Type: Package
Version: 1.0
Title: Calculation of Comorbidity and Frailty Scores
Description: Computes comorbidity indices and combined frailty scores for multiple ICD coding systems, including ICD-10-CA, ICD-10-CM, and ICD-11. The package provides tools to preprocess episode data, map diagnosis codes to chronic categories, propagate conditions across episodes, and generate comorbidity and frailty measures. The methods implemented are original to this package and were developed by the authors for research applications; a manuscript describing the methodology is currently in preparation.
URL: https://github.com/bayaniazadeh/LabTNSCPSSPackage
BugReports: https://github.com/bayaniazadeh/LabTNSCPSSPackage/issues
License: GPL-3
Depends: R (≥ 4.3.0)
Imports: dplyr, checkmate, data.table, stringi, ggplot2, lubridate, purrr
Suggests: testthat, roxygen2, devtools
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-02-17 15:24:59 UTC; azadehbayani
Author: Azadeh Bayani ORCID iD [aut, cre], Jean Noel Nikiema [ctb], Michèle Bally [ctb]
Maintainer: Azadeh Bayani <azadeh.bayani@umontreal.ca>
Repository: CRAN
Date/Publication: 2026-02-20 08:30:07 UTC

LABTNSCPSS: Calculation of Comorbidity and Frailty Scores

Description

Computes comorbidity indices and combined frailty scores for multiple ICD coding systems, including ICD-10-CA, ICD-10-CM, and ICD-11. The package provides tools to preprocess episode data, map diagnosis codes to chronic categories, propagate conditions across episodes, and generate comorbidity and frailty measures. The methods implemented are original to this package and were developed by the authors for research applications; a manuscript describing the methodology is currently in preparation.

Author(s)

Maintainer: Azadeh Bayani azadeh.bayani@umontreal.ca (ORCID)

Other contributors:

See Also

Useful links:

Basal_Codes

Description

Basal codes used for frailty/comorbidity mapping.

Usage

Basal_Codes

Format

A data frame.

Source

Internal data.

Code_Categories

Description

Category definitions used in the comorbidity/frailty algorithms.

Usage

Code_Categories

Format

A data frame.

Source

Internal data.

Frailty_Comorbidity_Mapping

Description

Mapping table between frailty items and comorbidity categories.

Usage

Frailty_Comorbidity_Mapping

Format

A data frame.

Source

Internal data.

Frailty_ICD10CM

Description

ICD-10-CM code lists used to compute frailty scores.

Usage

Frailty_ICD10CM

Format

A data frame.

Source

Internal data.

Frailty_ICD11

Description

ICD-11 code lists used to compute frailty scores.

Usage

Frailty_ICD11

Format

A data frame.

Source

Internal data.

Frailty_mapping

Description

Mapping table used to compute frailty scores.

Usage

Frailty_mapping

Format

A data frame.

Source

Internal data.

ICD10CA_categorisation

Description

ICD-10-CA categorisation table used by the package.

Usage

ICD10CA_categorisation

Format

A data frame.

Source

Internal data.

ICD10CM_Basal_Codes

Description

Basal codes for ICD-10-CM used by the package.

Usage

ICD10CM_Basal_Codes

Format

A data frame.

Source

Internal data.

ICD10CM_categorisation

Description

ICD-10-CM categorisation table used by the package.

Usage

ICD10CM_categorisation

Format

A data frame.

Source

Internal data.

ICD11_Basal_Codes

Description

Basal codes for ICD-11 used by the package.

Usage

ICD11_Basal_Codes

Format

A data frame.

Source

Internal data.

ICD11_categorisation

Description

ICD-11 categorisation table used by the package.

Usage

ICD11_categorisation

Format

A data frame.

Source

Internal data.

LABTNSCPSS: Tools for comorbidity and frailty scoring

Description

This package provides tools for preprocessing patient data, managing chronic pathologies, calculating frailty scores, and computing comorbidity indices (Charlson and Elixhauser).

Tools for calculating comorbidity indices and frailty scores from ICD coding systems (ICD-10-CA, ICD-10-CM, ICD-11).

