with the primary condition")
) |> 
kableExtra::kable_styling(bootstrap_options = "striped") |> 
kableExtra::pack_rows(index = table(cvd_and_metabolic$condition))
```

--------------------------------

### Get Elixhauser POA Requirements

Source: http://www.peteredewitt.com/medicalcoder/reference/get_elixhauser_poa.html

Retrieves the internal lookup table for Elixhauser comorbidities and their present-on-admission requirements. This function requires no arguments.

```R
get_elixhauser_poa()
```

--------------------------------

### View First Few Elixhauser Index Scores

Source: http://www.peteredewitt.com/medicalcoder/reference/get_elixhauser_index_scores.html

Use `head()` with `get_elixhauser_index_scores()` to display the initial rows of the Elixhauser index scores data frame, showing condition, index type, and scores for different variants.

```R
head(get_elixhauser_index_scores())
```

--------------------------------

### Get PCCC Conditions

Source: http://www.peteredewitt.com/medicalcoder/reference/get_pccc_conditions.html

Retrieves a data frame containing syntax-valid and human-readable labels for Pediatric Complex Chronic Conditions (PCCC) and their subconditions.

```APIDOC
## get_pccc_conditions()

### Description
Retrieve a copy of internal lookup tables for the syntax valid and human readable labels of the Pediatric Complex Chronic Conditions (PCCC) conditions and subconditions.

### Usage
```R
get_pccc_conditions()
```

### Value
a `data.frame` with the following columns
  * `condition`: (character) syntax valid name for the condition
  * `subcondition`: (character) syntax valid name for the subcondition
  * `condition_label`: (character) human readable label for the condition
  * `subcondition_label`: (character) human readable label for the subcondition

### See also
  * `get_pccc_codes()` for the lookup table of ICD codes used for the PCCC.
  * `comorbidities()` for applying comorbidity algorithms to a data set.
```

--------------------------------

### Display First Few Rows of Elixhauser Codes

Source: http://www.peteredewitt.com/medicalcoder/reference/get_elixhauser_codes.html

Shows the first few rows of the Elixhauser codes lookup table to give an idea of the data structure and content. Useful for a quick inspection.

```R
head(get_elixhauser_codes())
```

--------------------------------

### Get ICD Codes Table

Source: http://www.peteredewitt.com/medicalcoder/index.html

Retrieves a comprehensive table of ICD codes. Use this to inspect the structure and content of the available ICD data.

```r
str(medicalcoder::get_icd_codes())
```

--------------------------------

### List Available Comorbidity Methods

Source: http://www.peteredewitt.com/medicalcoder/articles/comorbidities.html

Retrieve a list of all supported comorbidity calculation methods within the package. This helps in selecting the appropriate algorithm and version for your analysis.

```r
medicalcoder:::comorbidities_methods()
##  [1] "pccc_v2.0"                 "pccc_v2.1"                
##  [3] "pccc_v3.0"                 "pccc_v3.1"                
##  [5] "charlson_deyo1992"         "charlson_quan2011"        
##  [7] "charlson_quan2005"         "charlson_cdmf2019"        
##  [9] "elixhauser_elixhauser1988" "elixhauser_ahrq_web"      
## [11] "elixhauser_quan2005"       "elixhauser_ahrq2022"      
## [13] "elixhauser_ahrq2023"       "elixhauser_ahrq2024"      
## [15] "elixhauser_ahrq2025"       "elixhauser_ahrq2026"      
## [17] "elixhauser_ahrq_icd10"
```

--------------------------------

### Compare Dimensions and Column Names

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-comorbidity.html

Examine the dimensions and column names of the results from both packages to identify differences in the output structure.

```R
dim(medicalcoder_elixhauser_results)
## [1] 38262    37
names(medicalcoder_elixhauser_results)
##  [1] "patid"               "AIDS"                "LIVER"              
##  [4] "PERIVASC"            "VALVE"               "TUMOR"              
##  [7] "METS"                "LYMPH"               "HYPOTHY"            
## [10] "DM"                  "DMCX"                "LYTES"              
## [13] "WGHTLOSS"            "ALCOHOL"             "OBESE"              
## [16] "BLDLOSS"             "ANEMDEF"             "COAG"               
## [19] "DRUG"                "PSYCH"               "DEPRESS"            
## [22] "NEURO"               "PARA"                "CHF"                
## [25] "HTN_UNCX"            "HTN_CX"              "RENLFAIL"           
## [28] "PULMCIRC"            "CHRNLUNG"            "CARDIAC_ARRHYTHMIAS"
## [31] "ARTH"                "ULCER"               "HTN_C"              
## [34] "num_cmrb"            "cmrb_flag"           "mortality_index"    
## [37] "readmission_index"

