P H Duhn, R Christensen, H Locht, M Henriksen, E Ginnerup-Nielsen, H Bliddal, E E Wæhrens, K Thielen, K Amris
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The number of clusters was based on coefficients in the agglomeration schedule, supported by dendrograms and silhouette plots. Simple and multiple regression analyses using all variables as independent predictors were used to assess the likelihood of cluster assignment, reported as odds ratios (ORs) with 95% confidence intervals (CIs).</p><p><strong>Results: </strong>Only one cluster emerged (Cluster 1: 455 patients). Participants in this cluster were characterized as working (OR 66.67, 95% CI 7.14 to 500.00), with a medium-term/higher education (OR 16.80, 95% CI 1.94 to 145.41), married/cohabiting (OR 14.29, 95% CI 1.26 to 166.67), and using mild analgesics (OR 25.64, 95% CI 0.58 to > 999.99). The odds of being an individual in Cluster 1 were lower when having a worse score on the PDQ (score ≥ 18) (OR < 0.001, 95% CI < 0.001 to 0.02).</p><p><strong>Conclusion: </strong>We identified one cluster, where participants were characterized by a potentially favourable clinical profile. More studies are needed to evaluate whether these characteristics could be used to guide the management of patients with CWP and FM.</p>","PeriodicalId":21424,"journal":{"name":"Scandinavian Journal of Rheumatology","volume":" ","pages":"325-334"},"PeriodicalIF":2.2000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Phenotypic characteristics of patients with chronic widespread pain and fibromyalgia: a cross-sectional cluster analysis.\",\"authors\":\"P H Duhn, R Christensen, H Locht, M Henriksen, E Ginnerup-Nielsen, H Bliddal, E E Wæhrens, K Thielen, K Amris\",\"doi\":\"10.1080/03009742.2023.2297514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aimed to explore whether phenotypic characteristics of patients with chronic widespread pain (CWP) and fibromyalgia (FM) can be aggregated into definable clusters that may help to tailor treatments.</p><p><strong>Method: </strong>Baseline variables (sex, age, education, marital/employment status, pain duration, prior CWP/FM diagnosis, concomitant rheumatic disease, analgesics, tender point count, and disease variables derived from standardized questionnaires) collected from 1099 patients (93.4% females, mean age 44.6 years) with a confirmed CWP or FM diagnosis were evaluated by hierarchical cluster analysis. 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引用次数: 0
摘要
研究目的本研究旨在探讨慢性广泛性疼痛(CWP)和纤维肌痛(FM)患者的表型特征是否可以归纳为可定义的群组,从而有助于量身定制治疗方案:通过分层聚类分析评估了从 1099 名确诊为 CWP 或 FM 的患者(93.4% 为女性,平均年龄 44.6 岁)中收集的基线变量(性别、年龄、教育程度、婚姻/就业状况、疼痛持续时间、既往 CWP/FM 诊断、并发风湿病、镇痛剂、压痛点计数以及标准化问卷调查得出的疾病变量)。聚类的数量以聚类表中的系数为基础,并辅以树枝图和剪影图。将所有变量作为独立预测因子进行简单和多元回归分析,以评估聚类分配的可能性,结果以几率比(OR)和 95% 置信区间(CI)报告:只有一个群组出现(群组 1:455 名患者)。该群组的参与者具有以下特征:有工作(OR 66.67,95% CI 7.14 至 500.00)、受过中期/高等教育(OR 16.80,95% CI 1.94 至 145.41)、已婚/同居(OR 14.29,95% CI 1.26 至 166.67)、使用轻度镇痛药(OR 25.64,95% CI 0.58 至大于 999.99)。当 PDQ 得分较低时(得分≥18 分),成为群组 1 成员的几率较低(OR 结论):我们发现了一个群组,其中的参与者具有潜在的有利临床特征。需要进行更多的研究,以评估这些特征是否可用于指导 CWP 和 FM 患者的管理。
Phenotypic characteristics of patients with chronic widespread pain and fibromyalgia: a cross-sectional cluster analysis.
Objective: This study aimed to explore whether phenotypic characteristics of patients with chronic widespread pain (CWP) and fibromyalgia (FM) can be aggregated into definable clusters that may help to tailor treatments.
Method: Baseline variables (sex, age, education, marital/employment status, pain duration, prior CWP/FM diagnosis, concomitant rheumatic disease, analgesics, tender point count, and disease variables derived from standardized questionnaires) collected from 1099 patients (93.4% females, mean age 44.6 years) with a confirmed CWP or FM diagnosis were evaluated by hierarchical cluster analysis. The number of clusters was based on coefficients in the agglomeration schedule, supported by dendrograms and silhouette plots. Simple and multiple regression analyses using all variables as independent predictors were used to assess the likelihood of cluster assignment, reported as odds ratios (ORs) with 95% confidence intervals (CIs).
Results: Only one cluster emerged (Cluster 1: 455 patients). Participants in this cluster were characterized as working (OR 66.67, 95% CI 7.14 to 500.00), with a medium-term/higher education (OR 16.80, 95% CI 1.94 to 145.41), married/cohabiting (OR 14.29, 95% CI 1.26 to 166.67), and using mild analgesics (OR 25.64, 95% CI 0.58 to > 999.99). The odds of being an individual in Cluster 1 were lower when having a worse score on the PDQ (score ≥ 18) (OR < 0.001, 95% CI < 0.001 to 0.02).
Conclusion: We identified one cluster, where participants were characterized by a potentially favourable clinical profile. More studies are needed to evaluate whether these characteristics could be used to guide the management of patients with CWP and FM.
期刊介绍:
Scandinavian Journal of Rheumatology is the official journal of the Scandinavian Society for Rheumatology, a non-profit organization following the statutes of the Scandinavian Society for Rheumatology/Scandinavian Research Foundation. The main objective of the Foundation is to support research and promote information and knowledge about rheumatology and related fields. The annual surplus by running the Journal is awarded to young, talented, researchers within the field of rheumatology.pasting
The Scandinavian Journal of Rheumatology is an international scientific journal covering clinical and experimental aspects of rheumatic diseases. The journal provides essential reading for rheumatologists as well as general practitioners, orthopaedic surgeons, radiologists, pharmacologists, pathologists and other health professionals with an interest in patients with rheumatic diseases.
The journal publishes original articles as well as reviews, editorials, letters and supplements within the various fields of clinical and experimental rheumatology, including;
Epidemiology
Aetiology and pathogenesis
Treatment and prophylaxis
Laboratory aspects including genetics, biochemistry, immunology, immunopathology, microbiology, histopathology, pathophysiology and pharmacology
Radiological aspects including X-ray, ultrasonography, CT, MRI and other forms of imaging.