A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort study.

BACKGROUND: Although hip fractures are commonly associated with functional decline, increased morbidity, and mortality, accurate models for both short- and long-term prediction that do not rely on in-person assessment remain lacking. The aim was to develop and validate a high-performing clinical decision support tool, that can be used for population screening without the need for patient assessment, for predicting hip fracture risk. METHODS AND FINDINGS: All individuals aged ≥50 years living in Sweden at baseline (randomly set between 2011 and 2013, N = 3,542,647), who had not been prescribed osteoporosis medication within the previous 2 years, were included and followed through the end of 2021. During follow-up, 142,327 individuals sustained a hip fracture. Using a broad unconditional approach, 139,980 variables encompassing diagnoses, medications, procedures, demographics and socioeconomic data with multiple historic windows and level of detail were defined. The dataset was divided into discovery (25%), development (65%) and holdout (10%) cohorts. The risk of hip fracture was evaluated using traditional Cox models, as well as machine learning methods XGBoost and DeepSurv. The developed clinical support tool FRACTURE-ML based on DeepSurv using 2,500 predictors yielded an area under the curve (AUC) of 0.89 (95% CI 0.88, 0.89) at year 1, 0.88 (95% CI 0.87,0.88) at two years, and a reduced model with 35 predictors yielded similar AUCs, 0.87 at 2 years and 0.85 at 5 years. Traditional Cox models with 35 and 400 predictors reached similar AUCs. Both the DeepSurv and the Cox models performed excellently at the individual level based on calibration plot analysis. Screening using the in Sweden advocated fracture liaison services (FLS) secondary prevention approach (recent fracture), resulted in an AUC of 0.55 (95% CI 0.54, 0.55) at two years. For 2-year prediction, FRACTURE-ML, which could be used as a complementary approach for primary prevention, identified nearly seven times more persons at risk (sensitivity 0.84 (95% CI 0.82, 0.85) versus 0.12 (95% CI 0.11, 0.13) than the FLS approach, with limited reduction in specificity (0.79 (95% CI 0.79, 0.79) versus 0.98 (95% CI 0.98, 0.98), respectively). The lack of external validation and implementation studies represents a limitation, as such studies are needed to establish the clinical usefulness of FRACTURE-ML. CONCLUSIONS: FRACTURE-ML was effective in predicting hip fracture and could be used as a resource-efficient solution for population screening to improve the prevention of hip fracture.