Objective: 

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.

Summary Background Data: 

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.

Methods: 

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 three 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.

Results: 

Our study population comprised 1.6 million survey-weighted admission encounters. Fourteen baseline conditions and one 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 (OR[95%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]: 1.4[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.

Conclusion: 

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.

中文翻译：

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可解释的机器学习将数据库引入床边：TROUT（创伤衰弱结果）指数的开发和验证，这是一种根据衰弱情况预测创伤后结果的护理工具

客观的： 

举例说明可解释的机器学习框架，将数据库带到床边；开发并验证护理点虚弱评估工具来预测受伤后的结果。

摘要背景数据： 

仅捕获基线状况、易于实施且在全国范围内得到验证的老年创伤虚弱指数仍未得到充分探索。我们假设创伤衰弱结果 (TROUT) 指数可以以最小的实施障碍预测主要不良结果。

方法： 

我们根据个人预后多变量预测模型透明报告指南开发了 TROUT 指数。利用美国全国范围内 65 岁以上患者的入院经历（2016-2017 年；10% 发展，90% 验证队列），无监督和监督机器学习算法确定了最能导致不良结果的基线条件。这些条件被汇总成 TROUT 指数分数（0-100），描绘了三个脆弱风险层。经过关联（虚弱风险层和结果之间，根据年龄、性别和伤害严重程度[作为效果调节剂]进行调整）和校准分析后，我们设计了一个移动应用程序来促进即时护理的实施。

结果： 

我们的研究人群包括 160 万次调查加权入学经历。十四种基线条件和一种损伤机制构成了鳟鱼指数。在验证队列中，衰弱风险的增加（低=参考组、中、高）与调整后死亡率逐步增加相关（OR[95%CI]：2.6[2.4-2.8]、4.3[4.0-4.7]），延长住院时间 (OR[95%CI]: 1.4[1.4-1.5], 1.8[1.8-1.9])，处置至医疗机构 (OR[95%CI]: 1.4[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]）。校准分析发现较高的 TROUT 指数得分与所有不良结果之间呈正相关。我们构建了一个移动应用程序（“TROUT Index”）并公开共享代码。

结论： 

TROUT 指数是一种可解释的即时护理工具，用于量化受伤患者的临床决策并将其纳入临床决策中。TROUT 指数不是预测受伤后结果的独立工具；我们的工具应与损伤模式、临床管理以及机构特定的工作流程结合起来考虑。实用的移动应用程序和公开可用的代码可以促进未来的实施和外部验证研究。