From 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.
J Trauma Acute Care Surg. 2020 Mar;88(3):416-424. doi: 10.1097/TA.0000000000002569.
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.
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.
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).
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.
Epidemiological Study, level IV.
老年创伤患者即使受伤轻微，其死亡率也很高。之前的预后模型由于依赖于索引住院期间无法获得的元素，因此使用效果不佳。本研究的目的是开发一种预测算法，以便使用易于获取的指标准确估计住院死亡率。
国家创伤数据库用于确定 65 岁及以上的患者。数据分为推导（2007-2013 年）和验证（2014-2015 年）数据集。两个数据集之间没有重叠。纳入的因素包括年龄、合并症、生理参数和损伤类型。开发了一种用于预测住院死亡率的两阶段评分系统：创伤后简易老年死亡率（qEMAT）评分，用于患者初次就诊时使用，以及全 EMAT（fEMAT）评分，用于放射学评估后使用。最终模型（逐步向前选择，p < 0.05）是根据校准和判别分析选择的。评估了校准（Brier 评分）和判别（接收者操作特征曲线下面积 [AuROC]）。由于国家创伤数据库不包括血液制品输注，这是老年创伤结局评分（GTOS）的一个要素，因此使用区域创伤登记处比较了 qEMAT 与 GTOS。目前有一个基于移动的应用程序可供免费使用。
共纳入 840,294 例患者作为推导数据集，427,358 例患者作为验证数据集。fEMAT 评分（中位数，91；标准差，82-102）包含 26 个因素，qEMAT 评分包含 8 个因素。fEMAT 的 AuROC 为 0.86（Brier，0.04），qEMAT 的 AuROC 为 0.84。fEMAT 优于其他创伤死亡率预测模型（例如，创伤和损伤严重度评分-穿透性和创伤和损伤严重度评分-钝性，年龄+损伤严重度评分）。qEMAT 优于 GTOS（AuROC，0.87 与 0.83）。
qEMAT 和 fEMAT 可以准确估计住院死亡率的概率，并可在入院时轻松计算。该信息可以帮助决定转至三级转诊中心、患者/家属咨询和姑息治疗的使用。
流行病学研究，IV 级。
