Citation

Cook A, Weddle J, Baker S, Hosmer D, Glance LG, Friedman L, Osler T. J. Trauma Acute Care Surg. 2014; 76(1): 47-53.

Affiliation

From the Baylor University Medical Center (A.C., J.W.) Department of Surgery, Trauma Division, Dallas, Texas; Johns Hopkins Bloomberg School of Public Health (S.B.), Department of Health Policy and Management, Baltimore, Maryland; University of Massachusetts School of Public Health and Health Sciences, Division of Biostatistics and Epidemiology (D.H.), Amherst, Massachusetts; University of Rochester School of Medicine (L.G.), Department of Anesthesiology, Rochester, New York; University of Illinois at Chicago (L.F.), School of Public Health, Chicago, Illinois; University of Vermont College of Medicine (T.O.), Department of Surgery, Burlington, Vermont.

Copyright

(Copyright © 2014, Lippincott Williams and Wilkins)

DOI

10.1097/TA.0b013e3182ab0d5d

PMID

24368356

Abstract

BACKGROUND: Performance benchmarking requires accurate measurement of injury severity. Despite its shortcomings, the Injury Severity Score (ISS) remains the industry standard 40 years after its creation. A new severity measure, the Trauma Mortality Prediction Model (TMPM), uses either the Abbreviated Injury Scale (AIS) or DRG International Classification of Diseases-9th Rev. (ICD-9) lexicons and may better quantify injury severity compared with ISS. We compared the performance of TMPM with ISS and other measures of injury severity in a single cohort of patients.

METHODS: We included 337,359 patient records with injuries reliably described in both the AIS and the ICD-9 lexicons from the National Trauma Data Bank. Five injury severity measures (ISS, maximum AIS score, New Injury Severity Score [NISS], ICD-9-Based Injury Severity Score [ICISS], TMPM) were computed using either the AIS or ICD-9 codes. These measures were compared for discrimination (area under the receiver operating characteristic curve), an estimate of proximity to a model that perfectly predicts the outcome (Akaike information criterion), and model calibration curves.

RESULTS: TMPM demonstrated superior receiver operating characteristic curve, Akaike information criterion, and calibration using either the AIS or ICD-9 lexicons. Calibration plots demonstrate the monotonic characteristics of the TMPM models contrasted by the nonmonotonic features of the other prediction models.

CONCLUSION: Severity measures were more accurate with the AIS lexicon rather than ICD-9. NISS proved superior to ISS in either lexicon. Since NISS is simpler to compute, it should replace ISS when a quick estimate of injury severity is required for AIS-coded injuries. Calibration curves suggest that the nonmonotonic nature of ISS may undermine its performance. TMPM demonstrated superior overall mortality prediction compared with all other models including ISS whether the AIS or ICD-9 lexicons were used. Because TMPM provides an absolute probability of death, it may allow clinicians to communicate more precisely with one another and with patients and families. LEVEL OF EVIDENCE: Disagnostic study, level I; prognostic study, level II.

Language: en