icd9Charlson: Calculate Charlson Comorbidity Index (Charlson Score)
In icd9: Tools for Working with ICD-9 Codes, and Finding Comorbidities

Description Usage Arguments Details Methods (by class) Examples

Charlson score is calculated in the basis of the Quan revision of Deyo's ICD-9 mapping. (Peptic Ulcer disease no longer warrants a point.) Quan published an updated set of scores, but it seems most people use the original scores for easier comaprison between studies, even though Quan's were more predictive.

icd9Charlson(x, visitId = NULL, scoringSystem = c("original", "charlson",
  "quan"), return.df = FALSE,
  stringsAsFactors = getOption("stringsAsFactors"), ...)

## S3 method for class 'data.frame'
icd9Charlson(x, visitId = NULL,
  scoringSystem = c("original", "charlson", "quan"), return.df = FALSE,
  stringsAsFactors = getOption("stringsAsFactors"), ...)

icd9CharlsonComorbid(x, visitId = NULL, applyHierarchy = FALSE,
  scoringSystem = c("original", "charlson", "quan"))

`x`	data frame containing a column of visit or patient identifiers, and a column of ICD-9 codes. It may have other columns which will be ignored. 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. If left empty, or `NULL`, then an attempt is made to guess which field has the ID for the patient encounter (not a patient ID, although this can of course be specified directly). The guesses proceed until a single match is made. Data frames may be wide with many matching fields, so to avoid false positives, anything but a single match is rejected. If there are no successful guesses, and `visitId` was not specified, then the first column of the data frame is used.
`scoringSystem`	One of `original`, `charlson`, or `quan`. The first two will give the original Charlson weights for each comorbidity, whereas `quan` uses the updated weights from Quan 2001.
`return.df`	single logical value, if true, a two column data frame will be returned, with the first column named as in input data frame (i.e. `visitId`), containing all the visits, and the second column containing the Charlson Comorbidity Index.
`stringsAsFactors`	single logical, passed on when constructing data.frame if `return.df` is `TRUE`. If the input data frame `x` has a factor for the visitId, this is not changed, but a non-factor visitId may be converted or not converted according to your system default or this setting.
`...`	further arguments to pass on to `icd9ComorbidQuanDeyo`, e.g. `icd9Field`
`applyHierarchy`	single logical value, default is FALSE. If TRUE, will drop DM if DMcx is present, etc.

When used, hierarchy is applied per Quan, "The following comorbid conditions were mutually exclusive: diabetes with chronic complications and diabetes without chronic complications; mild liver disease and moderate or severe liver disease; and any malignancy and metastatic solid tumor." The "quan" scoring weights come from the 2011 paper (dx.doi.org/10.1093/aje/kwq433). The comorbidity weights were recalculated using updated discharge data, and some changes, such as Myocardial Infarcation decreasing from 1 to 0, may reflect improved outcomes due to advances in treatment since the original weights were determined in 1984.

data.frame: Charlson scores from data frame of visits and ICD-9 codes

mydf <- data.frame(visitId = c("a", "b", "c"),
                   icd9 = c("441", "412.93", "044.9"))
cmb <- icd9ComorbidQuanDeyo(mydf, isShort = FALSE, applyHierarchy = TRUE)
cmb
icd9Charlson(mydf, isShort = FALSE)
icd9Charlson(mydf, isShort = FALSE, return.df = TRUE)
icd9CharlsonComorbid(cmb)

The icd9 package is now deprecated. The new 'icd' package is on CRAN and has ICD-10 support and bug-fixes. All the functions available in 'icd9' have been preserved in 'icd', but a simpler set of function names is also available.

To install it, use:
install.packages("icd")
# or for the development version:
devtools::install_github("jackwasey/icd")

Then:
remove.packages("icd9")

     MI   CHF   PVD Stroke Dementia Pulmonary Rheumatic   PUD LiverMild    DM
a FALSE FALSE  TRUE  FALSE    FALSE     FALSE     FALSE FALSE     FALSE FALSE
b  TRUE FALSE FALSE  FALSE    FALSE     FALSE     FALSE FALSE     FALSE FALSE
c FALSE FALSE FALSE  FALSE    FALSE     FALSE     FALSE FALSE     FALSE FALSE
   DMcx Paralysis Renal Cancer LiverSevere  Mets   HIV
a FALSE     FALSE FALSE  FALSE       FALSE FALSE FALSE
b FALSE     FALSE FALSE  FALSE       FALSE FALSE FALSE
c FALSE     FALSE FALSE  FALSE       FALSE FALSE  TRUE
a b c 
1 1 6 
  visitId Charlson
1       a        1
2       b        1
3       c        6
a b c 
1 1 6