Concurrent Prediction of Hospital Mortality and Length of Stay from Risk Factors on Admission


  • David E. Clark,

  • Louise M. Ryan

Address correspondence to David E. Clark, M.D., M.P.H., Department of Surgery, Maine Medical Center, 887 Congress St., Portland, ME 04102. Dr . Clark is also affiliated with the Harvard Injury Control Research Center, Boston, and the Bavarian Public Health Research Center, Munich, Germany. Louise M. Ryan, Ph.D., is with the Department of Biostatistics, Harvard School of Public Health, Boston, and the Department of Biostatistics, Dana-Farber Cancer Center, Boston.


Objective. To develop a method for predicting concurrently both hospital survival and length of stay (LOS) for seriously ill or injured patients, with particular attention to the competing risks of death or discharge alive as determinants of LOS.

Data Sources. Previously collected 1995–1996 registry data on 2,646 cases of injured patients from three trauma centers in Maine.

Study Design. Time intervals were determined for which the rates of discharge or death were relatively constant. Poisson regression was used to develop a model for each type of terminal event, with risk factors on admission contributing proportionately to the subsequent rates for each outcome in each interval. Mean LOS and cumulative survival were calculated from a combination of the resulting piecewise exponential models.

Principal Findings. Age, Glasgow Coma Scale, Abbreviated Injury Scores, and specific mechanisms of injury were significant predictors of the rates of death and discharge, with effects that were variable in different time intervals. Predicted probability of survival and mean LOS from the model were similar to actual values for categorized patient groups.

Conclusions. Piecewise exponential models may be useful in predicting LOS, especially if determinants of mortality are separated from determinants of discharge alive.