Frontiers in artificial intelligence · 2026
Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records.
СтатьяНазвание и аннотация: Перевод в очереди
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Larrañaga Igor, Rujas Miguel, Alayo Itxaso, Erreguerena Irati, Capo Marco, Lopez-Perez Laura, Mediavilla Marta M, Martínez María Dolores, Chuliá-Gil Héctor, Georga Eleni I, Haleem Muhammad Salman, Pecchia Leandro, Fotiadis Dimitrios I, Fico Giuseppe, Fullaondo Ane
Аннотация
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.
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.