Movement disorders clinical practice · 2026
Closing the Gate Before the Horse is out of the Barn: A Model to Effectively Predict the First Fall in Patients with Parkinson's Disease.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Anis Saar, Zimmerman Eric, Munyeshyaka Maxime, Fernandez Hubert H, Rosenfeldt Anson B, Alberts Jay L
Аннотация
BACKGROUND: Most fall-prediction models in Parkinson's disease (PD) rely on prior falls, limiting prevention. The Waiting Room of the Future (WROTF) platform collects standardized motor and cognitive measures during routine visits, integrated into electronic health records (EHR).
OBJECTIVE: To develop a first-fall prediction model combining WROTF-EHR data and compare its performance with established cognitive- and motor-based models.
METHODS: Previously published motor (Paul) and cognitive (CYCLE-II) models were refit using logistic regression. A new regularized (LASSO) model was developed. All models were evaluated using all participants and only those without prior falls.
RESULTS: Among 997 PD patients (mean age 69.6 ± 8.8; 62.8% male; disease duration 3.6 ± 4.3 years), AUCs were 0.77 (Paul), 0.70 (CYCLE-II), and 0.81 (LASSO). In fall-naïve patients, AUCs were 0.62, 0.68, and 0.70. Key predictors included processing speed, walking speed, disease duration, and age.
CONCLUSIONS: The model enables early fall-risk detection using routinely collected data, supporting proactive and personalized preventive care.
OBJECTIVE: To develop a first-fall prediction model combining WROTF-EHR data and compare its performance with established cognitive- and motor-based models.
METHODS: Previously published motor (Paul) and cognitive (CYCLE-II) models were refit using logistic regression. A new regularized (LASSO) model was developed. All models were evaluated using all participants and only those without prior falls.
RESULTS: Among 997 PD patients (mean age 69.6 ± 8.8; 62.8% male; disease duration 3.6 ± 4.3 years), AUCs were 0.77 (Paul), 0.70 (CYCLE-II), and 0.81 (LASSO). In fall-naïve patients, AUCs were 0.62, 0.68, and 0.70. Key predictors included processing speed, walking speed, disease duration, and age.
CONCLUSIONS: The model enables early fall-risk detection using routinely collected data, supporting proactive and personalized preventive care.