Create Deyo map of ICD-9-CM to Charlson comorbidities

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Description

Function that generates a data frame linking ICD-9-CM codes to the Charlson comorbidity categories using the Deyo mapping.

Usage

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Arguments

icd9

a unique character vector of ICD-9-CM codes

Details

NOTE: The input vector of ICD-9-CM codes must be unique, because the output dataframe uses the ICD-9-CM code as row.name.

Uses regular expressions created from the paper by Deyo in 1992.

ICD-9-CM codes must have periods removed. Diagnostic codes are prefixed with 'D' while procedure codes are prefixed with 'P'. So, diagnostic code 404.03 should be "D40403".

Value

A data frame, with ICD9 codes as row names and one logical column for each comorbidity in charlson_list

References

1. Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. Journal of clinical epidemiology 1992; 45:613-9 http://www.ncbi.nlm.nih.gov/pubmed/1607900

See Also

icd9cm_charlson_quan, icd9cm_charlson_romano, icd9cm_elixhauser_quan, icd9cm_elixhauser_ahrq37, charlson_weights,

Examples

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# Identify Charlson categories in ICD-9-CM listing
cases <- data.frame(id=c(1,1,1,2,2,2),
  icd9cm=c("D20206","D24220","D4439","D5064","DE8788","D40403"))
cases_with_cm <- merge(cases, icd9cm_charlson_deyo(levels(cases$icd9cm)), 
  by.x="icd9cm", by.y="row.names", all.x=TRUE)

# generate crude comorbidity summary for each patient
library(plyr)
ddply(cases_with_cm, .(id), 
  function(x) { data.frame(lapply(x[,3:ncol(x)], any)) })

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