Outcomes of Extracorporeal Life Support in Trauma
Journal: Journal of Surgery (Vol.2, No. 2)Publication Date: 2014-12-31
Authors : Debbie F. Lew; Curtis J. Wray; Kevin P. Lally; Lillian S. Kao; Adam M. Voge;
Page : 1-5
Keywords : Trauma; Extracorporeal life support; ECLS; ECMO;
Abstract
Background: Extracorporeal life support (ECLS) is a lifesaving treatment for critically ill trauma patients with refractory cardiopulmonary failure. The purpose of this analysis is to use a large national trauma database to characterize the use and outcomes of ECLS in trauma. Methods: Patients from centers that performed ECLS were identified from the 2007-2009 National Trauma Data Bank (NTDB). Demographic, clinical, and outcome data were abstracted. Univariate analyses were performed using chi-square, Mann-Whitney U-test, and regression analyses. Propensity score analysis and multiple regression models were developed using backwards stepwise logistic regression to identify risk factors for ECLS and predictors of mortality; p<0.05 was considered significant. Results: Forty-two centers of 682 (6%) performed ECLS on 78 of 245,950 patients (0.03%). ECLS patients were younger, more severely injured, had a longer ICU and hospital length of stay, and a higher mortality. Univariate analysis showed that ECLS patients had a significantly increased risk of death (OR 28; 95% CI 18-43; p<0.001). Among patients receiving ECLS, predictors of mortality included injury severity score (ISS) (OR 1.06; 95% CI 1.01-1.11; p=0.03) and cardiac arrest (OR 7.57; 95% CI 1.58-36.2; p=0.01). After propensity score adjusted logistic regression, ECLS was still a significant predictor of mortality (OR 6, 95% CI 2.76-13.1, p<0.01). Patients receiving ECLS at a center that performed 5 or more ECLS runs trended towards a lower mortality rate (27% vs. 73%, p=0.10). Conclusions: ECLS is infrequently used in the trauma setting. When compared to other trauma patients, ECLS patients are more critically ill and are at increased odds of death. Further study is required to determine which patient and hospital characteristics predict improved survival.
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