Author(s)

Maintainer: Azadeh Bayani azadeh.bayani@umontreal.ca (ORCID)

Other contributors:

See Also

Useful links:

Useful links:

Australian mortality data, 2010

Description

A dataset containing Australian mortality data, obtained from Stata 17.

Usage

australia10

Format

A data frame with 3,322 rows and 3 variables:

cause

ICD-10 code representing cause of death

sex

Gender

deaths

Number of deaths

Note

The R code used to download and process the dataset from Stata is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

Comorbidity mapping

Description

Maps ICD diagnostic codes to comorbidity categories using supported algorithms.

Usage

comorbidity(x, id, code, map, assign0, labelled = TRUE, tidy.codes = TRUE)

Arguments

x

A data.frame (or data.table/tibble) containing at least an ID column and a code column.

id

Name of the patient identifier column in 'x'.

code

Name of the diagnostic code column in 'x'.

map

Name of the mapping algorithm to use.

assign0

Logical; if TRUE, applies hierarchy rules to avoid double-counting.

labelled

Logical; if TRUE, adds labels for easier viewing.

tidy.codes

Logical; if TRUE, cleans codes before mapping.

Value

A data.frame with comorbidity indicator columns and class '"comorbidity"'.

Examples

x <- data.frame(
  id = c(1, 1, 2, 2),
  code = c("I10", "E119", "F200", "E110"),
  stringsAsFactors = FALSE
)

comorbidity(x = x, id = "id", code = "code",
            map = "charlson_icd10_quan", assign0 = FALSE)

comorbidity(x = x, id = "id", code = "code",
            map = "elixhauser_icd10_quan", assign0 = FALSE)

Choose a coding system

Description

Choose a coding system

Usage

get_coding_system(
  coding_system = NULL,
  valid_systems = c("ICD-10-CA", "ICD-10-CM", "ICD-11"),
  verbose = interactive()
)

Arguments

coding_system

Character string. If NULL and interactive(), the user is prompted.

valid_systems

Character vector of allowed coding systems.

verbose

Logical; if TRUE, prints guidance using message().

Value

A single character string indicating the selected coding system.

ICD-10 Diagnostic Codes, 2009 Version

Description

A dataset containing the 2009 version of the ICD-10 codes.

Usage

icd10_2009

Format

A data frame with 10,817 rows and 4 variables:

Code

ICD-10 diagnostic code

Code.clean

ICD-10 diagnostic code, removing all punctuation

ICD.title

Code description, in plain English.

Status

Additional information, if available.

Note

The R code used to download and process the dataset from the CDC website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

Source

CDC Website: https://goo.gl/6e2mvb

ICD-10 Diagnostic Codes, 2011 Version

Description

A dataset containing the 2011 version of the ICD-10 codes.

Usage

icd10_2011

Format

A data frame with 10,856 rows and 4 variables:

Code

ICD-10 diagnostic code

Code.clean

ICD-10 diagnostic code, removing all punctuation

ICD.title

Code description, in plain English.

Status

Additional information, if available.

Note

The R code used to download and process the dataset from the CDC website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

Source

CDC Website: https://goo.gl/rcTJJ2

ICD-10-CM Diagnostic Codes, 2017 Version

Description

A dataset containing the 2017 version of the ICD10-CM coding system.

Usage

icd10cm_2017

Format

A data frame with 71,486 rows and 2 variables:

Code

ICD-10-CM diagnostic code

Description

Description of each code

Note

The R code used to download and process the dataset from the CDC website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

ICD-10-CM Diagnostic Codes, 2018 Version

Description

A dataset containing the 2018 version of the ICD10-CM coding system.

Usage

icd10cm_2018

Format

A data frame with 71,704 rows and 2 variables:

Code

ICD-10-CM diagnostic code

Description

Description of each code

Note

The R code used to download and process the dataset from the CDC website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

ICD-10-CM Diagnostic Codes, 2022 Version

Description

A dataset containing the 2022 version of the ICD10-CM coding system.

Usage

icd10cm_2022

Format

A data frame with 72,750 rows and 2 variables:

Code

ICD-10-CM diagnostic code

Description

Description of each code

Note

The R code used to download and process the dataset from the CDC website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

ICD-9 Diagnostic Codes, 2015 Version (v32)

Description

A dataset containing the version of the ICD-9 codes effective October 1, 2014.

