Objectives Symptoms of people who have systemic sclerosis (SSc) are heterogeneous and difficult to address clinically. Because diverse symptoms often co-occur and may share common underlying mechanisms, identifying symptoms that cluster together may better target treatment approaches. We sought to identify and characterize patient subgroups based on symptom experience. Methods An exploratory hierarchical agglomerative cluster analysis was conducted to identify subgroups from a large SSc cohort from a single US academic medical center. Patient-reported symptoms of pain interference, fatigue, sleep disturbance, dyspnea, depression, and anxiety were used for clustering. A multivariate analysis of variance (MANOVA) was used to examine the relative contribution of each variable across subgroups. Analyses of variance were performed to determine participant characteristics based on subgroup assignment. Presence of symptom clusters were tallied within subgroup. Results Participants (N = 587; 84% female, 41% diffuse cutaneous subtype, 59% early disease) divided into three subgroups via cluster analysis based on symptom severity: (1) no/minimal; (2) mild; and (3) moderate. Participants in mild and moderate symptoms subgroups had similar disease severity, but different symptom presentation. In the mild symptoms subgroup, pain, fatigue, and sleep disturbance was the main symptom cluster. Participants in the moderate symptoms subgroup were characterized by co-occurring pain, fatigue, sleep disturbance, depression, and anxiety. Conclusion Identification of distinct symptom clusters, particularly among SSc patients who experience mild and moderate symptoms, suggest potential differences in treatment approach and in mechanisms underlying symptom experience that require further study.

中文翻译：

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系统性硬化症患者症状体验的差异：识别亚组的聚类分析方法

目标 系统性硬化症 (SSc) 患者的症状多种多样，难以在临床上解决。由于不同的症状经常同时出现，并且可能具有共同的潜在机制，因此识别聚集在一起的症状可能会更好地针对治疗方法。我们试图根据症状经历来识别和描述患者亚组。方法 采用探索性层次凝聚聚类分析来识别来自美国单个学术医疗中心的大型 SSc 队列中的亚组。患者报告的疼痛干扰、疲劳、睡眠障碍、呼吸困难、抑郁和焦虑症状用于聚类。使用多变量方差分析（MANOVA）来检查子组中每个变量的相对贡献。进行方差分析以确定基于亚组分配的参与者特征。统计亚组内症状群的存在情况。结果 参与者（N = 587；84% 为女性，41% 为弥漫性皮肤亚型，59% 为早期疾病）根据症状严重程度通过聚类分析分为三个亚组：(1) 无/极少；(2) 轻度；(3)中等。轻度和中度症状亚组的参与者疾病严重程度相似，但症状表现不同。在轻度症状亚组中，疼痛、疲劳和睡眠障碍是主要症状群。中度症状亚组的参与者的特点是同时出现疼痛、疲劳、睡眠障碍、抑郁和焦虑。结论 不同症状群的识别，特别是在出现轻度和中度症状的 SSc 患者中，表明治疗方法和症状体验背后的机制存在潜在差异，需要进一步研究。