dim(comorbidity_elixhauser_results)
## [1] 38262    32
names(comorbidity_elixhauser_results)
##  [1] "patid"    "chf"      "carit"    "valv"     "pcd"      "pvd"     
##  [7] "hypunc"   "hypc"     "para"     "ond"      "cpd"      "diabunc" 
## [13] "diabc"    "hypothy"  "rf"       "ld"       "pud"      "aids"    
## [19] "lymph"    "metacanc" "solidtum" "rheumd"   "coag"     "obes"    
## [25] "wloss"    "fed"      "blane"    "dane"     "alcohol"  "drug"    
## [31] "psycho"   "depre"

```

--------------------------------

### Create Patient Data Frame

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

Prepare a data frame with ICD codes for a patient. Ensure `dx` is 1 for diagnostic and 0 for procedure codes, and `icdv` specifies the ICD version.

```r
pat1 <-
  data.frame(
    dx = c(1, 1, 1, 1, 0, 0),
    icdv = 9L,
    code = c("34590", "78065", "3432", "78065", "9929", "8606")
  )
```

--------------------------------

### Access Comorbidities Function Documentation

Source: http://www.peteredewitt.com/medicalcoder/articles/comorbidities.html

Display the help manual for the `comorbidities` function. This provides detailed information on its arguments, usage, and return values.

```r
help(topic = "comorbidities", package = "medicalcoder")
```

--------------------------------

### Get Elixhauser POA Requirements

Source: http://www.peteredewitt.com/medicalcoder/reference/get_elixhauser_poa.html

Retrieves a data frame containing information about Elixhauser comorbidities, including whether a diagnosis needs to be present-on-admission (POA) to be flagged.

```APIDOC
## get_elixhauser_poa()

### Description
Retrieves a copy of internal lookup table with details on which Elixhauser comorbidities do and do not require the associated ICD codes to be present-on-admission to be flagged.

### Usage
```R
get_elixhauser_poa()
```

### Value
A `data.frame` with the following columns:
  * `condition`: Character vector of the conditions
  * `desc`: Character vector with a verbose description of the condition
  * `poa_required`: Integer indicators if the code needs to present on admission to be considered a comorbidity
  * `elixhauser_<variant>`: indicators for the Elixhauser `<variant>`

### See also
  * `get_elixhauser_index_scores()` for the lookup table of the condition by condition scores for mortality and readmission indices.
  * `get_elixhauser_codes()` for the lookup table of ICD codes mapping to the Elixhauser comorbidities.
  * `comorbidities()` for applying comorbidity algorithms to a data set.

### Examples
```R
head(get_elixhauser_poa())
str(get_elixhauser_poa())
```
```

--------------------------------

### Apply Elixhauser Method with Warning

Source: http://www.peteredewitt.com/medicalcoder/articles/elixhauser.html

This code applies the Elixhauser method without specifying primary diagnosis information, which will generate a warning. Pass `primarydx = 0` or `primarydx.var` to suppress this warning.

```r
# will warn because primarydx and primarydx.var are both NULL
mdcr_results0 <-
  comorbidities(
    data = mdcr,
    id.vars = "patid",
    icdv.var = "icdv",
    icd.codes = "code",
    dx.var = "dx",
    flag.method = "current",
    poa = 1,
    method = "elixhauser_ahrq2025"
  )
## Warning: Assuming all codes provided are secondary diagnostic codes.  Define
## `primarydx.var` or `primarydx` if this assumption is incorrect.
```

--------------------------------

### Get PCCC Codes by ICD-10

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

Retrieves PCCC codes matching specific ICD-10 diagnostic codes. Useful for understanding code details and technology dependency flags.

```r
codes <- c("H49.811", "J84.111", "Z96.41")
subset(get_pccc_codes(), full_code %in% codes)
##      icdv dx full_code   code   condition                 subcondition
## 6438   10  1   H49.811 H49811   metabolic    other_metabolic_disorders
## 6705   10  1   J84.111 J84111 respiratory chronic_respiratory_diseases
## 7898   10  1    Z96.41  Z9641   metabolic    device_and_technology_use
##      transplant_flag tech_dep_flag pccc_v3.1 pccc_v3.0 pccc_v2.1 pccc_v2.0
## 6438               0             0         1         1         1         1
## 6705               0             0         1         1         0         0
## 7898               0             1         1         1         1         1
```

--------------------------------

### Inspect First Few Rows of Charlson Codes

Source: http://www.peteredewitt.com/medicalcoder/reference/get_charlson_codes.html

Use `head()` to display the first few rows of the data frame returned by `get_charlson_codes()`. This is useful for a quick overview of the data structure and content.

