A common use of ICD codes is calculation as a Charlson score, which gives a measure of how well a patient is, albeit based on the limited available in admission and discharge diagnoses. The Charlson scoring system attributes scores based on presence of diseases falling into any of the Charlson comorbidities. Quan updated the scores given to each comorbidity to better reflect morbidity and mortality in a more recent population. Van Walraven provides a similar scoring methodology for the Elixhauser comorbidities (as used by the US AHRQ).
More complicated scoring systems may use lab values, patient demographic information, and so on. Any contributions to this package for calculations of scoring systems based on comorbidities and other data would be welcome.
The Vermont data are actually discharge, not admission diagnoses, but can be used to demonstrate generating Charlson scores.
# typical hospital format data, with many columns for diagnoses
head(vermont_dx)
#> visit_id age_group sex death DRG DX1 DX2 DX3 DX4 DX5
#> 1 7 40-44 male TRUE 640 27801 03842 51881 41519 99591
#> 2 10 75 and over female FALSE 470 71526 25000 42830 4280 4019
#> 3 13 75 and over female FALSE 470 71535 59651 78052 27800 V8537
#> 4 16 55-59 female FALSE 470 71535 49390 53081 27800 V140
#> 5 37 70-74 male FALSE 462 71536 4241 2859 2720 4414
#> 6 41 70-74 male FALSE 462 71536 V1259 V1582 V160 V171
#> DX6 DX7 DX8 DX9 DX10 DX11 DX12 DX13 DX14 DX15 DX16 DX17
#> 1 42842 5849 5609 6826 5853 42731 42732 25542 1533 5693 45340 6822
#> 2 4241 311 49390 2724 73300 41401
#> 3 311 4019 53081 56400
#> 4 V141 V142 V160 V8538
#> 5 53081 V5866
#> 6
#> DX18 DX19 DX20
#> 1 70712 27803 2767
#> 2
#> 3
#> 4
#> 5
#> 6
# convert to long format (could use other tools, but the icd version accounts better for known structure of the data.
head(vermont_dx %>% icd_wide_to_long)
#> visit_id age_group sex death DRG icd_code
#> 1 7 40-44 male TRUE 640 27801
#> 2 7 40-44 male TRUE 640 03842
#> 3 7 40-44 male TRUE 640 51881
#> 4 7 40-44 male TRUE 640 41519
#> 5 7 40-44 male TRUE 640 99591
#> 6 7 40-44 male TRUE 640 42842
# calculate charlson scores and summarize
vermont_dx %>% icd_wide_to_long %>% icd_charlson %>% summary
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.000 0.000 1.000 1.573 2.000 9.000
# show the first few actual scores: the names are the patient IDs:
vermont_dx %>% icd_wide_to_long %>% icd_charlson %>% head(25) -> vermont_charlson
vermont_charlson
#> 7 10 13 16 37 41 42 52 68 70 74 78 81 87 120 136 159 167
#> 5 3 0 1 1 0 0 0 0 0 0 1 2 0 4 0 0 1
#> 168 169 188 242 299 316 321
#> 0 0 0 0 1 0 0
names(vermont_charlson)
#> [1] "7" "10" "13" "16" "37" "41" "42" "52" "68" "70" "74"
#> [12] "78" "81" "87" "120" "136" "159" "167" "168" "169" "188" "242"
#> [23] "299" "316" "321"
unname(vermont_charlson)
#> [1] 5 3 0 1 1 0 0 0 0 0 0 1 2 0 4 0 0 1 0 0 0 0 1 0 0Behind the scenes, icd calculates the Charlson comorbidities for those ICD codes, applies the Charlson scoring system, and returns the Charlson score for each patient.
The same principle can be used to calculate the Van Walraven score, which is the Charlson score counterpart for Elixhauser comorbidities.
vermont_dx %>% icd_wide_to_long %>% icd_van_walraven %>% head(25)