The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring.
BACKGROUND
The prediction of outcome after injury must incorporate measures of injury severity, but there is no consensus on how many injuries should be used in calculating these measures. Initially, the single worst injury was used to predict outcome, but the introduction of the Injury Severity Score allowed up to three injuries to contribute to outcome prediction. Subsequently, other outcome prediction approaches used many (New Injury Severity Score [NISS]) or all (ICISS and Trauma Registry Abbreviated Injury Scale Score [TRAIS], which use International Classification of Diseases, Ninth Revision [ICD-9] and Abbreviated Injury Scale [AIS] survival risk ratios [SRRs], respectively) of a patient's injuries. The ability of only the most severe injury in predicting mortality has never been studied. Our objective was to determine the ability of a patient's worst injury to predict mortality.


METHODS
A 10-fold cross-validation design was used to compute six scores for each of 160,208 patients from a large trauma database (the National Trauma Data Bank [NTDB]). The scores were ICISS, TRAIS, ICISS1 (only a patient's worst ICD-9 SRR), TRAIS1 (only a patient's worst AIS SRR), NISS (sum of squares of worst three AIS severity measures), and MAXAIS (worst AIS severity measure). Discrimination was assessed using the area under the receiver operating characteristic curve. Logistic regression R2 gauged the proportion of variance each score explained. The Akaike information criterion, a deviance statistic (lower is better), assessed model fit.


RESULTS
The receiver operating characteristic curve, R2, and Akaike information criterion statistics (NC_ICISS and NC_ICDSRR1 represents scores derived from the original North Carolina Hospital Discharge Database SRRs) are summarized in tabular form in the Results section.


CONCLUSIONS
Regardless of scoring type (ICD/AIS SRRs or AIS severity), a patient's worst injury discriminates survival better, fits better, and explains more variance than currently used multiple injury scores.
Kilgo
Patrick D
PD
Department of Public Health Sciences, Wake Forest University School of Medicine, WistonSalem, North Carolina 27157-1063, USA. pkilgo@wfubmc.edu
Osler
Turner M
TM
Meredith
Wayne
W
D015998:Abbreviated Injury Scale; D000328:Adult; D005260:Female; D006801:Humans; D015601:Injury Severity Score; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests; D012372:ROC Curve; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000145:classification / Q000401:mortality*