R/cci.R

In caimiao0714/MiaoCom: An R package for calculating comorbidity indexes for epidemiologists

#' cci()
#'
#' To calculate the Charlson Comorbidity index ("cci_1987" 1987 orginal version and cci_2011 the 2011 Quan version)
#' "cci_1987": The Charlson Comorbidity index, developed by Mary E. Charlson in 1987.
#' "cci_2011": The Charlson Comorbidity index, updated by Hude Quan in 2011.
#'
#' @author Miao Cai <email: miao.cai@outlook.com>
#' @description This file aims to calculate the Charlson Comorbidity index(1985 orginal version and the 2011 Quan version)
#' @param data Your data file in which Charlson Comorbidity index is to be calculated
#' @param comorbidity A vector of all comorbidity variables
#' @param age The name of the age variable
#' @importFrom magrittr %>%
#' @importFrom dplyr mutate_if
#' @return data: a new data.frame named "data". This data frame contains two new variables: "cci_1987" & "cci_2011"
#' @references Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases, 40(5), 373-383.
#' @references Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J. C., ... & Ghali, W. A. (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care, 1130-1139.
#' @references Quan, H., Li, B., Couris, C. M., Fushimi, K., Graham, P., Hider, P., ... & Sundararajan, V. (2011). 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, 173(6), 676-682.
#'
#' @export cci
#'
cci <- function(data, comorbidity, age) {
  ### PART A: SUBSETTING DATA---------------------------

  data_comorbidity <- subset(data, select = comorbidity)# subset the whole dataset into "data_comorbidity": subset data that only contains comorbidities
  data_comorbidity %>% dplyr::mutate_if(is.factor, as.character) -> data_comorbidity # this converts factor values into characters
  data_comorbidity[is.na(data_comorbidity)] <- 'NA' #imputate missing values with zeros
  dim_comorbidity <- dim(data_comorbidity)# save the dimensionality of comorbidity subset data
  unlisted_data <- unlist(data_comorbidity)# unlist the comorbidity subset data(converts the data into a vector. Vectorization speeds up the functions)
  unlisted_data <- toupper(unlisted_data)

  ### PART B: FILTERING COMORBIDITIES---------------------------
  ##1:MI, Myocardial Infarction
  bidata_com <- grepl('I25\\.2|I21|I22', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Myocardial Infarction" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$MI <- rowSums(bidata_com)
  data$MI[data$MI >= 1] <- 1

  #2:CCF, Congestive Cardiac Failure
  bidata_com <- grepl('I09\\.9|I11\\.0|I13\\.0|I13\\.2|I25\\.5|I42\\.0|I42\\.5|I42\\.6|I42\\.7|I42\\.8|I42\\.9|P29\\.0|I50|I43', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Congestive Cardiac Failure" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$CCF <- rowSums(bidata_com)
  data$CCF[data$CCF >= 1] <- 1

  #3:PVD, Peripheral Vascular Disease
  bidata_com <- grepl('I73\\.1|I73\\.8|I73\\.9|I77\\.1|I79\\.0|I79\\.2|K55\\.1|K55\\.8|K55\\.9|Z95\\.8|Z95\\.9|I70|I71', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Peripheral Vascular Disease" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$PVD <- rowSums(bidata_com)
  data$PVD[data$PVD >= 1] <- 1

  #4:CD, Cerebrovascular Disease
  bidata_com <- grepl('H34\\.0|I60|I61|I62|I63|I64|I65|I66|I67|I68|I69|G45|G46', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Cerebrovascular Disease" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$CD <- rowSums(bidata_com)
  data$CD[data$CD >= 1] <- 1

  #5:Dementia
  bidata_com <- grepl('F05\\.1|G31\\.1|F00|F01|F02|F03|G30', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Dementia" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$Dementia <- rowSums(bidata_com)
  data$Dementia[data$Dementia >= 1] <- 1

