A Unified and Longitudinally Aware Framework for ICD-Based Comorbidity Assessment

Provides comorbidity classification algorithms such as the Pediatric Complex Chronic Conditions (PCCC), Charlson, and Elixhauser indices, supports longitudinal comorbidity flagging across encounters, and includes utilities for working with medical coding schemas such as the International Classification of Diseases (ICD).

medicalcoder: A Unified and Longitudinally Aware Framework for ICD-Based Comorbidity Assessment

medicalcoder is a lightweight, base-R package for working with ICD-9 and ICD-10 diagnosis and procedure codes. It implements widely used comorbidity algorithms such as Charlson, Elixhauser, and the Pediatric Complex Chronic Conditions (PCCC), supports longitudinal comorbidity flagging across encounters, and provides fast, dependency-free utilities to look up, validate, and manipulate ICD codes. Designed for portability and reproducibility, the package avoids external dependencies—requiring only R ≥ 3.5.0—yet offers a rich set of curated ICD code libraries from the United States' Centers for Medicare and Medicaid Services (CMS), Centers for Disease Control (CDC), the World Health Organization (WHO), Australia's Independent Health and Aged Care Pricing Authority (IHACPA), and Sweden's National Board of Health and Welfare (Socialstyrelsen).

The package balances performance with elegance: its internal caching, efficient joins, and compact data structures make it practical for large-scale health data analyses, while its clean design makes it easy to extend or audit. Whether you need to flag comorbidities, explore ICD hierarchies, or standardize clinical coding workflows, medicalcoder provides a robust, transparent foundation for research and applied work in biomedical informatics.

The primary objectives of medicalcoder are:

1. Fully self-contained

   • Minimal Dependencies

     • No dependencies other than base R.
     • Requires R version ≥ 3.5.0 due to a change in data serialization. R 3.5.0 was released in April 2018. The initial public release of medicalcoder was in 2025.
     • Several packages are listed in the Suggests section of the DESCRIPTION file. These are only needed for building vignettes, other documentation, and testing. They are not required to install the package.

   • No Imports

     • medicalcoder does not import any non-base namespaces. This improves ease of maintenance and usability.
     • Suggested packages are needed only for development work and building vignettes. They are not required for installation or use.

   • That said, there are non-trivial performance gains when passing a data.table to the comorbidities() function. Passing a tibble is typically faster than a base data.frame but slower than a data.table. (See benchmarking).

   • Internal lookup tables

     • All required data are included in the package. If you have the .tar.gz source file and R ≥ 3.5.0, that is all you need to install and use the package.

2. Efficient implementation of multiple comorbidity algorithms

   • Implements three general algorithms, each with multiple variants. Details are provided below.
   • Supports flagging of subconditions within PCCC.
   • Supports longitudinal flagging of comorbidities. medicalcoder will flag comorbidities based on present-on-admission indicators for the current encounter and can look back in time for a patient to flag a comorbidity if reported in a prior encounter. See examples.

3. Tools for working with ICD codes

   • Lookup tables.
   • Ability to work with both full codes (ICD codes with decimal points) and compact codes (ICD codes with decimal points omitted).

Why Use medicalcoder

There are several tools for working with ICD codes and comorbidity algorithms. medicalcoder provides novel features:

• Unified access to multiple comorbidity algorithms through a single function: comorbidities().
• Support for both ICD-9 and ICD-10 diagnostic and procedure codes.
• Longitudinal patient-level comorbidity flagging using present-on-admission indicators.
• Fully self-contained package (no external dependencies).

Install

From CRAN

install.packages("medicalcoder")

From GitHub

remotes::install_github("dewittpe/medicalcoder")

From source

If you have the .tar.gz file for version X.Y.Z, e.g., medicalcoder_X.Y.Z.tar.gz you can install from within R via:

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

From the command line:

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

Reproducibility and versioning

medicalcoder includes ICD code data and comorbidity mappings that can change between package releases. Record the package version used for an analysis so that its results can be reproduced:

packageVersion("medicalcoder")

For example, install a specific release from CRAN with:

remotes::install_version("medicalcoder", version = "0.10.0")

Citation

Two things to cite,

Peter E DeWitt, Seth Russell, James A Feinstein, Margaret N Rebull, Tellen D
Bennett, medicalcoder: a unified and longitudinally aware framework for
International Classification of Diseases code-based comorbidity assessment
in R, JAMIA Open, Volume 9, Issue 5, October 2026, ooag182

https://doi.org/10.1093/jamiaopen/ooag182

And the package itself (run this code locally to get the version you are using.

citation("medicalcoder", auto = TRUE)

Quick Start:

Example Data

Input data for comorbidities() is expected to be in a 'long' format. Each row is one code with additional columns for patient and/or encounter id. There are two example datasets in the package: mdcr and mdcr_longitudinal.

