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Charlson Comorbidities

library(medicalcoder)
packageVersion("medicalcoder")
## [1] '0.6.0'

Introduction

The medicalcoder package implements several variants of the Charlson comorbidities algorithm.

ICD Codes and Index Scores

End users can access the lookup tables for the mapping between ICD-9 and ICD-10 codes to conditions and the index scoring in data.frames from the get_charlson_codes() and get_charlson_index_scores() calls respectively.

str(get_charlson_codes())
## 'data.frame':    7410 obs. of  9 variables:
##  $ icdv             : int  9 9 9 9 9 9 9 9 9 9 ...
##  $ dx               : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ full_code        : chr  "003.1" "007.2" "007.4" "010" ...
##  $ code             : chr  "0031" "0072" "0074" "010" ...
##  $ condition        : chr  "aids" "aids" "aids" "aids" ...
##  $ charlson_cdmf2019: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ charlson_deyo1992: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ charlson_quan2005: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ charlson_quan2011: int  0 0 0 0 0 0 0 0 0 0 ...
str(get_charlson_index_scores())
## 'data.frame':    21 obs. of  6 variables:
##  $ condition_description: chr  "AIDS" "AIDS/HIV" "HIV infection, no AIDS" "Any malignancy" ...
##  $ condition            : chr  "aids" "aidshiv" "hiv" "mal" ...
##  $ charlson_cdmf2019    : int  6 NA 3 2 1 1 1 1 2 1 ...
##  $ charlson_quan2011    : int  NA 4 NA 2 0 1 2 2 1 0 ...
##  $ charlson_quan2005    : int  NA 6 NA 2 1 1 1 1 2 1 ...
##  $ charlson_deyo1992    : int  NA 6 NA 2 1 1 1 1 2 1 ...

Applying Charlson

Example: applying the Quan et al. (2005) variant of the Charlson comorbidities to the mdcr data is be done as follows.

mdcr_results <-
  comorbidities(
    data = mdcr,
    id.vars = "patid",
    icdv.var = "icdv",
    icd.codes = "code",
    dx.var = "dx",
    flag.method = "current",
    poa = 1,
    method = "charlson_quan2005"
  )

The return object is a data.frame with 0/1 integer indicator columns for the relevant conditions, the id.vars (if applicable), age_score (if age, in years, is supplied), num_cmrb the number of comorbidities, cmrb_flag a 0/1 indicator for presence of at least one comorbidity, and cci the Charlson Comorbidity Index.

str(mdcr_results)
## Classes 'medicalcoder_comorbidities' and 'data.frame':   38262 obs. of  22 variables:
##  $ patid    : int  10000 10002 10005 10006 10008 10010 10014 10015 10017 10018 ...
##  $ aidshiv  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ mal      : int  0 0 1 0 0 0 0 0 0 0 ...
##  $ cebvd    : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ copd     : int  1 0 0 0 0 0 0 0 0 0 ...
##  $ chf      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ dem      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ dmc      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ dm       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ hp       : int  0 0 0 0 0 0 0 1 0 0 ...
##  $ mld      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ msld     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ mst      : int  0 0 1 0 0 0 0 0 0 0 ...
##  $ mi       : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ pud      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ pvd      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ rnd      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ rhd      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ num_cmrb : int  1 0 2 0 0 0 0 1 0 0 ...
##  $ cmrb_flag: int  1 0 1 0 0 0 0 1 0 0 ...
##  $ cci      : int  1 0 6 0 0 0 0 2 0 0 ...
##  $ age_score: int  NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, "method")= chr "charlson_quan2005"
##  - attr(*, "id.vars")= chr "patid"
##  - attr(*, "flag.method")= chr "current"

Call summary() on the return object for a list of summary objects. These can be used to generate output tables to the end user’s liking.

str(
  summary(mdcr_results)
)
## List of 3
##  $ conditions   :'data.frame':   22 obs. of  4 variables:
##   ..$ condition_description: chr [1:22] "AIDS/HIV" "Any malignancy" "Cerebrovascular disease" "Chronic pulmonary disease" ...
##   ..$ condition            : chr [1:22] "aidshiv" "mal" "cebvd" "copd" ...
##   ..$ count                : num [1:22] 7 2577 411 3415 684 ...
##   ..$ percent              : num [1:22] 0.0183 6.7351 1.0742 8.9253 1.7877 ...
##  $ age_summary  :'data.frame':   1 obs. of  3 variables:
##   ..$ age_score: Factor w/ 0 levels: NA
##   ..$ count    : int 38262
##   ..$ percent  : num 100
##  $ index_summary:'data.frame':   1 obs. of  5 variables:
##   ..$ min   : int 0
##   ..$ q1    : num 0
##   ..$ median: num 0
##   ..$ q3    : num 1
##   ..$ max   : int 12
x <- summary(mdcr_results)$conditions[, c("condition_description", "count", "percent")]
tab <-
  kableExtra::kbl(
    x = x,
    format = "html",
    caption = "Counts and percentages of patients in the mdcr example data sets with the @quan2005 comorbidities.",
    col.names = c("", "Count", "Percentage"),
    digits = 3
  )
tab <- kableExtra::pack_rows(tab, group_label = "Comorbidity", start_row = 1, end_row = 17)
tab <- kableExtra::pack_rows(tab, group_label = "Total Comorbidities", start_row = 18, end_row = nrow(x))
tab
Counts and percentages of patients in the mdcr example data sets with the Quan et al. (2005) comorbidities.
Count Percentage
Comorbidity
AIDS/HIV 7 0.018
Any malignancy 2577 6.735
Cerebrovascular disease 411 1.074
Chronic pulmonary disease 3415 8.925
Congestive heart failure 684 1.788
Dementia 13 0.034
Diabetes with chronic complications 13 0.034
Diabetes without chronic complications 445 1.163
Hemiplegia or paraplegia 1177 3.076
Liver disease, mild 632 1.652
Liver disease, moderate to severe 206 0.538
Metastatic solid tumor 453 1.184
Myocardial infarction 10 0.026
Peptic ulcer disease 45 0.118
Peripheral vascular disease 217 0.567
Renal disease 877 2.292
Rheumatic disease 136 0.355
Total Comorbidities
>= 1 9789 25.584
>= 2 1368 3.575
>= 3 138 0.361
>= 4 20 0.052
>= 5 3 0.008

References

Deyo, Richard A, Daniel C Cherkin, and Marcia A Ciol. 1992. “Adapting a Clinical Comorbidity Index for Use with ICD-9-CM Administrative Databases.” Journal of Clinical Epidemiology 45 (6): 613–19. https://doi.org/https://doi.org/10.1016/0895-4356(92)90133-8.
Glasheen, William P, Tristan Cordier, Rajiv Gumpina, Gil Haugh, Jared Davis, and Andrew Renda. 2019. “Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation.” American Health & Drug Benefits 12 (4): 188. https://pubmed.ncbi.nlm.nih.gov/31428236/.
Quan, Hude, Bo Li, Colette M. Couris, Kiyohide Fushimi, Peter Graham, Philip Hider, Jean-Michel Januel, and Vijaya Sundararajan. 2011. “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 173 (6): 676–82. https://doi.org/10.1093/aje/kwq433.
Quan, Hude, Vijaya Sundararajan, Patricia Halfon, Andrew Fong, Bernard Burnand, Jean-Christophe Luthi, L Duncan Saunders, Catherine A. Beck, Thomas E. Feasby, and William A. Ghali. 2005. “Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data.” Medical Care 43 (11): 1130–39. https://doi.org/10.1097/01.mlr.0000182534.19832.83.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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