Multi-state piecewise exponential model of hospital outcomes after injury

Background: To allow more accurate prediction of hospital length of stay (LOS) after serious injury, a multi-state model is proposed, in which transitions from the hospitalized state to three possible discharged states (home, long-term care, or death) are assumed to follow piecewise constant rates for each of a limited number of time periods. These rates may be affected differently by time-varying covariates, and result in a piecewise exponential model that can be estimated from a reference database using standard statistical software and Poisson regression. Methods: Records of 259,941 injured patients from the U.S. Nationwide Inpatient Sample were used to create a multi-state piecewise exponential model with four time periods. Hospital mortality and LOS for patient subgroups were calculated from this model, and time-varying covariate effects were estimated. Results: Early mortality was increased by anatomic injury severity or penetrating mechanism, but these effects diminished with time; age and male sex remained strong predictors of mortality in all time periods. Rates of discharge home decreased steadily with time, while rates of transfer to long-term care peaked at five days. Predicted and observed LOS and mortality were similar for multiple subgroups. Conclusions: Multi-state piecewise exponential models may be useful to describe hospital outcomes, especially after serious injuries or other conditions when many patients are not discharged home.