title: Comparison of the Injury Severity Score and ICD-9 diagnosis codes as predictors of outcome in injury: analysis of 44,032 patients.
abstract: BACKGROUND
  Appropriate stratification of injury severity is a critical tool in the assessment of the treatment and the prevention of injury. Since its inception, the Injury Severity Score (ISS) has been the generally recognized "gold standard" for anatomic injury severity assessment. However, there is considerable time and expense involved in the collection of the information required to calculate an accurate ISS. In addition, the predictive power of the ISS has been shown to be limited. Previous work has demonstrated that the anatomic information about injury contained in the International Classification of Diseases Version 9 (ICD-9) can be a significant predictor of survival in trauma patients. The goal of this study was to utilize the San Diego County Trauma Registry (SDTR), one of the nation's leading trauma registries, to compare the predictive power of the ISS with the predictive power of the information contained in the injured patients' ICD-9 diagnoses codes. It was our primary hypothesis that survival risk ratios derived from patients' ICD-9 diagnoses codes would be equal or better predictors of survival than the Injury Severity Score. The implications of such a finding would have the potential for significant cost savings in the care of injured patients.
  
  
  METHODS
  Data for the test population were obtained from the SDTR, which contains data from 1985 through 1993 from five participating hospitals. Four data sources were utilized to estimate the expected survival rate/mortality rate for each ICD-9 code in the SDTR. These were (1) the SDTR patients themselves, (2) the North Carolina State Hospital Discharge Database, (3) the North Carolina Trauma Registry Database, and (4) the Agency for Health Care Policy Research's Health Care Utilization Project Database. Each of these data sources was separately utilized to develop a survival risk ratio (SRR) for each ICD-9 diagnoses code. The SRR was calculated by dividing the number of survivors for patients with each ICD-9 code by the total number of all patients with the particular ICD-9 diagnoses code. The four groups of SRRs derived from our four data sources were used as predictors of survival and the ability of the SRRs to predict survival was compared with the predictive power of the ISS using measures of accuracy, sensitivity, specificity, and receiver operator characteristic curves.
  
  
  RESULTS
  During the years 1985 through 1993, complete data were available for analysis on 44,032 patients. Of these, 2,848 patients died during their hospitalization (6%). Survival risk ratios were calculated for each of the diagnoses in the data base. Logistic regression, using the SAS System for statistical analysis, was used to assess the relative predictive power of the ISS and the survival risk ratios derived from the ICD-9 diagnoses codes from each of the four data bases. The analyses demonstrated that the regression models using the SRRs were generally as good or better than ISS as predictors of survival. The predictive power of the SRRs derived from the SDTR data, the North Carolina Trauma Registry data and the Health Care Utilization Report data were the best. In a subsequent analysis, the SRR values and the ISS were added to the patient's age and the revised Trauma Scores to create new predictive models in the mode of TRISS methodology. The analyses again indicated that the models using SRRs had as good or better predictive power than the model using the ISS.
  
  
  CONCLUSIONS
  The present study confirms previous work showing that survival risk ratios derived from injured patients' ICD-9 diagnoses codes are as good as or better than ISS as predictors of survival.
authors.affiliation: Department of Surgery, University of North Carolina at Chapel Hill 27599-7210, USA. rrutledg.@med.unc.edu
authors.forename: R
authors.initials: R
authors.lastname: Rutledge
authors.forename: D B
authors.initials: DB
authors.lastname: Hoyt
authors.forename: A B
authors.initials: AB
authors.lastname: Eastman
authors.forename: M J
authors.initials: MJ
authors.lastname: Sise
authors.forename: T
authors.initials: T
authors.lastname: Velky
authors.forename: T
authors.initials: T
authors.lastname: Canty
authors.forename: T
authors.initials: T
authors.lastname: Wachtel
authors.forename: T M
authors.initials: TM
authors.lastname: Osler
mesh_terms: D015998:Abbreviated Injury Scale; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D004194:Disease / Q000145:classification*; D006801:Humans; D007223:Infant; D015601:Injury Severity Score; D016015:Logistic Models; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012042:Registries; D015996:Survival Rate; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000145:classification* / Q000401:mortality