类风湿关节炎长期状况模式的分类及其与不良健康事件的关联:英国生物银行队列研究

Philip McLoone, Bhautesh D Jani, Stefan Siebert, Fraser R Morton, Jordan Canning, Sara Macdonald, Frances S Mair, Barbara I Nicholl
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引用次数: 1

摘要

目的:我们旨在对RA和≥2个额外长期疾病(LTCs)患者进行分类,并描述不同LTC类别、LTCs数量与不良健康结局之间的关系。方法:我们使用了报告RA的UK Biobank参与者(n=5,625),并采用潜在类别分析(LCA)为那些有≥2个额外LTC的患者创建LTC组合类别。使用Cox-proportional hazard和负二项回归来比较11年随访期间不同LTC类别和RA + 1个额外LTC患者的全因死亡率、主要不良心脏事件(MACE)和急诊住院人数的风险。无LTCs的RA患者为参照组。对人口统计学特征、吸烟、体重指数、饮酒和体育活动进行了调整。结果:共有2566名(46%)参与者报告了除RA外的≥2个LTCs。这涉及1138种不同的LTC组合,其中86%的报告是由≤2个人。LCA确定了5种发病率级别。死亡率最高的类别中最显著的疾病是癌症(第5类;Hr 2.66 95%ci(1.91-3.70))。MACE (HR 2.95 95%CI(2.11-4.14))和急诊住院率(比率比3.01(2.56-3.54))最高的是3级,包括哮喘、慢性阻塞性肺病和冠心病。随着LTCs数量的增加,每个类别的死亡率、MACE和急诊住院率都有所增加。结论:RA的不良健康结局风险随多发病模式的不同而不同。在RA患者的风险评估和制定管理计划时应考虑多发病模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study.

Purpose: We aimed to classify individuals with RA and ≥2 additional long-term conditions (LTCs) and describe the association between different LTC classes, number of LTCs and adverse health outcomes.

Methods: We used UK Biobank participants who reported RA (n=5,625) and employed latent class analysis (LCA) to create classes of LTC combinations for those with ≥2 additional LTCs. Cox-proportional hazard and negative binomial regression were used to compare the risk of all-cause mortality, major adverse cardiac events (MACE), and number of emergency hospitalisations over an 11-year follow-up across the different LTC classes and in those with RA plus one additional LTC. Persons with RA without LTCs were the reference group. Analyses were adjusted for demographic characteristics, smoking, BMI, alcohol consumption and physical activity.

Results: A total of 2,566 (46%) participants reported ≥2 LTCs in addition to RA. This involved 1,138 distinct LTC combinations of which 86% were reported by ≤2 individuals. LCA identified 5 morbidity-classes. The distinctive condition in the class with the highest mortality was cancer (class 5; HR 2.66 95%CI (1.91-3.70)). The highest MACE (HR 2.95 95%CI (2.11-4.14)) and emergency hospitalisations (rate ratio 3.01 (2.56-3.54)) were observed in class 3 which comprised asthma, COPD & CHD. There was an increase in mortality, MACE and emergency hospital admissions within each class as the number of LTCs increased.

Conclusions: The risk of adverse health outcomes in RA varied with different patterns of multimorbidity. The pattern of multimorbidity should be considered in risk assessment and formulating management plans in patients with RA.

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