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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE
)
options(width = 100)
```
# comorbidity <img src="man/figures/hex.png" width = "150" align="right" />
`r Sys.Date()`
[](https://ci.appveyor.com/project/ellessenne/comorbidity)
[](https://travis-ci.org/ellessenne/comorbidity)
[](https://codecov.io/github/ellessenne/comorbidity?branch=master)
[](https://cran.r-project.org/package=comorbidity)
[](https://cran.r-project.org/package=comorbidity)
[](https://cran.r-project.org/package=comorbidity)
[](https://doi.org/10.21105/joss.00648)
[](https://zenodo.org/badge/latestdoi/68221970)
[](http://makeapullrequest.com)
`comorbidity` is an R package for computing comorbidity scores such as the weighted Charlson score and the Elixhauser comorbidity score; both ICD-10 and ICD-9 coding systems are supported.
## Installation
`comorbidity` is on CRAN. You can install it as usual with:
```{r cran-installation, eval = FALSE}
install.packages("comorbidity")
```
Alternatively, you can install the development version from GitHub with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("ellessenne/comorbidity")
```
## Simulating ICD-10 codes
The `comorbidity` packages includes a function named `sample_diag()` that allows simulating ICD diagnostic codes in a straightforward way. For instance, we could simulate ICD-10 codes:
```{r simulate-data}
# load the comorbidity package
library(comorbidity)
# set a seed for reproducibility
set.seed(1)
# simulate 50 ICD-10 codes for 5 individuals
x <- data.frame(
id = sample(1:5, size = 50, replace = TRUE),
code = sample_diag(n = 50),
stringsAsFactors = FALSE
)
x <- x[order(x$id, x$code), ]
print(head(x, n = 15), row.names = FALSE)
```
It is also possible to simulate from two different versions of the ICD-10 coding system. The default is to simulate ICD-10 codes from the 2011 version:
```{r simulate-data-2011}
set.seed(1)
x1 <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30),
stringsAsFactors = FALSE
)
set.seed(1)
x2 <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2011"),
stringsAsFactors = FALSE
)
# should return TRUE
all.equal(x1, x2)
```
Alternatively, you could use the 2009 version:
```{r simulate-data-2009}
set.seed(1)
x1 <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2009"),
stringsAsFactors = FALSE
)
set.seed(1)
x2 <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2011"),
stringsAsFactors = FALSE
)
# should not return TRUE
all.equal(x1, x2)
```
## Simulating ICD-9 codes
ICD-9 codes can be easily simulated too:
```{r simulate-data-icd9}
set.seed(2)
x9 <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD9_2015"),
stringsAsFactors = FALSE
)
x9 <- x9[order(x9$id, x9$code), ]
print(head(x9, n = 15), row.names = FALSE)
```
## Computing comorbidity scores
The main function of the `comorbidity` package is named `comorbidity()`, and it can be used to compute any supported comorbidity score; scores can be specified by setting the `score` argument, which is required.
Say we have 3 individuals with a total of 30 ICD-10 diagnostic codes:
```{r simulate-data-cs}
set.seed(1)
x <- data.frame(
id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30),
stringsAsFactors = FALSE
)
```
We could compute the Charlson score, index, and each comorbidity domain:
```{r charlson}
charlson <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd10", assign0 = FALSE)
charlson
```
We set the `assign0` argument to `FALSE` to not apply a hierarchy of comorbidity codes, as described in `?comorbidity::comorbidity`. The default is to assume ICD-10 codes are passed to `comorbidity`:
```{r charlson-2}
charlson.default <- comorbidity(x = x, id = "id", code = "code", score = "charlson", assign0 = FALSE)
all.equal(charlson, charlson.default)
```
Alternatively, we could compute the Elixhauser score:
```{r elixhauser}
elixhauser <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd10", assign0 = FALSE)
elixhauser
```
Conversely, say we have 5 individuals with a total of 100 ICD-9 diagnostic codes:
```{r simulate-data-cs-9}
set.seed(3)
x <- data.frame(
id = sample(1:5, size = 100, replace = TRUE),
code = sample_diag(n = 100, version = "ICD9_2015"),
stringsAsFactors = FALSE
)
```
The Charlson and Elixhauser comorbidity codes can be easily computed:
We could compute the Charlson score, index, and each comorbidity domain:
```{r charlson-9}
charlson9 <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd9", assign0 = FALSE)
charlson9
```
Alternatively, we could compute the Elixhauser score:
```{r elixhauser-9}
elixhauser9 <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd9", assign0 = FALSE)
elixhauser9
```
The weighted Elixhauser score is computed using both the AHRQ and the van Walraven algorithm (`wscore_ahrq` and `wscore_vw`).
## Citation
If you find `comorbidity` useful, please cite it in your publications:
```{r citation}
citation("comorbidity")
```
## References
This package is based on the ICD-10-based formulations of the Charlson score and Elixhauser score proposed by Quan _et al_. in 2005.
The ICD-9 formulation of the Charlson score is also from Quan _et al_.
The ICD-9-based Elixhauser score is according to the AHRQ formulation (Moore _et al_., 2017).
Weights for the Charlson score are based on the original formulation by Charlson _et al_. in 1987, while weights for the Elixhauser score are based on work by van Walraven _et al_.
Finally, the categorisation of scores and weighted scores is based on work by Menendez _et al_.
Further details on each algorithm are included in the package vignette, which you can access by typing the following in the R console:
```{r vignette, eval = FALSE}
vignette("comorbidityscores", package = "comorbidity")
```
* Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. _Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data_. Medical Care 2005; 43(11):1130-1139. DOI: [10.1097/01.mlr.0000182534.19832.83](https://doi.org/10.1097/01.mlr.0000182534.19832.83)
* Charlson ME, Pompei P, Ales KL, et al. _A new method of classifying prognostic comorbidity in longitudinal studies: development and validation_. Journal of Chronic Diseases 1987; 40:373-383. DOI: [10.1016/0021-9681(87)90171-8](https://doi.org/10.1016/0021-9681(87)90171-8)
* Moore BJ, White S, Washington R, Coenen N, and Elixhauser A. _Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ Elixhauser comorbidity index_. Medical Care 2017; 55(7):698-705. DOI: [10.1097/MLR.0000000000000735](https://doi.org/10.1097/MLR.0000000000000735)
* Elixhauser A, Steiner C, Harris DR and Coffey RM. _Comorbidity measures for use with administrative data_. Medical Care 1998; 36(1):8-27. DOI: [10.1097/00005650-199801000-00004 ](https://doi.org/10.1097/00005650-199801000-00004 )
* van Walraven C, Austin PC, Jennings A, Quan H and Forster AJ. _A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data_. Medical Care 2009; 47(6):626-633. DOI: [10.1097/mlr.0b013e31819432e5](https://doi.org/10.1097/mlr.0b013e31819432e5)
* Menendez ME, Neuhaus V, van Dijk CN, Ring D. _The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery_. Clinical Orthopaedics and Related Research 2014; 472(9):2878-2886. DOI: [10.1007/s11999-014-3686-7](https://doi.org/10.1007/s11999-014-3686-7)
## Copyright
The icon for the hex sticker was made by [monkik](https://www.flaticon.com/authors/monkik) from [www.flaticon.com](https://www.flaticon.com), and is licensed by [Creative Commons BY 3.0](http://creativecommons.org/licenses/by/3.0).