icd9 (version 1.0)

icd9Count: count icd9 codes or comorbidities for each patient

Description

icd9Count takes a data frame with a column for visitId and another for ICD-9 code, and returns the number of distinct codes for each patient.

The visitId field is typically the first column. If there is no column called visitId and visitId is not specified, the first column is used.

icd9CountComorbidBin differs from the other counting functions in that it counts _comorbidities_, not individual diagnoses. It accepts any data frame with either logicals or zero/non-zero contents, with a single column for visitId. No checks are made to see whether visitId is duplicated.

For icd9Count, it is assumed that all the columns apart from vistiId represent actual or possible ICD-9 codes. Duplicate visitIds are repeated as given and aggregated.

Usage

icd9Count(x, visitId = NULL, return.df = FALSE)
icd9CountComorbidBin(x, visitId = NULL, return.df = FALSE)
icd9CountWide(x, visitId = NULL, return.df = FALSE, aggregate = FALSE)

Arguments

x

data frame with one row per patient, and a true/false or 1/0 flag for each column. By default, the first column is the patient identifier and is not counted. If visitId is not specified, the first column is used.

visitId

The name of the column in the data frame which contains the patient or visit identifier. Typically this is the visit identifier, since patients come leave and enter hospital with different ICD-9 codes. It is a character vector of length one. Defaults to "visitId"

return.df

single logical, if TRUE, return the result as a data frame with the first column being the visitId, and the second being the count. If visitId was a factor or named differently in the input, this is preserved.

aggregate,

single logical, default is FALSE. If TRUE, the length (or rows) of the output will no longer match the input, but duplicate visitIds will be counted together.

Value

vector of the count of comorbidities for each patient. This is sometimes used as a metric of comorbidity load, instead of, or inaddition to metrics like the Charlson Comorbidity Index (aka Charlson Score)

Details

TODO: optionally check each code is valid before counting.

Examples

# NOT RUN {
mydf <- data.frame(visitId = c("r", "r", "s"),
                   icd9 = c("441", "412.93", "044.9"))
  icd9Count(mydf, return.df = TRUE)
  icd9Count(mydf)

  cmb <- icd9ComorbidQuanDeyo(mydf, isShort = FALSE)
  icd9CountComorbidBin(cmb)

  wide <- data.frame(visitId = c("r", "s", "t"),
                   icd9_1 = c("0011", "441", "456"),
                   icd9_2 = c(NA, "442", NA),
                   icd9_3 = c(NA, NA, "510"))
  icd9CountWide(wide)
  # or:
  library(magrittr)
  wide %>% icd9WideToLong %>% icd9Count
# }