Erken Parkinson hastalığında ilk düşüş için klinik bir tahmin modelinin geliştirilmesi ve onaylanması: iki sonbahar-naif kohortun bir çalışması.

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BackgroundFalls 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.ObjectiveThis study aimed to develop and externally validate a clinical prediction model for first falls in a population of fall-naive, early PD patients.MethodsThis prognostic model study used data from two cohorts: the Parkinson's Progression Markers Initiative (PPMI) for model development (n = 283) and internal validation (n = 120), and an independent Chinese cohort for external validation (n = 150). Participants were fall-naive with early PD (Hoehn and Yahr stage 1-2) and were followed for 36 months. The primary outcome was time to the first fall. A Cox proportional hazards model was developed using readily accessible clinical variables. Model performance was assessed using discrimination (C-index, AUC), calibration, and decision curve analysis.ResultsDuring 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.ConclusionA 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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