Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records

Background Falls represent a major clinical and financial challenge for healthcare systems. Accurately predicting first falls remains challenging, especially when using routinely collected data. Objective Develop and evaluate a predictive model to identify elderly people at risk of first fall—defined as a fall after a 90-day fall-free period—in the Basque Country using routinely collected health records. Method A retrospective study included patients aged ≥65 with at least two chronic conditions among heart failure, chronic obstructive pulmonary disease (COPD), and diabetes. Data on demographics, diagnoses, prescriptions and healthcare utilisation were obtained from Osakidetza-Basque Health Service databases. Patients were labelled as “fallers” if they fell during 2022–2023 after a 90-day fall-free period rather than a true first-ever fall. Predictive models—logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB)—were trained using recursive feature elimination with cross-validation (RFECV). Shapley additive explanations (SHAP) enhanced model interpretability and explainability. Results 35,197 patients were included, with 10.6% experiencing a fall. All models achieved similar results, with an AUCROC score of 0.71, while precision remained low. Emergency room visits in the prior 3 months, presence of caregiver, and age consistently ranked the top predictors. The number of prescriptions and antidepressant use also emerged as relevant. Discussion Models showed moderate predictive performance. Relying solely on routinely collected health records limited clinical applicability due to low precision. Future work should integrate diverse data sources—health records, real-time gait and balance metrics, environmental factors—to improve fall prediction and support clinical decision-making.