Explainable Machine Learning to Bring Database to the Bedside

Abstract

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. BACKGROUND: 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 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. RESULTS: 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. 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.

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