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Predictors of elderly mortality after trauma: A novel outcome score
Rachel S Morris1, David Milia, James Glover
1From the Department of Surgery (R.S.M., J.G., J.C., C.J.T.), University of Minnesota, Minneapolis, Minnesota; Department of Surgery (D.M.), Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Surgery (L.M.N., M.R.H.), University of Michigan, Ann Arbor; Department of Surgery (B.C.), University of Minnesota, Minneapolis, Minnestoa; Institute for Health Informatics (E.L., E.K., C.J.T.), University of Minnesota, Minneapolis, Minnesota; Department of Surgery (D.S.), University of California San Francisco, San Francisco, California; Department of Surgery (C.J.T.), North Memorial Health Hospital, Robbinsdale; and Department of Surgery (M.R.H.), Center for Healthcare Outcomes and Policy, University of Michigan, Ann Arbor, Minnesota.
Introduction:
Elderly trauma patients are at high risk for mortality, even when presenting with minor injuries. Previous prognostic models are poorly used because of their reliance on elements unavailable during the index hospitalization. The purpose of this study was to develop a predictive algorithm to accurately estimate in-hospital mortality using easily available metrics.
Methods:
The National Trauma Databank was used to identify patients 65 years and older. Data were split into derivation (2007-2013) and validation (2014-2015) data sets. There was no overlap between data sets. Factors included age, comorbidities, physiologic parameters, and injury types. A two-tiered scoring system to predict in-hospital mortality was developed: a quick elderly mortality after trauma (qEMAT) score for use at initial patient presentation and a full EMAT (fEMAT) score for use after radiologic evaluation. The final model (stepwise forward selection, p < 0.05) was chosen based on calibration and discrimination analysis. Calibration (Brier score) and discrimination (area under the receiving operating characteristic curve [AuROC]) were evaluated. Because National Trauma Databank did not include blood product transfusion, an element of the Geriatric Trauma Outcome Score (GTOS), a regional trauma registry was used to compare qEMAT versus GTOS. A mobile-based application is currently available for cost-free utilization.
Results:
A total of 840,294 patients were included in the derivation data set and 427,358 patients in the validation data set. The fEMAT score (median, 91; S.D., 82-102) included 26 factors, and the qEMAT score included eight factors. The AuROC was 0.86 for fEMAT (Brier, 0.04) and 0.84 for qEMAT. The fEMAT outperformed other trauma mortality prediction models (e.g., Trauma and Injury Severity Score-Penetrating and Trauma and Injury Severity Score-Blunt, age + Injury Severity Score). The qEMAT outperformed the GTOS (AuROC, 0.87 vs. 0.83).
Conclusion:
The qEMAT and fEMAT accurately estimate the probability of in-hospital mortality and can be easily calculated on admission. This information could aid in deciding transfer to tertiary referral center, patient/family counseling, and palliative care utilization.
Level Of Evidence:
Epidemiological Study, level IV.
