Integrating Machine Learning into Fracture Liaison Services: Toward precision risk stratification in osteoporosis care: A prospective cohort study

Abstract

BACKGROUND: Traditional fracture risk assessment tools have limitations in accurately predicting re-fracture risk. Machine learning (ML) approaches offer a novel method for improving risk prediction. This study aimed to apply ML approach to identify key predictors of mortality, re-fractures, and falls among patients enrolled in a Fracture Liaison Service (FLS) program. METHODS: This prospective cohort study analyzed data from 600 patients aged ≥50 years with a history of hip or vertebral fractures who were enrolled in an FLS program between 2014 and 2016. The patients underwent comprehensive assessments, and ML models, specifically a balanced random forest classifier with recursive feature elimination with cross-validation, were used to identify the most influential predictors for three outcomes: two-year mortality, re-fractures, and falls. Model performance was evaluated based on accuracy, precision, sensitivity, specificity, F1-score, and area under the curve. RESULTS: Over two years, 14.2% of patients died, 6% sustained new fractures, and 33.2% experienced at least one fall. The ML analysis identified age, body mass index, self-care ability, and serum calcium and alkaline phosphatase levels as the strongest predictors of mortality. Re-fracture risk was primarily influenced by 10-year major fracture probability, body height, and BMI. Falls were significantly associated with BMI, age, and serum alkaline phosphatase levels. CONCLUSION: While the ML models demonstrated modest predictive discrimination for re-fractures, they can analyze complex data to identify hidden risk factors that may be overlooked by traditional methods. These insights highlight the potential of AI-driven models for osteoporosis management, enabling more precise interventions for high-risk individuals.

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