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

The mdcr dataset consists of 319 856 rows. Each row contains one ICD code (code). The column icdv denotes each code as ICD-9 or ICD-10, and the dx column denotes diagnostic (1L) or procedure (0L) code. This dataset contains diagnostic and procedure codes for 38 262 patients.

str(mdcr)
#> 'data.frame':	319856 obs. of  4 variables:
#>  $ patid: int  71412 71412 71412 71412 71412 17087 64424 64424 84361 84361 ...
#>  $ icdv : int  9 9 9 9 9 10 9 9 9 9 ...
#>  $ code : chr  "99931" "75169" "99591" "V5865" ...
#>  $ dx   : int  1 1 1 1 1 1 1 0 1 1 ...
head(mdcr)
#>   patid icdv  code dx
#> 1 71412    9 99931  1
#> 2 71412    9 75169  1
#> 3 71412    9 99591  1
#> 4 71412    9 V5865  1
#> 5 71412    9  V427  1
#> 6 17087   10  V441  1

The mdcr_longitudinal dataset is distinct from the mdcr dataset. The major difference is that this dataset contains only diagnostic codes and there are only 3 patients. The date column denotes the date of the diagnosis and allows us to look at changes in comorbidities over time.

str(mdcr_longitudinal)
#> 'data.frame':	60 obs. of  4 variables:
#>  $ patid: int  9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 ...
#>  $ date : IDate, format: "2016-03-18" "2016-03-24" ...
#>  $ icdv : int  10 10 10 10 10 10 10 10 10 10 ...
#>  $ code : chr  "Z77.22" "IMO0002" "V87.7XXA" "J95.851" ...
head(mdcr_longitudinal)
#>     patid       date icdv     code
#> 1 9663901 2016-03-18   10   Z77.22
#> 2 9663901 2016-03-24   10  IMO0002
#> 3 9663901 2016-03-24   10 V87.7XXA
#> 4 9663901 2016-03-25   10  J95.851
#> 5 9663901 2016-03-30   10  IMO0002
#> 6 9663901 2016-03-30   10    Z93.0

Comorbidity Algorithms

There are three comorbidity methods, each with several variants, available in medicalcoder. All of which are accessible through the comorbidities() method by specifying the method argument.

General examples and explanations for when conditions are flagged are in the vignette

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

Pediatric Complex Chronic Conditions (PCCC)

• Version 2.0

  • BMC Pediatrics: Feudtner et al. (2014)
  • Consistent with R package pccc

• Version 2.1

  • Updated code base with the same assessment algorithm as version 2.0.

• Version 3.0

  • JAMA Network Open: Feinstein et al. (2024)
  • Children's Hospital Association Toolkit

• Version 3.1

  • Updated code base with the same assessment algorithm as version 3.0.

• All variants can flag conditions and subconditions.

# PCCC v2.1 and v3.1 example
library(medicalcoder)
cmrbs2 <-
  comorbidities(
    data      = mdcr,
    id.vars   = "patid", # can use more than one column, e.g., site, patient, encounter
    icd.codes = "code",
    dx.var    = "dx",
    poa       = 1L, # consider all codes to be present-on-admission
    method    = "pccc_v2.1"
  )
cmrbs3 <-
  comorbidities(
    data      = mdcr,
    id.vars   = "patid",
    icd.codes = "code",
    dx.var    = "dx",
    poa       = 1L, # consider all codes to be present-on-admission
    method    = "pccc_v3.1"
  )
str(cmrbs2, max.level = 0)
#> Classes 'medicalcoder_comorbidities' and 'data.frame':	38262 obs. of  16 variables:
#>  - attr(*, "method")= chr "pccc_v2.1"
#>  - attr(*, "id.vars")= chr "patid"
#>  - attr(*, "flag.method")= chr "current"
str(cmrbs3, max.level = 0)
#> Classes 'medicalcoder_comorbidities' and 'data.frame':	38262 obs. of  49 variables:
#>  - attr(*, "method")= chr "pccc_v3.1"
#>  - attr(*, "id.vars")= chr "patid"
#>  - attr(*, "flag.method")= chr "current"

