Development of a Fall-Related Injury Risk-Stratified Prediction Model for Older Adults

Background Falls and related injuries among older adults represent a significant public health issue, adversely affecting their quality of life and increasing the socioeconomic burden. This study aims to develop a risk stratification prediction model based on machine learning algorithms. Methods Using a convenience sampling method, a total of 402 older adults scheduled for discharge at the Center of Gerontology and Geriatrics of West China Hospital, Sichuan University from April to September 2024 were enrolled. The participants were followed for 6 months. The participants were categorized into three groups: non-fall, no/minor injury, and moderate to severe injury group. Least absolute shrinkage and selection operator (LASSO) regression was applied to screen candidate predictors. Multinomial logistic regression (MLR), random forest (RF), support vector machine (SVM), and naive Bayes (NB) were employed for model development, with internal validation performed via 5-fold cross-validation. Model performance was assessed via the macro-average AUC, F1 score and Brier score. The optimal model was interpreted using SHAP analysis. This study was approved by the Committee of Ethics of West China Hospital of Sichuan University (Approval No. 2024–923). Results The SVM demonstrated the best performance, with a macro-average AUC of 0.856 (95% CI: 0.837–0.878), and F1-score of 0.527 (95% CI: 0.410–0.619), and a Brier score of 0.086 (95% CI: 0.078–0.091). Conclusions This study developed a risk-stratified prediction model for fall-related injuries in older adults. The SVM model is capable of accurately predicting the risk levels of fall-related injuries in older adults. Registration www.chictr.org.cn ChiCTR2400085499. Registered 11/6/2024, first recruitment 13/6/2024.