Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset.

IF 2.1 Q3 RHEUMATOLOGY
Rheumatology Advances in Practice Pub Date : 2025-04-18 eCollection Date: 2025-01-01 DOI:10.1093/rap/rkaf031
Vincenzo Venerito, Sergio Del Vescovo, Sergio Prieto-González, Marco Fornaro, Lorenzo Cavagna, Florenzo Iannone, Masataka Kuwana, Vishwesh Agarwal, Jessica Day, Mrudula Joshi, Sreoshy Saha, Kshitij Jagtap, Wanruchada Katchamart, Phonpen Akarawatcharangura Goo, Binit Vaidya, Tsvetelina Velikova, Parikshit Sen, Samuel Katsuyuki Shinjo, Ai Lyn Tan, Nelly Ziade, Marcin Milchert, Abraham Edgar Gracia-Ramos, Carlo V Caballero-Uribe, Hector Chinoy, Latika Gupta, Vikas Agarwal
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引用次数: 0

Abstract

Objectives: To comprehensively compare the disease burden among patients with RA, PsA and AS using Patient-Reported Outcome Measurement Information System (PROMIS) scores and to identify distinct patient clusters based on comorbidity profiles and PROMIS outcomes.

Methods: Data from the global COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 e-survey were analysed. Patients with RA, PsA or AS undergoing treatment with DMARDs were included. PROMIS scores (global physical health, global mental health, fatigue 4a and physical function short form 10a), comorbidities and other variables were compared among the three groups, stratified by disease activity status. Unsupervised hierarchical clustering with eXtreme Gradient Boosting feature importance analysis was performed to identify patient subgroups based on comorbidity profiles and PROMIS outcomes.

Results: The study included 2561 patients (1907 RA, 311 PsA, 343 AS). After adjusting for demographic factors, no significant differences in PROMIS scores were observed among the three groups, regardless of disease activity status. Clustering analysis identified four distinct patient groups: low burden, comorbid PsA/AS, low burden with depression and high-burden RA. Feature importance analysis revealed PROMIS global physical health as the strongest determinant of cluster assignment, followed by depression and diagnosis. The comorbid PsA/AS and high-burden RA clusters showed a higher prevalence of comorbidities (56.47% and 69.7%, respectively) and depression (41.18% and 41.67%, respectively), along with poorer PROMIS outcomes.

Conclusion: Disease burden in inflammatory arthritis is determined by a complex interplay of factors, with physical health status and depression playing crucial roles. The identification of distinct patient clusters suggests the need for a paradigm shift towards more integrated care approaches that equally emphasize physical and mental health, regardless of the underlying diagnosis.

炎症性关节炎中的疾病负担:基于covid -2电子调查数据集的无监督机器学习方法
目的:利用患者报告的结果测量信息系统(PROMIS)评分,全面比较RA、PsA和AS患者的疾病负担,并根据合并症概况和PROMIS结果确定不同的患者群。方法:对全球自身免疫性疾病COVID-19疫苗接种(COVAD) 2电子调查数据进行分析。接受DMARDs治疗的RA、PsA或AS患者被纳入研究。按疾病活动状态分层,比较三组患者的PROMIS评分(整体身体健康、整体心理健康、疲劳4a和身体功能短表10a)、合并症和其他变量。采用无监督分层聚类和极端梯度增强特征重要性分析,根据合并症概况和PROMIS结果确定患者亚组。结果:共纳入2561例患者(RA 1907例,PsA 311例,AS 343例)。在调整人口统计学因素后,无论疾病活动状态如何,三组之间的PROMIS评分均无显著差异。聚类分析确定了四种不同的患者组:低负担、合并症PsA/AS、低负担伴抑郁和高负担RA。特征重要性分析显示,PROMIS整体身体健康状况是聚类分配的最强决定因素,其次是抑郁和诊断。合并症PsA/AS和高负担类风湿性关节炎集群显示出更高的合并症患病率(分别为56.47%和69.7%)和抑郁症患病率(分别为41.18%和41.67%),以及较差的PROMIS结果。结论:炎症性关节炎的疾病负担是多种因素共同作用的结果,身体健康状况和抑郁情绪在其中起重要作用。对不同患者群的识别表明,需要向更加综合的护理方法转变,同样强调身心健康,而不管潜在的诊断如何。
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来源期刊
Rheumatology Advances in Practice
Rheumatology Advances in Practice Medicine-Rheumatology
CiteScore
3.60
自引率
3.20%
发文量
197
审稿时长
11 weeks
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