A summary of the flagged conditions is generated with a call to summary().

s2 <- summary(cmrbs2)
str(s2)

For pccc_v2.0 and pccc_v2.1 the data.frame returned by summary() reports the count (unique id.vars with the condition) and percentage.

s3 <- summary(cmrbs3)
str(s3)

For pccc_v3.0 and pccc_v3.1 the returned data.frame reports counts and percentages for how the condition was flagged based on diagnosis/procedure codes only, technology-dependent codes only, or both. The dxpr_or_tech columns answer the question "did this patient have the condition".

Further detail, examples, and explanations are in the vignette.

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

Charlson Comorbidities

There are eight variants of Charlson comorbidities implemented in medicalcoder:

• Deyo, Cherkin, and Ciol (1992)
• Quan et al. (2005)
• Quan et al. (2011)
• Sundararajan et al. (2004)
• Glasheen (2019)
• Ludvigsson et al. (2021)
• Beyrer et al. (2021)
• MIMIC-IV Charlson SQL from mimic-code

# Charlson example
cmrbs <-
  comorbidities(
    data      = mdcr,
    id.vars   = "patid",
    icd.codes = "code",
    dx.var    = "dx",
    poa       = 1L,       # assume all codes are present-on-admission
    primarydx = 0L,       # assume all codes are secondary diagnosis codes
    method    = "charlson_quan2005"
  )

A summary of the flagged conditions can be generated by calling summary(). Where the summary for the PCCC method was a data.frame the return for the Charlson comorbidities is a list of data frames summarizing the conditions, age category, and the index score.

s <- summary(cmrbs)
str(s, max.level = 1)

More details and examples are provided in the vignette:

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

Elixhauser Comorbidities

• Elixhauser et al. (1998)
  • method = elixhauser_elixhauser1998
• Quan et al. (2005)
  • method = elixhauser_quan2005
• AHRQ (2017, 2022, 2023, 2024, 2025, 2026, ICD10)
  • For ICD-9 codes
    • method = elixhauser_ahrq_web
  • For ICD-10 codes
    • method = elixhauser_ahrq2022
    • method = elixhauser_ahrq2023
    • method = elixhauser_ahrq2024
    • method = elixhauser_ahrq2025
    • method = elixhauser_ahrq2026
    • method = elixhauser_ahrq_icd10

# Elixhauser example
cmrbs <-
  comorbidities(
    data      = mdcr,
    id.vars   = "patid",
    icd.codes = "code",
    dx.var    = "dx",
    poa       = 1L,
    primarydx = 0L,
    method    = "elixhauser_ahrq_icd10"
  )

The summary for the results from method = elixhauser_ahrq_icd10 is similar to those for Charlson. A data.frame with the counts and percentages of distinct data[id.vars] with the noted condition, and a summary of the index scores.

s <- summary(cmrbs)
str(s, max.level = 1)

More details and examples are provided in the vignette:

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

ICD

The package contains internal datasets with references for ICD-9 and ICD-10 US based diagnostic and procedure codes. These codes are supplemented with additional codes from the World Health Organization and ICD-10-AM codes from IHACPA, and ICD-10-SE codes from Socialstyrelsen.

You can get a table of ICD codes via get_icd_codes().

str(medicalcoder::get_icd_codes())
#> 'data.frame':	330947 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 ...

The columns are:

• icdv: integer value 9L or 10L; for ICD-9 or ICD-10

• dx: integer 0L or 1L; 0L = procedure code, 1L = diagnostic code

• full_code: character string for the ICD code with any appropriate decimal point.

• code: character string for the compact ICD code, that is, the ICD code without any decimal point, e.g., the full code C00.1 has the compact code form C001.

• src: character string denoting the source of the ICD code information.

