INTRODUCTION: Non-home discharge after orthopedic trauma is associated with worse outcomes, increased costs, and greater resource utilization. Existing prediction tools often rely on hospital course variables unavailable at presentation or are limited to specific fracture populations. This study aimed to develop and validate the Fracture Orthopedic Risk of Non-Home Discharge (FORD) Score, a bedside tool using only emergency department-available variables to predict non-home discharge in adult fracture patients. METHODS: A retrospective cohort study was conducted of adult fracture patients treated at an ACS-verified Level I trauma center from 2015 to 2023. Patients were randomly split into derivation (67%) and validation (33%) cohorts. Candidate predictors available immediately upon patient arrival were evaluated using univariate logistic regression, followed by multivariate logistic regression after collinearity assessment. Independent predictors were converted into an integer-based point system to construct the FORD Score. Model discrimination, calibration, and classification performance were assessed in the validation cohort and compared with established trauma severity measures. RESULTS: The final cohort included 8422 patients, of whom 8.1% had non-home discharge. Fifteen independent predictors comprised the FORD Score, including age, physiologic abnormalities, fracture characteristics, and transport mode. In the validation cohort, FORD demonstrated good discrimination (AUROC 0.818, 95% CI 0.791-0.846) and excellent calibration. At the optimal threshold (score ≥4), sensitivity was 74.1%, specificity 75.8%, PPV 21.3%, and NPV 97.1%. FORD outperformed GTOS-II (AUROC 0.777; DeLong p = 0.018) and TRIAGES (AUROC 0.746; p < 0.001). Non-home discharge rates ranged from 1.7% in the lowest risk group to 34.1% in the highest, a 20-fold gradient. CONCLUSION: The FORD Score is a validated bedside tool that accurately predicts non-home discharge in adult orthopedic trauma patients using only admission data, enabling early discharge planning and optimized resource allocation.
Salman et al. (Tue,) studied this question.