Shin Yong-Woo, Byun Jung-Ick, Sunwoo Jun-Sang, Rhee Chae-Seo, Shin Jung-Hwan, Kim Han-Joon, Jung Ki-Young
Department of Neurology, Inha University Hospital, Incheon 22332, Republic of Korea.
Department of Neurology, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea.
Clocks Sleep. 2025 Apr 11;7(2):19. doi: 10.3390/clockssleep7020019.
Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is recognized as a precursor to neurodegenerative diseases. This study aimed to develop predictive models for the timing and subtype of phenoconversion in iRBD. We analyzed comprehensive clinical data from 178 individuals with iRBD over a median follow-up of 3.6 years and applied machine learning models to predict when phenoconversion would occur and whether progression would present with motor- or cognition-first symptoms. During follow-up, 30 patients developed a neurodegenerative disorder, and the extreme gradient boosting survival embeddings-Kaplan neighbors (XGBSE-KN) model demonstrated the best performance for timing (concordance index: 0.823; integrated Brier score: 0.123). Age, antidepressant use, and Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III scores correlated with higher phenoconversion risk, while coffee consumption was protective. For subtype classification, the RandomForestClassifier achieved the highest performance (Matthews correlation coefficient: 0.697), indicating that higher Montreal Cognitive Assessment scores and younger age predicted motor-first progression, whereas longer total sleep time was associated with cognition-first outcomes. These findings highlight the utility of machine learning in guiding prognosis and tailored interventions for iRBD. Future research should include additional biomarkers, extend follow-up, and validate these models in external cohorts to ensure generalizability.
孤立性快速眼动（REM）睡眠行为障碍（iRBD）被认为是神经退行性疾病的前驱症状。本研究旨在建立iRBD表型转化时间和亚型的预测模型。我们分析了178例iRBD患者的综合临床数据，中位随访时间为3.6年，并应用机器学习模型预测表型转化的时间以及病情进展是否会出现运动首发或认知首发症状。在随访期间，30例患者发展为神经退行性疾病，极端梯度提升生存嵌入-卡普兰邻域（XGBSE-KN）模型在预测时间方面表现最佳（一致性指数：0.823；综合Brier评分：0.123）。年龄、抗抑郁药使用情况以及运动障碍协会统一帕金森病评定量表第三部分评分与更高的表型转化风险相关，而喝咖啡具有保护作用。对于亚型分类，随机森林分类器表现最佳（马修斯相关系数：0.697），这表明更高的蒙特利尔认知评估得分和更年轻的年龄预示着运动首发进展，而更长的总睡眠时间与认知首发结局相关。这些发现凸显了机器学习在指导iRBD预后和个性化干预方面的作用。未来的研究应纳入更多生物标志物，延长随访时间，并在外部队列中验证这些模型以确保其可推广性。