  • cms: The ICD-9-CM, ICD-9-PCS, ICD-10-CM, or ICD-10-PCS codes curated by the Centers for Medicare and Medicaid Services (CMS).
  • cdc: CDC mortality ICD-10 codes (calendar years), plus historical CDC ICD-9 extracts. CDC-hosted ICD-10-CM files are represented by the canonical cms source.
  • ihacpa: ICD-10-AM codes from the Independent Health and Aged Care Pricing Authority.
  • socialstyrelsen: ICD-10-SE codes from Sweden's National Board of Health and Welfare (Socialstyrelsen).
  • who: World Health Organization.

• known_start: The earliest source year when source data for the code was available in the source code for medicalcoder. Codes from CMS are for the United States fiscal year. Codes from IHACPA use the Australian financial year, which starts July 1 and ends June 30. Codes from CDC, Socialstyrelsen, and WHO are calendar year. The United States fiscal year starts October 1 and concludes September 30. For example, fiscal year 2013 started October 1 2012 and concluded September 30 2013.

  To reemphasize that the year is for the data within medicalcoder. For ICD-9-CM, the codes went into effect for fiscal year 1980. The source code only has documented source files for the codes dating back to 1997.

• known_end: The latest (fiscal) year when the code was part of the ICD system and/or known within the medicalcoder lookup tables.

• Assignable codes. Some codes are header codes, e.g., ICD-10-CM three-digit code Z94 is a header code because the four-digit codes Z94.0, Z94.1, Z94.2, Z94.3, Z94.4, Z94.5, Z94.6, Z94.7, Z94.8, and Z94.9 exist. All but Z94.8 are assignable codes because no five-digit codes with the same initial four-digits exist. Z94.8 is a header code because the five-digit codes Z94.81, Z94.82, Z94.83, Z94.84, and Z94.89 exist.

  • assignable_start: Earliest (fiscal) year when the code was assignable.
  • assignable_end: Latest (fiscal) year when the code was assignable.

subset(
  x = lookup_icd_codes("^Z94", regex = TRUE, full.codes = TRUE, compact.codes = FALSE),
  subset = src == "cms",
  select = c("full_code", "known_start", "known_end", "assignable_start", "assignable_end")
)
#>    full_code known_start known_end assignable_start assignable_end
#> 1        Z94        2014      2027               NA             NA
#> 5      Z94.0        2014      2027             2014           2027
#> 9      Z94.1        2014      2027             2014           2027
#> 13     Z94.2        2014      2027             2014           2027
#> 17     Z94.3        2014      2027             2014           2027
#> 21     Z94.4        2014      2027             2014           2027
#> 25     Z94.5        2014      2027             2014           2027
#> 29     Z94.6        2014      2027             2014           2027
#> 33     Z94.7        2014      2027             2014           2027
#> 37     Z94.8        2014      2027               NA             NA
#> 41    Z94.81        2014      2027             2014           2027
#> 43    Z94.82        2014      2027             2014           2027
#> 45    Z94.83        2014      2027             2014           2027
#> 47    Z94.84        2014      2027             2014           2027
#> 50    Z94.89        2014      2027             2014           2027
#> 52     Z94.9        2014      2027             2014           2027

Additionally, the get_icd_codes() method can provide descriptions and the ICD hierarchy by using the with.descriptions and/or with.hierarchy arguments.

Functions lookup_icd_codes(), is_icd(), and icd_compact_to_full() are also provided for working with ICD codes.

More details and examples are in the vignette:

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

Benchmarking

The major factors impacting the expected computation time for applying a comorbidity algorithm to a dataset are:

1. Data size: number of subjects/encounters.
2. Data storage class: medicalcoder has been built such that no imports of other namespaces are required. That said, when a data.table is passed to comorbidities() and the data.table namespace is available, then S3 dispatch for merge is used, along with some other methods, to reduce memory use and reduce computation time. When a tibble is passed and the tidyverse namespaces are available, the tibble-aware paths improve performance over a base data.frame, but data.table remains fastest.
3. flag.method: "current" will take less time than the "cumulative" method.

Details on the benchmarking method, summary graphics, and tables can be found on the medicalcoder GitHub benchmarking directory.

Testing

Along with the GitHub actions and testing on current versions of R, the testing directory in the medicalcoder GitHub repo reports the R CMD check results for R versions from 3.5.0 through the latest release, with and without suggested packages.