  #6:COPD, chronic obstructive pulmonary disease
  bidata_com <- grepl('I27\\.8|I27\\.9|J68\\.4|J70\\.1|J70\\.3|J40|J41|J42|J43|J44|J45|J46|J47|J60|J61|J62|J63|J64|J65|J66|J67', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "COPD" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$COPD <- rowSums(bidata_com)
  data$COPD[data$COPD >= 1] <- 1

  #7:RD - Connective tissue disease
  bidata_com <- grepl('M31\\.5|M35\\.1|M35\\.3|M36\\.0|M32|M33|M34|M05|M06', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "RD" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$RD <- rowSums(bidata_com)
  data$RD[data$RD >= 1] <- 1

  #8:PUD, Ulcers
  bidata_com <- grepl('K25|K26|K27|K28', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "PUD" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$PUD <- rowSums(bidata_com)
  data$PUD[data$PUD >= 1] <- 1

  #9:MLD, Mild liver disease
  bidata_com <- grepl('K70\\.0|K70\\.1|K70\\.2|K70\\.3|K70\\.9|K71\\.3|K71\\.4|K71\\.5|K71\\.7|K76\\.0|K76\\.2|K76\\.3|K76\\.4|K76\\.8|K76\\.9|Z94\\.4|B18|K73|K74', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "MLD" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$MLD <- rowSums(bidata_com)
  data$MLD[data$MLD >= 1] <- 1

  #10:DMandEOD (with end-organ damage)
  bidata_com <- grepl('E10\\.2|E10\\.3|E10\\.4|E10\\.5|E10\\.7|E11\\.2|E11\\.3|E11\\.4|E11\\.5|E11\\.7|E12\\.2|E12\\.3|E12\\.4|E12\\.5|E12\\.7|E13\\.2|E13\\.3|E13\\.4|E13\\.5|E13\\.7|E14\\.2|E14\\.3|E14\\.4|E14\\.5|E14\\.7', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "DMandEOD (with end-organ damage)" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$DMandEOD <- rowSums(bidata_com)
  data$DMandEOD[data$DMandEOD >= 1] <- 1

  #11:DMnotEOD (without end-organ damage)
  bidata_com <- grepl('E10\\.0|E10\\.1|E10\\.6|E10\\.8|E10\\.9|E11\\.0|E11\\.1|E11\\.6|E11\\.8|E11\\.9|E12\\.0|E12\\.1|E12\\.6|E12\\.8|E12\\.9|E13\\.0|E13\\.1|E13\\.6|E13\\.8|E13\\.9|E14\\.0|E14\\.1|E14\\.6|E14\\.8|E14\\.9', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "DMnotEOD (without end-organ damage)" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$DMnotEOD <- rowSums(bidata_com)
  data$DMnotEOD[data$DMnotEOD >= 1] <- 1

  #12: Hemiplegia
  bidata_com <- grepl('G04\\.1|G11\\.4|G80\\.1|G80\\.2|G83\\.0|G83\\.1|G83\\.2|G83\\.4|G83\\.9|G81|G82', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Hemiplegia" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$Hemiplegia <- rowSums(bidata_com)
  data$Hemiplegia[data$Hemiplegia >= 1] <- 1

  #13:MSCKD, Moderate to Severe Chronic Kidney Disease
  bidata_com <- grepl('I12\\.0|I13\\.1|N03\\.2|N03\\.3|N03\\.4|N03\\.5|N03\\.6|N03\\.7|N05\\.2|N05\\.3|N05\\.4|N05\\.5|N05\\.6|N05\\.7|N25\\.0|Z49\\.0|Z49\\.1|Z49\\.2|Z94\\.0|Z99\\.2|N18|N19', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "MSCKD" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$MSCKD <- rowSums(bidata_com)
  data$MSCKD[data$MSCKD >= 1] <- 1

