ArticleFrontiers in aging neuroscience2026
Development and validation of a clinical prediction model for first fall in early Parkinson's disease: a study of two fall-naive cohorts.
Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A machine learning approach to quantifying fall conversion risk in fall-naïve Parkinson's patients.Parkinsonism & related disorders · 2026Article
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9 authors.
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Abstract
Background: Falls are frequent and debilitating complications in Parkinson's disease (PD), with a substantial risk present even in early stages. Predicting the first fall is critical for preventive interventions, yet existing models are often unsuitable for fall-naive, early PD patients due to their reliance on fall history. Objective: This study aimed to develop and externally validate a clinical prediction model for first falls in a population of fall-naive, early PD patients. Methods: This prognostic model study used data from two cohorts: the Parkinson's Progression Markers Initiative (PPMI) for model development ( Results: During follow-up, 16.9% of PPMI participants and 20.7% of the Chinese cohort experienced a first fall. The final model incorporated five independent predictors: lower body mass index, asymptomatic orthostatic hypotension, lower Montreal Cognitive Assessment score, a Geriatric Depression Scale-15 score > 5, and a higher postural instability and gait disorder score. The model demonstrated good discrimination with an optimism-corrected C-index of 0.844 in the training set and maintained its performance in both internal (C-index: 0.768) and external validation (C-index: 0.825). Decision curve analysis indicated that the model demonstrated superior clinical net benefit for predicting falls over 36 months compared to 18 months. Conclusion: A history of falls is not necessary to predict the first fall in early PD. Our externally validated model, based on five easily ascertainable clinical factors, provides a practical tool for early risk stratification and can help guide individualized preventive strategies to delay or prevent initial falls in this vulnerable population.
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