Last updated: 2024-07-13 21:12:35.086616
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.
comorbidity
is on CRAN. You can install it as usual
with:
install.packages("comorbidity")
Alternatively, you can install the development version from GitHub with:
# install.packages("remotes")
::install_github("ellessenne/comorbidity") remotes
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:
# load the comorbidity package
library(comorbidity)
# set a seed for reproducibility
set.seed(1)
# simulate 50 ICD-10 codes for 5 individuals
<- data.frame(
x id = sample(1:5, size = 50, replace = TRUE),
code = sample_diag(n = 50)
)<- x[order(x$id, x$code), ]
x print(head(x, n = 15), row.names = FALSE)
## id code
## 1 B02
## 1 B582
## 1 I749
## 1 J450
## 1 L893
## 1 Q113
## 1 Q26
## 1 Q978
## 1 T224
## 1 V101
## 1 V244
## 1 V46
## 2 A665
## 2 C843
## 2 D838
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:
set.seed(1)
<- data.frame(
x1 id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30)
)set.seed(1)
<- data.frame(
x2 id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2011")
)# should return TRUE
all.equal(x1, x2)
## [1] TRUE
Alternatively, you could use the 2009 version:
set.seed(1)
<- data.frame(
x1 id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2009")
)set.seed(1)
<- data.frame(
x2 id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD10_2011")
)# should not return TRUE
all.equal(x1, x2)
## [1] "Component \"code\": 30 string mismatches"
ICD-9 codes can be easily simulated too:
set.seed(2)
<- data.frame(
x9 id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30, version = "ICD9_2015")
)<- x9[order(x9$id, x9$code), ]
x9 print(head(x9, n = 15), row.names = FALSE)
## id code
## 1 01130
## 1 01780
## 1 30151
## 1 3073
## 1 36907
## 1 37845
## 1 64212
## 1 66704
## 1 72633
## 1 9689
## 1 V289
## 2 0502
## 2 09169
## 2 20046
## 2 25082
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:
set.seed(1)
<- data.frame(
x id = sample(1:3, size = 30, replace = TRUE),
code = sample_diag(n = 30)
)
We could compute the Charlson comorbidity domains:
<- comorbidity(x = x, id = "id", code = "code", map = "charlson_icd10_quan", assign0 = FALSE)
charlson
charlson## id mi chf pvd cevd dementia cpd rheumd pud mld diab diabwc hp rend canc msld metacanc aids
## 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
## 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
We set the assign0
argument to FALSE
to not
apply a hierarchy of comorbidity codes, as described in
?comorbidity::comorbidity
.
Alternatively, we could compute the Elixhauser score:
<- comorbidity(x = x, id = "id", code = "code", map = "elixhauser_icd10_quan", assign0 = FALSE)
elixhauser
elixhauser## id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf ld pud aids lymph
## 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 2 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## metacanc solidtum rheumd coag obes wloss fed blane dane alcohol drug psycho depre
## 1 0 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 1 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0
Weighted an unweighted comorbidity scores can be obtained using the
score()
function:
<- score(charlson, weights = NULL, assign0 = FALSE)
unw_cci
unw_cci## [1] 2 1 0
## attr(,"map")
## [1] "charlson_icd10_quan"
<- score(charlson, weights = "quan", assign0 = FALSE)
quan_cci
quan_cci## [1] 6 2 0
## attr(,"map")
## [1] "charlson_icd10_quan"
## attr(,"weights")
## [1] "quan"
all.equal(unw_cci, quan_cci)
## [1] "Attributes: < Length mismatch: comparison on first 1 components >"
## [2] "Mean relative difference: 1.666667"
Code for the Elixhauser score is omitted, but works analogously.
Conversely, say we have 5 individuals with a total of 100 ICD-9 diagnostic codes:
set.seed(3)
<- data.frame(
x id = sample(1:5, size = 100, replace = TRUE),
code = sample_diag(n = 100, version = "ICD9_2015")
)
The Charlson and Elixhauser comorbidity codes can be easily computed once again:
<- comorbidity(x = x, id = "id", code = "code", map = "charlson_icd9_quan", assign0 = FALSE)
charlson9
charlson9## id mi chf pvd cevd dementia cpd rheumd pud mld diab diabwc hp rend canc msld metacanc aids
## 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 2 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 3 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 4 4 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0
## 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
<- comorbidity(x = x, id = "id", code = "code", map = "elixhauser_icd9_quan", assign0 = FALSE)
elixhauser9
elixhauser9## id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf ld pud aids lymph
## 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 4 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## metacanc solidtum rheumd coag obes wloss fed blane dane alcohol drug psycho depre
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 1 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 1 0 0 0 0 0 0 0 0 0 0
Scores:
<- score(elixhauser9, weights = NULL, assign0 = FALSE)
unw_eci <- score(elixhauser9, weights = "vw", assign0 = FALSE)
vw_eci all.equal(unw_eci, vw_eci)
## [1] "Attributes: < Length mismatch: comparison on first 1 components >"
## [2] "Mean relative difference: 2"
If you find comorbidity
useful, please cite it in your
publications:
citation("comorbidity")
## To cite package 'comorbidity' in publications use:
##
## Gasparini, (2018). comorbidity: An R package for computing comorbidity scores. Journal
## of Open Source Software, 3(23), 648, https://doi.org/10.21105/joss.00648
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## author = {Alessandro Gasparini},
## title = {comorbidity: An R package for computing comorbidity scores},
## journal = {Journal of Open Source Software},
## year = {2018},
## volume = {3},
## issue = {23},
## pages = {648},
## doi = {10.21105/joss.00648},
## url = {https://doi.org/10.21105/joss.00648},
## }
More details on which comorbidity mapping and scoring algorithm are available within the package can be found in the two accompanying vignettes, which can be accessed on CRAN or directly from your R session:
vignette("A-introduction", package = "comorbidity")
vignette("B-comorbidity-scores", package = "comorbidity")
The list of available algorithms can be printed interactively using
the available_algorithms()
function:
available_algorithms()
## Supported comorbidity mapping algorithms:
## * charlson_icd9_quan
## * charlson_icd10_quan
## * charlson_icd10_se
## * charlson_icd10_am
## * charlson_icd10_am_ucodes
## * elixhauser_icd9_quan
## * elixhauser_icd10_quan
##
## Supported scoring weights algorithms:
## * For charlson_icd9_quan: charlson, quan
## * For charlson_icd10_quan: charlson, quan
## * For charlson_icd10_se: charlson, quan
## * For charlson_icd10_am: charlson, quan
## * For charlson_icd10_am_ucodes: charlson, quan
## * For elixhauser_icd9_quan: vw, swiss
## * For elixhauser_icd10_quan: vw, swiss
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