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Prediction of Subsequent Fragility Fractures: Application of Machine Learning Publisher Pubmed



Zabihiyeganeh M1 ; Mirzaei A1, 2 ; Tabrizian P1 ; Rezaee A1, 3 ; Sheikhtaheri A4 ; Kadijani AA1 ; Kadijani BA5 ; Sharifi Kia A1, 6
Authors
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Authors Affiliations
  1. 1. Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, University of Medical Sciences, Baharestan Sq, Tehran, Iran
  2. 2. Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, MN, United States
  3. 3. Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
  4. 4. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
  6. 6. Department of Computer Science, Faculty of Science, Western University, London, ON, Canada

Source: BMC Musculoskeletal Disorders Published:2024


Abstract

Background: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. Methods: We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. Results: The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. Conclusion: ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies. © The Author(s) 2024.
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