A comparison among the abilities of various injury severity measures to predict mortality with and without accompanying physiologic information.
BACKGROUND
A few recent studies have compared the abilities of different injury severity measures to predict inpatient mortality. This study extended previous studies in that it used a registry with noncenters as well as centers, and examined the relative marginal abilities of competing severity measures to predict mortality when physiologic data also are available.


METHODS
Several methods for assessing injury severity of trauma patients were compared in terms of their ability to predict mortality with and without the addition of additional demographic and physiologic information using logistic regression models. Separate determinations also were made for all patients and for three groups of patients with blunt trauma resulting from motor vehicle crashes, low falls, and other blunt injuries. Statistical models were compared using measures of discrimination and calibration.


RESULTS
The International Classification of Disease-Based Severity Score (ICISS) had the best discrimination for each of the eight models examined, and it was significantly better than all the other measures in relation to the models for all patients and for victims of motor vehicle crashes. The ICISS also had the best calibration in half of the models with and half without demographic and physiologic information. The New Injury Severity Score had the best calibration in relation to two of the remaining four models. Physiologic data add substantially to the ability to predict mortality regardless of the anatomic injury severity measure used.


CONCLUSIONS
On the average, the ICISS had the best discrimination of all of the measures, as well as a slight edge with respect to calibration in predicting trauma mortality with or without the aid of demographic or physiologic measures.
Hannan
Edward L
EL
Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, USA. elh03@health.state.ny.us
Waller
Christine Hicks
CH
Farrell
Louise Szypulski
LS
Cayten
C Gene
CG
D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D015601:Injury Severity Score; D008297:Male; D008499:Medical Records; D008875:Middle Aged; D009518:New York / Q000453:epidemiology; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012042:Registries; D014947:Wounds and Injuries / Q000145:classification / Q000401:mortality* / Q000503:physiopathology