Deng Penghui, Xu Kun, Zhou Xiaoxia, Xiang Yaqin, Xu Qian, Sun Qiying, Li Yan, Yu Haiqing, Wu Xinyin, Yan Xinxiang, Guo Jifeng, Tang Beisha, Liu Zhenhua
Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Aging Neurosci. 2022 Jul 29;14:938071. doi: 10.3389/fnagi.2022.938071. eCollection 2022.
Although risk factors for excessive daytime sleepiness (EDS) have been reported, there are still few cohort-based predictive models for EDS in Parkinson's disease (PD). This 1-year longitudinal study aimed to develop a predictive model of EDS in patients with PD using a nomogram and machine learning (ML).
A total of 995 patients with PD without EDS were included, and clinical data during the baseline period were recorded, which included basic information as well as motor and non-motor symptoms. One year later, the presence of EDS in this population was re-evaluated. First, the baseline characteristics of patients with PD with or without EDS were analyzed. Furthermore, a Cox proportional risk regression model and XGBoost ML were used to construct a prediction model of EDS in PD.
At the 1-year follow-up, EDS occurred in 260 of 995 patients with PD (26.13%). Baseline features analysis showed that EDS correlated significantly with age, age of onset (AOO), hypertension, freezing of gait (FOG). In the Cox proportional risk regression model, we included high body mass index (BMI), late AOO, low motor score on the 39-item Parkinson's Disease Questionnaire (PDQ-39), low orientation score on the Mini-Mental State Examination (MMSE), and absence of FOG. Kaplan-Meier survival curves showed that the survival prognosis of patients with PD in the high-risk group was significantly worse than that in the low-risk group. XGBoost demonstrated that BMI, AOO, PDQ-39 motor score, MMSE orientation score, and FOG contributed to the model to different degrees, in decreasing order of importance, and the overall accuracy of the model was 71.86% after testing.
In this study, we showed that risk factors for EDS in patients with PD include high BMI, late AOO, a low motor score of PDQ-39, low orientation score of MMSE, and lack of FOG, and their importance decreased in turn. Our model can predict EDS in PD with relative effectivity and accuracy.
尽管已有关于日间过度嗜睡（EDS）风险因素的报道，但帕金森病（PD）中基于队列的EDS预测模型仍然很少。这项为期1年的纵向研究旨在使用列线图和机器学习（ML）开发PD患者EDS的预测模型。
共纳入995例无EDS的PD患者，记录基线期的临床数据，包括基本信息以及运动和非运动症状。1年后，重新评估该人群中EDS的存在情况。首先，分析有或无EDS的PD患者的基线特征。此外，使用Cox比例风险回归模型和XGBoost机器学习构建PD中EDS的预测模型。
在1年随访中，995例PD患者中有260例出现EDS（26.13%）。基线特征分析表明，EDS与年龄、发病年龄（AOO）、高血压、步态冻结（FOG）显著相关。在Cox比例风险回归模型中，纳入了高体重指数（BMI）、晚期AOO、帕金森病问卷39项（PDQ - 39）的低运动评分、简易精神状态检查表（MMSE）的低定向评分以及无FOG。Kaplan - Meier生存曲线显示，高危组PD患者的生存预后明显差于低危组。XGBoost表明，BMI、AOO、PDQ - 39运动评分、MMSE定向评分和FOG对模型有不同程度的贡献，重要性依次降低，测试后模型的总体准确率为71.86%。
在本研究中，我们表明PD患者中EDS的风险因素包括高BMI、晚期AOO、PDQ - 39的低运动评分、MMSE的低定向评分以及缺乏FOG，其重要性依次降低。我们的模型可以相对有效且准确地预测PD中的EDS。