Usage

icd9_2015

Format

A data frame with 14,567 rows and 3 variables:

Code

ICD-9 diagnostic code

Long_description

Long description of each code

Short_description

Short description of each code

Note

The R code used to download and process the dataset from the CMS.gov website is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

Source

CMS.gov Website: https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html

Adult same-day discharges, 2010

Description

A dataset containing adult same-day discharges from 2010, obtained from Stata 17.

Usage

nhds2010

Format

A data frame with 2,210 rows and 15 variables:

ageu

Units for age

age

Age

sex

Sex

race

Race

month

Discharge month

status

Discharge status

region

Region

atype

Type of admission

dx1

Diagnosis 1, ICD9-CM

dx2

Diagnosis 2, ICD9-CM

dx3

Diagnosis 3, ICD9-CM, imported incorrectly

dx3corr

Diagnosis 3, ICD9-CM, corrected

pr1

Procedure 1

wgt

Frequency weight

recid

Order of record (raw data)

Note

The R code used to download and process the dataset from Stata is available [here](https://raw.githubusercontent.com/ellessenne/comorbidity/master/data-raw/make-data.R).

Run the LABTNSCPSS comorbidity + frailty pipeline

Description

Runs data cleaning, chronic pathology propagation, frailty calculation, and comorbidity/frailty scoring.

Usage

run_pipeline(input_file, col_mapping, coding_system = NULL, out_dir = NULL)

Arguments

input_file

Path to the input CSV file.

col_mapping

Named list mapping standard names to the user's column names.

coding_system

Coding system used (e.g., "ICD-10-CA"). Optional; used by internal functions when applicable.

out_dir

Output directory for generated files. If NULL, writes next to 'input_file'.

Value

A named list of 'data.frame's returned by 'Comorbidity_Frailty_Calculation()'. Elements typically include comorbidity and frailty summary tables.

Compute (weighted) comorbidity scores

Description

Compute (weighted) comorbidity scores

Usage

score(x, weights = NULL, assign0)

Arguments

x

An object of class 'comorbidity' returned by a call to comorbidity().

weights

A string denoting the weighting system to be used (depends on the mapping algorithm).

Possible values for the Charlson index are: * 'charlson', for the original weights by Charlson et al. (1987); * 'quan', for the revised weights by Quan et al. (2011).

Possible values for the Elixhauser score are: * 'vw', for the weights by van Walraven et al. (2009); * 'swiss', for the Swiss Elixhauser weights by Sharma et al. (2021).

Defaults to 'NULL', in which case an unweighted score will be used.

assign0

A logical value indicating whether to apply a hierarchy of comorbidities (so milder forms are set to 0).

Value

A numeric vector with the (possibly weighted) comorbidity score for each subject.

References

Charlson ME, Pompei P, Ales KL, et al. _A new method of classifying prognostic comorbidity in longitudinal studies: development and validation_. Journal of Chronic Diseases 1987; 40:373-383.

Quan H, Li B, Couris CM, et al. _Updating and validating the Charlson Comorbidity Index and Score for risk adjustment in hospital discharge abstracts using data from 6 countries_. American Journal of Epidemiology 2011; 173(6):676-682.

van Walraven C, Austin PC, Jennings A, Quan H and Forster AJ. _A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data_. Medical Care 2009; 47(6):626-633.

Sharma N, Schwendimann R, Endrich O, et al. _Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data_. BMC Health Services Research 2021; 21(13).

Examples

# IMPORTANT: these examples must be runnable during R CMD check.
# If `x` is not defined in examples, create a tiny reproducible dataset here.
x <- data.frame(id = c(1, 1, 2), code = c("I10", "E119", "J449"), stringsAsFactors = FALSE)

# Checking the `assign0` argument:
x3 <- data.frame(id = 1, code = c("E100", "E102"), stringsAsFactors = FALSE)
ccF <- comorbidity(x = x3, id = "id", code = "code", map = "charlson_icd10_quan", assign0 = FALSE)
score(x = ccF, assign0 = FALSE)
score(x = ccF, assign0 = TRUE)