Computing comorbidity scores such as the weighted Charlson score
(Charlson, 1987
2018-10-08
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("devtools")devtools::install_github("ellessenne/comorbidity")
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 packagelibrary(comorbidity)# set a seed for reproducibilityset.seed(1)# simulate 50 ICD-10 codes for 5 individualsx <- 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)#> id code#> 1 C838#> 1 H30#> 1 I260#> 1 I469#> 1 K611#> 1 L949#> 1 V09#> 2 B677#> 2 C081#> 2 I446#> 2 K225#> 2 M41#> 2 M430#> 2 T635#> 2 U016
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)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 TRUEall.equal(x1, x2)#> [1] TRUE
Alternatively, you could use the 2009 version:
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 TRUEall.equal(x1, x2)#> [1] "Component \"code\": 29 string mismatches"
ICD-9 codes can be easily simulated too:
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)#> id code#> 1 01161#> 1 2535#> 1 37854#> 1 46451#> 1 83664#> 1 94433#> 1 9711#> 1 E8038#> 1 E8498#> 1 V092#> 2 01092#> 2 01301#> 2 2337#> 2 37033#> 2 67450
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)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:
charlson <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd10")charlson#> id ami chf pvd cevd dementia copd rheumd pud mld diab diabwc hp rend canc#> 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0#> 2 2 0 0 0 0 0 0 0 0 0 0 0 0 1 1#> 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0#> msld metacanc aids score index wscore windex#> 1 0 0 0 1 1-2 1 1-2#> 2 0 0 0 2 1-2 4 3-4#> 3 0 0 0 0 0 0 0
The default is to assume ICD-10 codes:
charlson.default <- comorbidity(x = x, id = "id", code = "code", score = "charlson")all.equal(charlson, charlson.default)#> [1] TRUE
Alternatively, we could compute the Elixhauser score:
elixhauser <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd10")elixhauser#> id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0#> 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1#> 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0#> ld pud aids lymph metacanc solidtum rheumd coag obes wloss fed blane dane#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0#> 2 0 0 0 0 0 1 0 0 0 0 0 0 0#> 3 0 0 0 0 0 0 0 0 0 0 0 0 0#> alcohol drug psycho depre score index wscore windex#> 1 0 0 0 0 1 1-4 4 1-4#> 2 0 0 0 0 2 1-4 13 >=5#> 3 0 0 0 0 0 0 0 0
Conversely, say we have 5 individuals with a total of 100 ICD-9 diagnostic codes:
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:
charlson9 <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd9")charlson9#> id ami chf pvd cevd dementia copd rheumd pud mld diab diabwc hp rend canc#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1#> 2 2 0 0 0 0 0 0 0 0 0 0 1 0 0 1#> 3 3 0 0 0 0 0 0 0 0 0 1 0 0 0 1#> 4 4 1 0 0 0 0 0 0 0 0 0 0 0 0 1#> 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 1#> msld metacanc aids score index wscore windex#> 1 0 0 0 1 1-2 2 1-2#> 2 1 0 0 3 3-4 7 >=5#> 3 0 0 0 2 1-2 3 3-4#> 4 0 0 0 2 1-2 3 3-4#> 5 0 0 0 1 1-2 2 1-2
Alternatively, we could compute the Elixhauser score:
elixhauser9 <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd9")elixhauser9#> id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf#> 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0#> 2 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0#> 3 3 0 0 0 0 0 0 0 0 0 0 0 1 0 0#> 4 4 0 0 0 0 0 0 1 0 0 0 0 0 0 0#> 5 5 0 0 0 1 0 0 0 0 0 0 0 0 0 0#> ld pud aids lymph metacanc solidtum rheumd coag obes wloss fed blane dane#> 1 0 0 0 0 0 1 0 0 0 0 0 0 0#> 2 1 0 0 1 0 1 0 0 0 0 0 0 0#> 3 0 0 0 1 0 0 0 0 0 0 0 0 0#> 4 0 0 0 0 0 1 0 0 0 0 0 0 0#> 5 0 0 0 0 0 1 0 0 0 0 0 0 0#> alcohol drug psycho depre score index wscore windex#> 1 0 0 0 0 1 1-4 7 >=5#> 2 0 1 0 0 5 >=5 7 >=5#> 3 1 0 0 0 3 1-4 2 1-4#> 4 0 0 0 1 3 1-4 1 1-4#> 5 0 0 0 0 2 1-4 13 >=5
If you find comorbidity
useful, please cite it in your publications:
citation("comorbidity")#>#> To cite the comorbidity package in publications, please 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},#> }
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.
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comorbidity
is faster, with a conservative estimated speed-up of >60%;comorbidity
.The score
argument from comorbidity
has been splitted into score
and icd
. For instance, the command comorbidity(x = x, id = "id", code = "code", score = "charlson_icd10")
has to be modified as r comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd10")
. The default value of icd
is icd10
, for ICD-10 codes, and possible values are icd10
and icd9
.
nhds2010
and australia10
datasets, imported from Stata version 15.Bug fix:
comorbidity()
(@corinne-riddell, #5);citation("comorbidity")
now returns a properly formatted entry.comorbidity
sample_diag_icd10()
function renamed back to sample_diag()
, as now can simulate ICD-9-CM codes tooicd10_2009
and icd10_2011
with ICD-10 codes, 2009 and 2011 versions (respectively)sample_diag
is now sample_diag_icd10
and simulates proper ICD-10 codescomorbidity
as it now can compute more than just the Charlson scorecharlson
is marginally fastercharlson
using only base R functionsNEWS.md
file to track changes to the packagetestthat