Multivariate Models for Predicting Survival of Patients with Trauma from Low Falls: The Impact of Gender and Pre-existing Conditions

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Abstract

Objective

To determine if pre-existing conditions significantly improve the ability of current (TRISS and ASCOT) methods for predicting survival of patients with trauma from low falls.

Design

Retrospective analysis using logistic regression models to identify significant independent predictors of survival.

Setting

Eight hospitals affiliated with New York Medical College.

Patients

A total of 1906 patients with trauma from low falls who were admitted to the eight hospitals between July 1987 and June 1989.

Main Results

Gender and several pre-existing conditions significantly improved the ability of age and the physiologic and anatomic variables contained in the TRISS and ASCOT methodologies to predict survival for trauma patients suffering from low falls, with males experiencing a lower probability of survival. Odds of survival for patients with these pre-existing conditions ranged from 0.18 to 0.59 times the odds of survival for similar patients without the pre-existing conditions when the TRISS variables were used, and from 0.23 to 0.56 times the odds for similar patients when ASCOT variables were used. Furthermore, some substantial differences were found when hospital performance was assessed with and without the benefit of pre-existing conditions.

Conclusions

Pre-existing conditions and male gender are significantly related to survival of patients with trauma from low falls, and should be included along with age and the various physiologic and anatomic measures currently being used to pre-dict survival for those patients.

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