Population Health Research Institute, Center for Health Care Research and Policy, MetroHealth Medical Center, School of Medicine, Case Western Reserve University, Cleveland, OH, USA; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Neurological Institute, University Hospitals Cleveland Medical Center and Case Western Reserve University School of Medicine, Cleveland, OH, USA.
Parkinsonism Relat Disord. 2022 Sep;102:42-50. doi: 10.1016/j.parkreldis.2022.07.006. Epub 2022 Jul 18.
Pain is a common and complex symptom in Parkinson's disease. The underlying mechanisms and longitudinal patterns are not well understood, which impedes therapeutic decision making. The objectives of this study were to characterize longitudinal pain trajectories, identify clusters (subgroups) with similar patterns, and examine associations with sociodemographic and clinical characteristics.
Latent class growth analysis was applied to 16,863 people with Parkinson's disease stratified by early (N = 8612; <3 years), mid (N = 6181; 3-10 years) and later (N = 2070; >10 years) disease duration over ∼4.5 years (2017-2021) using the Fox Insight Data Exploration Network, to discern clusters of individuals with similar longitudinal patterns of self-reported pain. Associations were evaluated between cluster membership and sociodemographic and clinical factors.
Across the disease duration strata, five clusters were identified. The clusters ranged from none to moderate pain, with a small cluster of subjects with severe pain. The percentage of subjects with moderate (early = 17.3%, mid = 24.2%, later = 34.4%) and severe (early = 2.3%, mid = 4.4%, later = 6.5%) pain at baseline increased across disease duration groups. The trajectories tended to be variable or slightly worsening in the early duration group, more stable in the mid duration group, and improving in the later duration group. Across strata, the clusters with moderate to severe pain were associated with more severe impairment, depression, anxiety and arthritis, higher body mass index, lower income, and lower education.
This latent class growth analysis, applied to people with Parkinson's disease, provides a template for using self-reported outcomes to improve our understanding of pain trajectories.
疼痛是帕金森病的一种常见且复杂的症状。其潜在机制和纵向模式尚不清楚，这阻碍了治疗决策的制定。本研究的目的是描述纵向疼痛轨迹，确定具有相似模式的聚类（亚组），并研究与社会人口学和临床特征的关联。
应用潜在类别增长分析，对来自 Fox Insight Data Exploration Network 的 16863 名帕金森病患者（早期[<3 年，N=8612]、中期[3-10 年，N=6181]和晚期[>10 年，N=2070]）进行分层，以识别具有相似纵向自我报告疼痛模式的个体聚类。评估聚类成员与社会人口学和临床因素之间的关联。
在整个疾病持续时间范围内，确定了五个聚类。聚类范围从无疼痛到中度疼痛，伴有一小部分严重疼痛的患者。中度疼痛（早期=17.3%，中期=24.2%，晚期=34.4%）和严重疼痛（早期=2.3%，中期=4.4%，晚期=6.5%）的患者比例随着疾病持续时间的增加而增加。在早期持续时间组中，轨迹倾向于多变或略有恶化，在中期持续时间组中较为稳定，而在晚期持续时间组中则有所改善。在各个时间范围内，中重度疼痛的聚类与更严重的功能障碍、抑郁、焦虑和关节炎、更高的身体质量指数、更低的收入和更低的教育程度相关。
本潜在类别增长分析应用于帕金森病患者，为使用自我报告的结果来提高我们对疼痛轨迹的理解提供了模板。
