‘Not at target’: prevalence and consequences of inadequate disease control in systemic lupus erythematosus—a multinational observational cohort study

Arthritis Research & Therapy(2022)

引用 12|浏览32
暂无评分
摘要
Background The unmet need in systemic lupus erythematosus (SLE) with the current standard of care is widely recognised, but few studies have quantified this. The recent definition of treat-to-target endpoints and other thresholds of uncontrolled disease activity provide an opportunity to formally define unmet need in SLE. In this study, we enumerated the prevalence of these states and examined their association with adverse outcomes. Methods Data were collected prospectively in a 13-country longitudinal SLE cohort between 2013 and 2019. Unmet need was defined as never attaining lupus low disease activity state (LLDAS), a time-adjusted mean SLEDAI-2K (AMS) > 4, or ever experiencing high disease activity status (HDAS; SLEDAI-2K ≥10). Health-related quality of life (HRQoL) was assessed using SF36 (v2) and damage accrual using the SLICC-ACR SLE Damage Index (SDI). Results A total of 3384 SLE patients were followed over 30,313 visits (median [IQR] follow-up 2.4 [0.4, 4.3] years). Eight hundred thirteen patients (24%) never achieved LLDAS. Median AMS was 3.0 [1.4, 4.9]; 34% of patients had AMS > 4. Twenty-five per cent of patients had episodes of HDAS. Each of LLDAS-never, AMS>4, and HDAS-ever was strongly associated with damage accrual, higher glucocorticoid use, and worse HRQoL. Mortality was significantly increased in LLDAS-never (adjusted HR [95% CI ] = 4.98 [2.07, 12.0], p <0.001) and HDAS-ever (adjusted hazard ratio (HR) [95% CI ] = 5.45 [2.75, 10.8], p <0.001) patients. Conclusion Failure to achieve LLDAS, high average disease activity, and episodes of HDAS were prevalent in SLE and were significantly associated with poor outcomes including organ damage, glucocorticoid exposure, poor quality of life, and mortality.
更多
查看译文
关键词
Systemic lupus erythematosus, Disease activity, Outcomes, Quality of life, Unmet need
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要