Abstract

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 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.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.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.

View details for DOI 10.1097/SLA.0000000000005649

View details for PubMedID 35920568