A machine learning-based scoring system and ten factors associated with hip fracture occurrence in the elderly

Abstract

Hip fractures are fragility fractures frequently seen in persons over 80-years-old. Although various factors, including decreased bone mineral density and a history of falls, are reported as hip fracture risks, few large-scale studies have confirmed their relevance to individuals older than 80, and tools to assess contributions of various risks to fracture development and the degree of risk are lacking. We recruited 1395 fresh hip fracture patients and 1075 controls without hip fractures and comprehensively evaluated various reported risk factors and their association with hip fracture development. We initially constructed a predictive model using Extreme Gradient Boosting (XGBoost), a machine learning algorithm, incorporating all 40 variables and evaluated the model's performance using the area under the receiver operating characteristic curve (AUC), yielding a value of 0.87. We also employed SHapley Additive exPlanation (SHAP) values to evaluate each feature importance and ranked the top 20. We then used a stepwise selection method to determine key factors sequentially until the AUC reached a plateau nearly equal to that of all variables and identified the top 10 sufficient to evaluate hip fracture risk. For each, we determined the cutoff value for hip fracture occurrence and calculated scores of each variable based on the respective feature importance. Individual scores were: serum 25(OH)D levels (<10 ng/ml, score 7), femoral neck T-score (<-3, score 5), Barthel index score (<100, score 3), maximal handgrip strength (<18 kg, score 3), GLFS-25 score (≥24, score 2), number of falls in previous 12 months (≥3, score 2), serum IGF-1 levels (<50 ng/ml, score 2), cups of tea/day (≥5, score -2), use of anti-osteoporosis drugs (yes, score -2), and BMI (<18.5 kg/m², score 1). Using these scores, we performed receiver operating characteristic (ROC) analysis and the resultant optimal cutoff value was 7, with a specificity of 0.78, sensitivity of 0.75, and AUC of 0.85. These ten factors and the scoring system may represent tools useful to predict hip fracture.

Keywords: Hip fracture; Machine learning; SHapley Additive exPlanation values.