icd

Comorbidities from ICD-9 and ICD-10 codes, manipulation and validation

Introduction

Calculate comorbidities, Charlson and van Walraven scores, perform fast and accurate validation, conversion, manipulation, filtering and comparison of ICD-9 and ICD-10 codes. This package enables a work flow from raw lists of ICD codes in hospital databases to comorbidities. ICD-9 and ICD-10 comorbidity mappings from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ included. Common ambiguities and code formats are handled.

icd is used by many researchers around the world who work in public health, epidemiology, clinical research, nutrition, journalism, health administration and more. I’m grateful for contact from people in these fields for their feedback and code contributions, and I’m pleased to say that icd has been used in works like the Pulitzer finalist work on maternal death by ProPublica.

Features

• find comorbidities of patients based on ICD-9 or ICD-10 codes, e.g. Cancer, Heart Disease
  • several standard mappings of ICD codes to comorbidities are included (Quan, Deyo, Elixhauser, AHRQ, PCCC)
  • very fast assignment of ICD codes to comorbidities (using novel matrix multiplication algorithm and C++ internally)
• use your existing data format, minimizing requirements for pre-processing
• summarize groups of ICD codes in natural language
• Charlson and Van Walraven score calculations
• Hierarchical Condition Codes (HCC) from CMS
• Clinical Classifcations Software (CCS) comorbidities from AHRQ
• Pediatric Complex Chronic Condition comorbidities
• AHRQ ICD-10 procedure code classification
• annual revisions of ICD-9-CM and ICD-10-CM
• correct conversion between different representations of ICD codes, with and without a decimal points, leading and trailing characters (this is not trivial for ICD-9-CM). ICD-9 to ICD-10 cross-walk is not yet implemented
• comprehensive test suite to increase confidence in accurate processing of ICD codes
• all internal ICD and comorbidity data is extracted directly from public data or code, allowing end-to-end reproducibility
• used, tested and benchmarked against other comorbidity calculators on hardware from laptops to big servers

Examples

See also the vignettes and examples embedded in the help for each function for more. Here’s a taste:

# install.packages("icd")
library(icd)

# Typical diagnostic code data, with many-to-many relationship
patient_data
#>   visit_id  icd9
#> 1     1000 40201
#> 2     1000  2258
#> 3     1000  7208
#> 4     1000 25001
#> 5     1001 34400
#> 6     1001  4011
#> 7     1002  4011
#> 8     1000  <NA>

# get comorbidities using Quan's application of Deyo's Charlson comorbidity groups
comorbid_charlson(patient_data)
#>         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

# or go straight to the Charlson scores:
charlson(patient_data)
#> 1000 1001 1002 
#>    2    2    0

# for more examples, see this and other vignettes
vignette("introduction", package = "icd")

Relevance

ICD-9 codes are still in heavy use around the world, particularly in the USA where the ICD-9-CM (Clinical Modification) was in widespread use until the end of 2015. ICD-10 has been used worldwide for reporting cause of death for more than a decade, and ICD-11 is due to be released in 2018. ICD-10-CM is now the primary coding scheme for US hospital admission and discharge diagnoses used for regulatory purposes and billing. A vast amount of electronic patient data is recorded with ICD-9 codes of some kind: this package enables their use in R alongside ICD-10.

Comorbidities

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 codes

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.

ICD-10 codes

ICD-10 has a somewhat simpler format, with consistent use of a letter, then two alphanumeric characters. However, especially for ICD-10-CM, there are a multitude of qualifiers, e.g. specifying recurrence, laterality, which vastly increase the number of possible codes. This package recognizes validity of codes by syntax alone, or whether the codes appear in a canonical list. The current ICD-10-CM master list is the 2016 set. There is no capability of converting between ICD-9 and ICD-10, but comorbidities can be generated from older ICD-9 codes and newer ICD-10 codes in parallel, and the comorbidities can then be compared.

How to get help

Look at the help files for details and examples of almost every function in this package. There are several vignettes showing the main features. Many users have emailed me directly for help, and I’ll do what I can, but it is often better to examine or add to the list of issues so we can help each other. Advanced users may look at the source code, particularly the extensive test suite which exercises all the key functions.

?comorbid
?comorbid_hcc
?explain_code
?is_valid

# first show the list
vignette(package = "icd")
vignette("pccc", package = "icd")

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.

Development version

The latest version is available in github icd, and can be installed with:

    install.packages("devtools")
    devtools::install_github("jackwasey/icd")

Contributing and Building

A substantial amount of code has now been contributed to the package. Contributions of any kind to icd are very welcome. See the [GitHub issues page]](https://github.com/jackwasey/icd/issues) to see open issues and feature requests. Documentation, vignettes and examples are very welcome, especially if accompanied by some real-world data.

To build icd, Rcpp must be compiled from source. This happens automatically on Linux, but on Mac and Windows, the following may sometimes be required, especially after upgrading R itself. This is a limitation of the R build system.

install.packages("Rcpp", type = "source")