Division of General Surgery, Department of Surgery, Stanford University, Stanford, CA.
Surgeons Writing About Trauma, Stanford University, Stanford, CA.
Ann Surg. 2023 Jul 1;278(1):135-139. doi: 10.1097/SLA.0000000000005649. Epub 2022 Aug 3.
Exemplify an explainable machine learning framework to bring database to the bedside; develop and validate a point-of-care frailty assessment tool to prognosticate outcomes after injury.
A geriatric trauma frailty index that captures only baseline conditions, is readily-implementable, and validated nationwide remains underexplored. We hypothesized Trauma fRailty OUTcomes (TROUT) Index could prognosticate major adverse outcomes with minimal implementation barriers.
We developed TROUT index according to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis guidelines. Using nationwide US admission encounters of patients aged ≥65 years (2016-2017; 10% development, 90% validation cohorts), unsupervised and supervised machine learning algorithms identified baseline conditions that contribute most to adverse outcomes. These conditions were aggregated into TROUT Index scores (0-100) that delineate 3 frailty risk strata. After associative [between frailty risk strata and outcomes, adjusted for age, sex, and injury severity (as effect modifier)] and calibration analysis, we designed a mobile application to facilitate point-of-care implementation.
Our study population comprised 1.6 million survey-weighted admission encounters. Fourteen baseline conditions and 1 mechanism of injury constituted the TROUT Index. Among the validation cohort, increasing frailty risk (low=reference group, moderate, high) was associated with stepwise increased adjusted odds of mortality {odds ratio [OR] [95% confidence interval (CI)]: 2.6 [2.4-2.8], 4.3 [4.0-4.7]}, prolonged hospitalization [OR (95% CI)]: 1.4 (1.4-1.5), 1.8 (1.8-1.9)], disposition to a facility [OR (95% CI): 1.49 (1.4-1.5), 1.8 (1.7-1.8)], and mechanical ventilation [OR (95% CI): 2.3 (1.9-2.7), 3.6 (3.0-4.5)]. Calibration analysis found positive correlations between higher TROUT Index scores and all adverse outcomes. We built a mobile application ("TROUT Index") and shared code publicly.
The TROUT Index is an interpretable, point-of-care tool to quantify and integrate frailty within clinical decision-making among injured patients. The TROUT Index is not a stand-alone tool to predict outcomes after injury; our tool should be considered in conjunction with injury pattern, clinical management, and within institution-specific workflows. A practical mobile application and publicly available code can facilitate future implementation and external validation studies.
展示一个可解释的机器学习框架，将数据库应用于临床实践；开发和验证一种即时的衰弱评估工具，以预测创伤后结局。
一种能够捕捉到基线情况、易于实施且经过全国范围验证的老年创伤性衰弱指数仍未得到充分探索。我们假设创伤性衰弱结局（Trauma fRailty OUTcomes，TROUT）指数可以通过最小化实施障碍来预测主要不良结局。
我们根据多变量个体预后预测模型透明报告指南制定了 TROUT 指数。使用全美 2016-2017 年≥65 岁患者入院的入院记录（10%用于开发，90%用于验证队列），非监督和监督机器学习算法确定了对不良结局贡献最大的基线情况。这些情况被汇总到 TROUT 指数评分（0-100）中，将衰弱风险分为 3 个层次。在关联分析（比较衰弱风险分层与结局，调整年龄、性别和损伤严重程度（作为效应修饰因素））和校准分析后，我们设计了一个移动应用程序，以促进即时实施。
我们的研究人群包括 160 万经过调查加权的入院记录。14 项基线情况和 1 种损伤机制构成了 TROUT 指数。在验证队列中，衰弱风险增加（低=参考组，中、高）与调整后死亡率的逐步增加相关（比值比[OR] [95%置信区间（CI）]：2.6 [2.4-2.8]，4.3 [4.0-4.7]），住院时间延长[OR（95% CI）]：1.4（1.4-1.5），1.8（1.8-1.9）]，机构安置[OR（95% CI）]：1.49（1.4-1.5），1.8（1.7-1.8）]和机械通气[OR（95% CI）]：2.3（1.9-2.7），3.6（3.0-4.5）]。校准分析发现，TROUT 指数评分越高，与所有不良结局的相关性越正。我们开发了一个移动应用程序（“TROUT 指数”）并公开了代码。
TROUT 指数是一种可解释的即时衰弱评估工具，可以在受伤患者的临床决策中定量和整合衰弱情况。TROUT 指数不是一种单独的预测创伤后结局的工具；我们的工具应与损伤模式、临床管理以及特定机构的工作流程一起考虑。一个实用的移动应用程序和公开的代码可以促进未来的实施和外部验证研究。