```r
head(get_charlson_codes())
```

--------------------------------

### Get Charlson Index Scores

Source: http://www.peteredewitt.com/medicalcoder/articles/charlson.html

Retrieve the lookup table for Charlson index scores associated with specific conditions. This data.frame includes scores for different algorithm variants.

```r
str(get_charlson_index_scores())
```

--------------------------------

### Calculate Elixhauser Comorbidities

Source: http://www.peteredewitt.com/medicalcoder/reference/summary.medicalcoder_comorbidities.html

Calculates Elixhauser comorbidity scores using the 'elixhauser_ahrq2025' method. This example specifies the primary diagnosis variable. The `poa` argument indicates presence of absence.

```r
elixhauser_results <- 
  comorbidities(data = mdcr,
                icd.codes = "code",
                id.vars = "patid",
                dx.var = "dx",
                method = "elixhauser_ahrq2025",
                primarydx = 1,
                flag.method = 'current',
                poa = 1)
summary(elixhauser_results)
```

--------------------------------

### Get PCCC Codes Data Frame

Source: http://www.peteredewitt.com/medicalcoder/reference/get_pccc_codes.html

Use this function to retrieve the complete lookup table for PCCC ICD codes. The output is a data frame suitable for further analysis or mapping.

```R
get_pccc_codes()
```

--------------------------------

### Get Charlson Index Scores Function

Source: http://www.peteredewitt.com/medicalcoder/reference/get_charlson_index_scores.html

This function retrieves a data frame containing Charlson comorbidity index scores for various conditions across different scoring variants.

```APIDOC
## get_charlson_index_scores()

### Description
Retrieve a copy of internal lookup tables of index scores used in assessing Charlson comorbidities.

### Usage
```R
get_charlson_index_scores()
```

### Value
A `data.frame` with the following columns:
  * `condition`: Character vector of the conditions
  * `index`: Character vector indicating if the score is for the mortality or the readmission index score
  * `charlson_<variant>`: the index scores for the variant

### See also
  * `get_charlson_codes()` for a lookup table of the ICD codes mapping to the Charlson comorbidities.
  * `comorbidities()` for applying comorbidity algorithms to a data set.

### Examples
```R
head(get_charlson_index_scores())
str(get_charlson_index_scores())
```
```

--------------------------------

### Apply Elixhauser Method Suppressing Warning

Source: http://www.peteredewitt.com/medicalcoder/articles/elixhauser.html

This code applies the Elixhauser method and suppresses the warning by setting `primarydx = 0`. The output is identical to the version that generated a warning.

```r
# no warning
mdcr_results <-
  comorbidities(
    data = mdcr,
    id.vars = "patid",
    icdv.var = "icdv",
    icd.codes = "code",
    dx.var = "dx",
    flag.method = "current",
    poa = 1,
    method = "elixhauser_ahrq2025",
    primarydx = 0
  )

identical(mdcr_results, mdcr_results0)
## [1] TRUE
```

--------------------------------

### Comorbidity Flagging with ICD-9 Specified

Source: http://www.peteredewitt.com/medicalcoder/articles/pccc.html

This example explicitly sets `icdv = 9L` to ensure that all input codes are treated as ICD-9, which can prevent unexpected flags when dealing with mixed ICD versions.

```R
comorbidities(
  data = subset(mdcr, patid == "95471"),
  icd.codes = "code",
  id.vars = 'patid',
  dx.var = "dx",
  icdv = 9L,
  poa = 1,
  method = "pccc_v3.1"
)[, c('patid', 'cmrb_flag')]
```

--------------------------------

### Define Column Mapping for Comparison

Source: http://www.peteredewitt.com/medicalcoder/articles/medicalcoder-vs-mimic.html

Creates a data frame to map column names between the `medicalcoder` output and the MIMIC-IV SQL results for easier comparison.

```r
dcolumns <- fread(text = "
medicalcoder | mimic
aidshiv      | aids
cebvd        | cerebrovascular_disease
copd         | chronic_pulmonary_disease
chf          | congestive_heart_failure
dem          | dementia
hp           | paraplegia
mi           | myocardial_infarct
pud          | peptic_ulcer_disease
pvd          | peripheral_vascular_disease
rnd          | renal_disease
rhd          | rheumatic_disease
age_score.x  | age_score.y
cci          | charlson_comorbidity_index
")
```
Tokens:
Raw