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This vignette illustrates how to use the {comorbidity} package to identify comorbid conditions and to compute weighted (or unweighted) comorbidity scores.
For this, we will simulate a dataset with 100 patients and 10000
ICD-10 codes using the sample_diag() function:
library(comorbidity)
set.seed(1)
df <- data.frame(
id = sample(seq(100), size = 10000, replace = TRUE),
code = sample_diag(n = 100)
)
# Sort
df <- df[order(df$id, df$code), ]
str(df)
#> 'data.frame': 10000 obs. of 2 variables:
#> $ id : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ code: chr "A671" "B170" "C33" "C33" ...By default, the sample_diag() function simulates ICD-10
data; it is however possible to simulate ICD-9 codes too, as we will see
later on.
The comorbidity() function can be used to apply mapping
algorithms to a dataset. Here, for instance, we use the Quan et
al. (2005) version of the Charlson Comorbidity Index:
charlson_df <- comorbidity(
x = df,
id = "id",
code = "code",
map = "charlson_icd10_quan",
assign0 = FALSE
)
str(charlson_df)
#> Classes 'comorbidity' and 'data.frame': 100 obs. of 18 variables:
#> $ id : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ mi : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ chf : int 0 1 1 0 0 1 1 0 1 1 ...
#> $ pvd : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ cevd : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ dementia: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ cpd : int 1 1 1 1 0 0 1 1 1 1 ...
#> $ rheumd : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ pud : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ mld : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ diab : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ diabwc : int 1 0 1 1 1 1 1 1 1 1 ...
#> $ hp : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ rend : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ canc : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ msld : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ metacanc: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ aids : int 0 0 0 0 0 0 0 0 0 0 ...
#> - attr(*, "variable.labels")= chr [1:18] "ID" "Myocardial infarction" "Congestive heart failure" "Peripheral vascular disease" ...
#> - attr(*, "map")= chr "charlson_icd10_quan"The resulting data frame has a row per subject, a column for IDs, and a column for each condition included in a given score (e.g. 17 conditions for the Charlson score).
The different columns are also labelled for compatibility with the
RStudio viewer, see e.g. View(charlson_df) after running
the code above on your computer.
To see all supported mapping algorithms, please see the vignette:
After calculating a data frame of comorbid conditions, that can be
used to calculate comorbidity scores using the score()
function. Say we want to calculate the Charlson comorbidity score,
weighted using the Quan et al. (2011) weights:
quan_cci <- score(x = charlson_df, weights = "quan", assign0 = FALSE)
table(quan_cci)
#> quan_cci
#> 2 3 4 5 6
#> 7 18 20 30 25This returns a single value per subject:
If a pure combination of conditions is required (e.g. an unweighted
score), pass the NULL value to the weights
argument of the score() function:
unw_cci <- score(x = charlson_df, weights = NULL, assign0 = FALSE)
table(unw_cci)
#> unw_cci
#> 1 2 3 4
#> 7 25 43 25Once again, available weighting systems/algorithms are described in the same vignette that was mentioned above.
Charlson ME 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
Quan H 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
Quan H et al. Updating and validating the Charlson Comorbidity Index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. American Journal of Epidemiology 2011;173(6):676-82. DOI: 10.1093/aje/kwq433
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They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.