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, Hospital length of stay (LOS) has been used as an outcome variable, but reduced LOS could be achieved either by improved care or by increased mortality, A solution to this statistical problem of ''competing risks'' would enable injury outcomes based on LOS to be modeled using time-to-event methods, Methods: Time-to-event methodology was applied to 2,106 cases with complete data from the 1991-1994 registry of a regional trauma center. LOS was used as the outcome variable, modified by assigning an arbitrarily long LOS to any fatal case, A combination of proportional hazards and logistic regression models was used to explore the effects of potential predictive variables, including Trauma Score (TS), Injury Severity Score (ISS), components of TS or ISS, age, sex, alcohol use, and whether a patient was transferred. Results: The ''TRISS'' combination of TS, ISS, and age previously shown to predict mortality also predicted ''modified LOS'' (Wald p value less than 0.001 for each variable). Models using only age and certain components of ISS or TS fit our data even better, with fewer parameters, Other variables were not predictive, Modified Kapian-Meier plots provided easily interpreted graphical results, combining both mortality and LOS information. Conclusions: With a simple modification to allow for competing risks, time-to-event methods enable more informative modeling of injury outcomes than binary (lived/died) methods alone, Such models may be useful for describing and comparing groups of hospitalized trauma patients.