MODELING INJURY OUTCOMES USING TIME-TO-EVENT METHODS

Background: Mortality is an important measurement of injury outcomes, but measurements reflecting disability or cost also important, Hospita l length of stay (LOS) has been used as an outcome variable, but reduc ed LOS could be achieved either by improved care or by increased morta lity, A solution to this statistical problem of ''competing risks'' wo uld enable injury outcomes based on LOS to be modeled using time-to-ev ent methods, Methods: Time-to-event methodology was applied to 2,106 c ases with complete data from the 1991-1994 registry of a regional trau ma center. LOS was used as the outcome variable, modified by assigning an arbitrarily long LOS to any fatal case, A combination of proportio nal hazards and logistic regression models was used to explore the eff ects of potential predictive variables, including Trauma Score (TS), I njury Severity Score (ISS), components of TS or ISS, age, sex, alcohol use, and whether a patient was transferred. Results: The ''TRISS'' co mbination of TS, ISS, and age previously shown to predict mortality al so predicted ''modified LOS'' (Wald p value less than 0.001 for each v ariable). Models using only age and certain components of ISS or TS fi t our data even better, with fewer parameters, Other variables were no t predictive, Modified Kapian-Meier plots provided easily interpreted graphical results, combining both mortality and LOS information. Concl usions: With a simple modification to allow for competing risks, time- to-event methods enable more informative modeling of injury outcomes t han binary (lived/died) methods alone, Such models may be useful for d escribing and comparing groups of hospitalized trauma patients.