Malaguti Maria Chiara, Longo Chiara, Moroni Monica, Ragni Flavio, Bovo Stefano, Chierici Marco, Gios Lorenzo, Avanzino Laura, Marchese Roberta, Di Biasio Francesca, Pardini Matteo, Cerne Denise, Mandich Paola, Marenco Manuela, Uccelli Antonio, Giometto Bruno, Jurman Giuseppe, Osmani Venet
Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy.
Fondazione Bruno Kessler, Trento, Italy.
Eur J Neurol. 2025 May;32(5):e70118. doi: 10.1111/ene.70118.
Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurate prediction of fall risk and identification of contributing factors are essential for timely interventions.
Our objective was to develop and validate a machine learning (ML) algorithm across multiple centers in Italy to accurately forecast fall risk and identify related factors using routinely collected clinical data.
Patient data from two Italian centers (N = 251) were divided into a training cohort (N = 164) for ML model development and a validation cohort (N = 87). External validation was conducted on a subset of PPMI study patients (N = 65). We compared the performance of logistic regression (LR) and Support Vector Classifier (SVC) models trained on clinical data. The Shapley Additive exPlanations (SHAP) method was employed to examine the predictive power of individual variables.
In the training set, SVC outperformed LR slightly (AUC: LR = 0.779 ± 0.054, SVC = 0.792 ± 0.056). However, LR demonstrated better prediction accuracy in both internal (AUC: LR = 0.753, SVC = 0.733) and external validation cohorts (AUC: LR = 0.714, SVC = 0.676). SHAP analysis on the LR model revealed associations between fall risk and both motor and non-motor variables.
ML-based models effectively estimate fall risk across different clinical centers, enabling tailored interventions to enhance PD patients' quality of life. Challenges persist in predicting falls in US-based patients due to demographic and healthcare system differences.
姿势不稳和步态困难是帕金森病（PD）的关键症状，会大幅增加跌倒风险。35%至90%的PD患者会发生跌倒，这是该病管理中的一项重大挑战。准确预测跌倒风险并识别相关因素对于及时干预至关重要。
我们的目标是在意大利的多个中心开发并验证一种机器学习（ML）算法，以使用常规收集的临床数据准确预测跌倒风险并识别相关因素。
来自意大利两个中心的患者数据（N = 251）被分为用于ML模型开发的训练队列（N = 164）和验证队列（N = 87）。对PPMI研究患者的一个子集（N = 65）进行了外部验证。我们比较了基于临床数据训练的逻辑回归（LR）模型和支持向量分类器（SVC）模型的性能。采用夏普利加性解释（SHAP）方法来检验各个变量的预测能力。
在训练集中，SVC略优于LR（曲线下面积：LR = 0.779±0.054，SVC = 0.792±0.056）。然而，LR在内部（曲线下面积：LR = 0.753，SVC = 0.733）和外部验证队列（曲线下面积：LR = 0.714，SVC = 0.676）中均表现出更好的预测准确性。对LR模型的SHAP分析揭示了跌倒风险与运动和非运动变量之间的关联。
基于ML的模型能够在不同临床中心有效估计跌倒风险，从而实现量身定制的干预措施，以提高PD患者的生活质量。由于人口统计学和医疗保健系统的差异，在美国患者中预测跌倒仍然存在挑战。
