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J Trauma Acute Care Surg. 2019 Dec 30. doi: 10.1097/TA.0000000000002569. [Epub ahead of print]

Predictors of Elderly Mortality After Trauma: A Novel Outcome Score.

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Department of Surgery, University of Minnesota, Minneapolis, MN.
Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
Department of Surgery, University of Michigan, Ann Arbor, MI.
University of Minnesota, Minneapolis, MN.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN.
Department of Surgery, Center for Healthcare Outcomes and Policy, University of Michigan, Ann Arbor, MI.
Department of Surgery, University of California San-Francisco, San-Francisco, CA.
Department of Surgery, North Memorial Health Hospital, Robbinsdale, MN.



Elderly trauma patients are at high risk for mortality, even when presenting with minor injuries. Previous prognostic models are poorly utilized due to 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.


The National Trauma Databank (NTDB) was used to identify patients ≥65 years old. Data was split into derivation (2007-2013) and validation (2014-2015) datasets. There was no overlap between datasets. Factors included age, co-morbidities, 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) were evaluated. As NTDB did not include blood product transfusion, an element of the Geriatric Trauma Outcome Score (GTOS), a regional trauma registry was used to compare qEMAT vs. GTOS. A mobile-based application is currently available for cost-free utilization.


840,294 patients were included in the derivation dataset and 427,358 patients in the validation dataset. The fEMAT score (median 91, SD: 82-102) included 26 factors and the qEMAT score included 8 factors. AuROC was 0.86 for fEMAT (Brier: 0.04) and 0.84 for qEMAT. fEMAT outperformed other trauma mortality prediction models (e.g. TRISS Penetrating and TRISS blunt, age + ISS). qEMAT outperformed the GTOS (AuROC: 0.87 vs. 0.83).


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 evidenceLevel III evidence.

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