Hazard Regression Models of Early Mortality in Trauma Centers

Abstract

Factors affecting early hospital deaths after trauma can be different from factors affecting later hospital deaths, and the distribution of short and long prehospital times can vary among hospitals. Hazard regression (HR) models might therefore be more useful than logistic regression (LR) models for analysis of trauma mortality, especially when treatment effects at different time points are of interest. We obtained data for trauma center patients from the 2008-2009 National Trauma Data Bank. Patients were included if they had complete data for prehospital times, hospital times, survival outcomes, age, vital signs, and severity scores. Patients were excluded if pulseless on admission, transferred in or out, or had an Injury Severity Score <9. Using covariates proposed for the Trauma Quality Improvement Program and an indicator for each hospital, we compared LR models predicting survival at 8 hours after injury with HR models with survival censored at 8 hours. Hazard regression models were then modified to allow time-varying hospital effects. A total of 85,327 patients in 161 hospitals met inclusion criteria. Crude hazards peaked initially and then declined steadily. When hazard ratios were assumed constant in HR models, they were similar to odds ratios in LR models associating increased mortality with increased age, firearm mechanism, increased severity, more deranged physiology, and estimated hospital-specific effects. However, when hospital effects were allowed to vary by time, HR models demonstrated that hospital outliers were not the same at different times after injury. Hazard regression models with time-varying hazard ratios reveal inconsistencies in treatment effects, data quality, and/or timing of early death among trauma centers. Hazard regression models are generally more flexible than LR models, can be adapted for censored data, and potentially offer a better tool for analysis of factors affecting early death after injury.