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.