# Introduction

#### Alessandro Gasparini

#### 2023-05-01

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.

# Mapping Comorbidities

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).

    length(unique(df$id)) == nrow(charlson_df)
    #> [1] TRUE

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:

    vignette("B-comorbidity-scores", package = "comorbidity")

# Comorbidity Scores

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 25

This returns a single value per subject:

    length(quan_cci) == nrow(charlson_df)
    #> [1] TRUE

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 25

Once again, available weighting systems/algorithms are described in the same vignette that was mentioned above.

# References

- 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