  #14:Malignancy, Maligant Tumor
  bidata_com <- grepl('C00|C01|C02|C03|C04|C05|C06|C07|C08|C09|C10|C11|C12|C13|C14|C15|C16|C17|C18|C19|C20|C21|C22|C23|C24|C25|C26|C30|C31|C32|C33|C34|C37|C38|C39|C40|C41|C43|C45|C46|C47|C48|C49|C50|C51|C52|C53|C54|C55|C56|C57|C58|C60|C61|C62|C63|C64|C65|C66|C67|C68|C69|C70|C71|C72|C73|C74|C75|C76|C81|C82|C83|C84|C85|C88|C90|C91|C92|C93|C94|C95|C96|C97', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Maligant Tumor" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$Malignancy <- rowSums(bidata_com)
  data$Malignancy[data$Malignancy >= 1] <- 1

  #15:MSLD, Moderate–severe liver disease
  bidata_com <- grepl('I85\\.0|I85\\.9|I86\\.4|I98\\.2|K70\\.4|K71\\.1|K72\\.1|K72\\.9|K76\\.5|K76\\.6|K76\\.7', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Moderate–severe liver disease" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$MSLD <- rowSums(bidata_com)
  data$MSLD[data$MSLD >= 1] <- 1

  #16:MST, Metastatic solid tumour
  bidata_com <- grepl('C77|C78|C79|C80', unlisted_data, perl = TRUE, ignore.case = TRUE) # regular expression to find out cases with "Metastatic solid tumour" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$MST <- rowSums(bidata_com)
  data$MST[data$MST >= 1] <- 1

  #17: AIDS
  bidata_com <- grepl('B20|B21|B22|B24', unlisted_data, perl = TRUE, ignore.case = TRUE)# regular expression to find out cases with "AIDS" comorbidity ICD-10 codes
  dim(bidata_com) <- dim_comorbidity # convert the comorbidity data vector back into data.frame
  data$AIDS <- rowSums(bidata_com)
  data$AIDS[data$AIDS >= 1] <- 1

  ###PART C---AGE---------------------------
  # About age: There are a variety of calculating weights for patient ages. In the orginal 1987 paper, it wrote
  # "Using this approach, a patient 40 yr of age would be assumed to have no risk of comorbid death attributable to age and a patient with a comorbidity index score of 0 would have no risk attributable to pre-existing comorbid disease. Each decade of  age over 40 would add 1 point to risk (i.e. 50 yr, 1; 60 yr, 2; 70 yr 3; etc.) and the "age points" would be added to the score from the  comorbidity index (i.e. 0, 1, 2, 3, etc.)."
  # Therefore, we use this approach for my algorithm
  data$age_group <- 0
  data$age_group[data[[age]] <= 59 & data[[age]] >= 50] <- 1
  data$age_group[data[[age]] <= 69 & data[[age]] >= 60] <- 2
  data$age_group[data[[age]] <= 79 & data[[age]] >= 70] <- 3
  data$age_group[data[[age]] >= 80] <- 4

  ###PART D---WEIGHTED SUM---------------------------
  #1:CCI_1987, by Mary E. Charlson in 1987
  # Reference: Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of chronic diseases, 40(5), 373-383.
  data$CCI_1987  <- data$MI + data$CCF + data$PVD + data$CD + data$Dementia + data$COPD + data$RD + data$PUD + data$MLD + data$DMnotEOD + data$DMandEOD * 2 + data$Hemiplegia * 2 + data$MSCKD * 2 + data$Malignancy * 2 + data$MSLD * 3 + data$MST * 6 + data$AIDS * 6 + data$age_group


  #2:CCI_2011, weights were updated by Hude Quan et al.
  #Reference:
  data$CCI_2011 <- data$CCF * 2 + data$Dementia * 2 + data$COPD + data$RD + data$MLD *
    2 + data$DMandEOD + data$Hemiplegia * 2 + data$MSCKD + data$Malignancy *
    2 + data$MSLD * 4 + data$MST * 6 + data$AIDS * 4 + data$age_group

  # All previous ICD-10 coding algorithms were refered to:
  # Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J. C., ... & Ghali, W. A. (2005). Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care, 1130-1139.

  return(data)
}