Xu Xiaozhou, Zhang Shushan, Xu Chuanying, Zhang Wei, Zhao Hui, Liu Yumeng, Zhai Shilei, Zu Jie, Li Zhining, Xiao Lishun
Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.
Office of Hospital Quality and Safety Management, The First People's Hospital of Lianyungang, Lianyungang, China.
Front Neurol. 2025 Aug 20;16:1597132. doi: 10.3389/fneur.2025.1597132. eCollection 2025.
Motor symptoms of Parkinson's disease (PD) patients affect their ability of daily activities. Identifying distinct trajectories of motor symptom progression in PD patients can facilitate long-term management.
A total of 155 PD patients were acquired from the Parkinson's Disease Progression Marker Initiative (PPMI). Distinct longitudinal trajectory clusters of motor symptom progression in PD patients were identified by unsupervised self-organizing maps (SOMs), and baseline characteristics were compared among different clusters. Linear mixed-effect analysis was utilized to estimate the longitudinal courses of some cardinal motor symptoms among clusters, while survival analysis was used to compare time-to-clinical milestones within 5 years. The support vector machine (SVM) was built to predict patients' trajectory clusters, and its performance was evaluated through the mean area under the receiver-operating characteristic curve (mAUC), accuracy and macro -score. Shapley values were calculated to interpret individual variability.
The optimal clusters by SOMs are 3. Cardinal motor symptoms of the progressive cluster worsened more rapidly, and this cluster is more likely to have impaired balance, loss of independence, sleep disturbance, and cognitive impairment within 5 years. The mAUC, accuracy, and macro -score of multi-class SVM model were 0.8846, 0.7692, and 0.7778, respectively. An interactive web application was developed to predict the individual's trajectory cluster.
Subtyping motor symptom progression into different trajectories can improve patients' management. Using baseline data to predict which trajectory cluster a patient belongs to may help develop interventions.
帕金森病（PD）患者的运动症状会影响其日常活动能力。识别PD患者运动症状进展的不同轨迹有助于长期管理。
从帕金森病进展标志物计划（PPMI）中纳入了155例PD患者。通过无监督自组织映射（SOM）识别PD患者运动症状进展的不同纵向轨迹簇，并比较不同簇之间的基线特征。采用线性混合效应分析估计各簇中一些主要运动症状的纵向病程，同时采用生存分析比较5年内达到临床里程碑的时间。构建支持向量机（SVM）来预测患者的轨迹簇，并通过受试者操作特征曲线下的平均面积（mAUC）、准确率和宏分数评估其性能。计算Shapley值以解释个体变异性。
SOM确定的最佳簇数为3个。进展簇的主要运动症状恶化更快，该簇在5年内更易出现平衡受损、失去独立性、睡眠障碍和认知障碍。多类SVM模型的mAUC、准确率和宏分数分别为0.8846、0.7692和0.7778。开发了一个交互式网络应用程序来预测个体的轨迹簇。
将运动症状进展分为不同轨迹可改善患者管理。利用基线数据预测患者所属的轨迹簇可能有助于制定干预措施。
