ICD comorbidities, manipulation and validation
The icd9 package is obsolete and replaced by icd which is backward compatible, and adds ICD-10 support. icd should be available on CRAN soon. icd9 should be uninstalled and replaced by icd.
install.packages("icd", repos = "https://cloud.r-project.org")
- assignment of patients to high level comorbidities based on admission or discharge ICD-9 codes
- several mappings of ICD-9 codes to comorbidities are included (Quan, Deyo, Elixhauser, AHRQ)
- very fast assignment of ICD-9 codes to comorbidities (using C and C++ internally, with automatic parallel execution using OpenMP when available)
- Charlson and Van Walvaren score calculations
- validation of ICD-9 codes from different annual revisions of ICD-9-CM
- summarizing ICD-9 codes into groups, and to human-readable descriptions
- correct conversion between different representations of ICD-9 codes, with and without a decimal point
- comprehensive test suite to increase confidence in accurate processing of ICD-9 codes.
New since last CRAN release:
- further performance increases: 1 million ICD-9 codes assigned to comorbidities in less than a second
- logical matrix or data.frame for comorbidity output and manipulation
- see NEWS.md and github changelog for more details
- minor update to fix an obscure memory leak found with address sanitizer.
Calculate comorbidities, Charlson scores, perform fast and accurate validation, conversion, manipulation, filtering and comparison of ICD-9-CM (clinical modification) codes. ICD-9 codes appear numeric but leading and trailing zeroes, and both decimal and non-decimal "short" format codes exist. The package enables a work flow from raw lists of ICD-9 codes from hospital billing databases to comorbidities. ICD-9 to comorbidity mappings from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ included. Any other mapping of codes, such as ICD-10, to comorbidities can be used.
ICD-9 codes are still in heavy use around the world, particularly in the USA where the ICD-9-CM (Clinical Modification) is in widespread use. ICD-10 and the corresponding ICD-10-CM are imminent, however a vast amount of patient data is recorded with ICD-9 codes of some kind: this package enables their use in R. A common requirement for medical research involving patients is determining new or existing comorbidities. This is often reported in Table 1 of research papers to demonstrate the similarity or differences of groups of patients. This package is focussed on fast and accurate generation of this comorbidity information from raw lists of ICD-9 codes.
ICD-9 code types
ICD-9 codes are not numbers, and great care is needed when matching individual codes and ranges of codes. It is easy to make mistakes, hence the need for this package. ICD-9 codes can be presented in short 5 character format, or decimal format, with a decimal place separating the code into two groups. There are also codes beginning with V and E which have different validation rules. Zeroes after a decimal place are meaningful, so numeric ICD-9 codes cannot be used in most cases. In addition, most clinical databases contain invalid codes, and even decimal and non-decimal format codes in different places. This package primarily deals with ICD-9-CM (Clinical Modification) codes, but should be applicable or easily extendible to the original WHO ICD-9 system.
See the vignette and code help for many more. Here's a taste:
patientData #> visitId icd9 poa #> 1 1000 40201 Y #> 2 1000 2258 <NA> #> 3 1000 7208 N #> 4 1000 25001 Y #> 5 1001 34400 X #> 6 1001 4011 Y #> 7 1002 4011 E # reformat input data as needed patientData %>% icd9LongToWide # everything works well with magrittr %>% #> [,1] [,2] [,3] [,4] #> 1000 "40201" "2258" "7208" "25001" #> 1001 "34400" "4011" NA NA #> 1002 "4011" NA NA NA # get comorbidities: icd9ComorbidQuanDeyo(patientData) #> MI CHF PVD Stroke Dementia Pulmonary Rheumatic PUD LiverMild #> 1000 FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> 1001 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> 1002 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> DM DMcx Paralysis Renal Cancer LiverSevere Mets HIV #> 1000 TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> 1001 FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE #> 1002 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE # find diagnoses present on admission: icd9FilterPoa(patientData) #> visitId icd9 #> 1 1000 40201 #> 4 1000 25001 #> 6 1001 4011
Note that reformatting from wide to long and back is not as straightforward as using the various Hadley Wickham tools for doing this: knowing the more detailed structure of the data let's us do this better for the case of dealing with ICD codes.
The latest version is available in github and can be installed with:
install.packages("devtools") # if needed devtools::install_github("jackwasey/icd9") install.packages("magrittr") # recommended, but not required
The master branch at github should always build and pass all tests and R CMD check, and will be similar or identical to the most recent CRAN release. The CRAN releases are stable milestones. Contributions and bug reports are encouraged.