Many problems with clinical data are best solved using the standard and widely validated comorbidities, mapped to subsets of ICD-9 or ICD-10 codes, as published by authors including Elixhauser, Quan, Deyo and the AHRQ. It is also common to have a pre-defined specific set of ICD codes. Examples of this include:

Studying sub-types of a common comorbidity, such as obesity: although obesity is in the AHRQ and Elixhauser mappings, there is no granularity.
Studying a particular disease: most diseases are not in the standard comorbidity maps. To distinguish patients having or not having a particular set of diseases is usually not possible using the standard comorbidity maps. E.g. Which patients have eczema? Which emergency room patients presented with a sexual health problem?

icd has a simple mechanism to use custom category-ICD maps. A comorbidity map is a list of character vectors. Each list must be named to reflect the ICD codes it contains; the character vector contains the ICD codes themselves.

Let’s take a look at the first few items in the Charlson map from Quan and Deyo:

print(icd::icd10_map_quan_deyo, n_comorbidities = 3, n_codes = 8)
#> Showing first 3 comorbidities, and first 8 of each.
#> $MI
#> [1] "I21"   "I210"  "I2101" "I2102" "I2109" "I211"  "I2111" "I2119"
#> 
#> $CHF
#> [1] "I099" "I110" "I130" "I132" "I255" "I420" "I425" "I426"
#> 
#> $PVD
#> [1] "I70"    "I700"   "I701"   "I702"   "I7020"  "I70201" "I70202" "I70203"
#> 
#> ...

Obesity example

Now we can make our own comorbidity map, and apply it to a question on ‘Stackoverflow’.

library(icd)
obesity_map <- list(
  "1" = "E663",
  "2" = c("E669", "E668", "E6609"),
  "3" = "E661",
  "4" = "E662")

obesity <- data.frame(
  ICD.10.Code = c("E6601", "E663", "E663", "E6609"),
  Encounter.ID = c("408773", "542207", "358741", "342534")
)

custom_map_result <- icd::icd10_comorbid(
  obesity,
  map = obesity_map)

custom_map_result
#>            1     2     3     4
#> 408773 FALSE FALSE FALSE FALSE
#> 542207  TRUE FALSE FALSE FALSE
#> 358741  TRUE FALSE FALSE FALSE
#> 342534 FALSE  TRUE FALSE FALSE

# finally, format as requested by the user
apply(custom_map_result, 1, function(x) {
  if (!any(x)) 4 else which(x)[1]
  })
#> 408773 542207 358741 342534 
#>      4      1      1      2

# see also:
icd::icd10_map_ahrq$Obesity
#>  [1] "E6601"  "E6609"  "E661"   "E662"   "E668"   "E669"   "O99210" "O99211"
#>  [9] "O99212" "O99213" "O99214" "O99215" "R939"   "Z6830"  "Z6831"  "Z6832" 
#> [17] "Z6833"  "Z6834"  "Z6835"  "Z6836"  "Z6837"  "Z6838"  "Z6839"  "Z6841" 
#> [25] "Z6842"  "Z6843"  "Z6844"  "Z6845"  "Z6854"
icd::icd10_map_quan_elix$Obesity
#> [1] "E66"   "E660"  "E6601" "E6609" "E661"  "E662"  "E663"  "E668"  "E669"
icd::icd10_comorbid_quan_elix(
  obesity,
  return_df = TRUE)["Obesity"]
#>   Obesity
#> 1    TRUE
#> 2    TRUE
#> 3    TRUE
#> 4    TRUE

Maps with ranges of codes

Sometimes there are a large number of ICD codes, and they can be defined more succinctly with ranges, then by specifying every single code. In addition, as new codes are added to ICD-10-CM, (especially ICD-10-CM – it moves much faster than ICD-10 from the WHO) having specific hard-coded ICD-10 codes will miss closely related codes in the future. Using the ranges functions from icd helps with both these problems.

Again, using a Stackoverflow question:

library(icd)
diagnoses <- c("C349", "A219", "B003", "C509", "B700", "A090")
one_pt <- data.frame(id = rep("patient1", length(diagnoses)),
                     diagnoses)
dif_pt <- data.frame(id = paste0("patient", seq_along(diagnoses)),
                     diagnoses)
my_map <- list(c01to17 = expand_range("C01", "C17"),
               a74to75 = expand_range("A748", "A759"),
               b00to33 = expand_range("B001", "B331"),
               b69to72 = expand_range("B69", "B72"),
               c00to94 = expand_range("C000", "C942"))
# optionally use as.comorbidity_map which ensures it is valid, and let's it
# print more pleasantly
my_map <- as.comorbidity_map(my_map)
print(my_map)
#> Showing first 7 comorbidities, and first 7 of each.
#> $c01to17
#> [1] "C01"  "C02"  "C020" "C021" "C022" "C023" "C024"
#> 
#> $a74to75
#> [1] "A748"  "A7481" "A7489" "A749"  "A75"   "A750"  "A751" 
#> 
#> $b00to33
#> [1] "B001"  "B002"  "B003"  "B004"  "B005"  "B0050" "B0051"
#> 
#> $b69to72
#> [1] "B69"   "B690"  "B691"  "B698"  "B6981" "B6989" "B699" 
#> 
#> $c00to94
#> [1] "C000" "C001" "C002" "C003" "C004" "C005" "C006"
icd::comorbid(one_pt, map = my_map)
#>          c01to17 a74to75 b00to33 b69to72 c00to94
#> patient1   FALSE   FALSE    TRUE    TRUE    TRUE
(six_pts_cmb <- icd::comorbid(dif_pt, map = my_map))
#>          c01to17 a74to75 b00to33 b69to72 c00to94
#> patient1   FALSE   FALSE   FALSE   FALSE    TRUE
#> patient2   FALSE   FALSE   FALSE   FALSE   FALSE
#> patient3   FALSE   FALSE    TRUE   FALSE   FALSE
#> patient4   FALSE   FALSE   FALSE   FALSE    TRUE
#> patient5   FALSE   FALSE   FALSE    TRUE   FALSE
#> patient6   FALSE   FALSE   FALSE   FALSE   FALSE

Quick custom comorbidity maps are helpful

Jack O. Wasey

2020-06-08

Obesity example

Maps with ranges of codes