在当代不分化的关节炎中解脱异质性 - 使用潜伏类分析的大型队列研究
N K den Hollander1, M Verstappen1, B T van Dijk1
1Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
Seminars in arthritis and rheumatism
|August 22, 2023
概括
研究人员使用数据驱动方法在不分化关节炎 (UA) 中确定了五个不同的子组. 这些子组表现出不同的自然疾病进展,影响结果,如自发解决和类风湿性关节炎的发展.
科学领域:
- 类风湿病学 类风湿病学
- 临床免疫学临床免疫学
- 流行病学 流行病学
背景情况:
- 不分化的关节炎 (UA) 呈现出异质的临床图景,结果可变,包括自发解脱或进展为类风湿性关节炎 (RA).
- 在UA中识别不同的子组对于了解疾病轨迹和预测结果至关重要,特别是在早期疾病修饰性抗风湿药物 (DMARD) 治疗不常见的队列中.
研究的目的:
- 根据临床特征在UA内识别同质子组.
- 调查这些已识别的子组之间的关系和自发性关节炎解消和风湿性关节炎在一年内发展的结果.
主要方法:
- 隐性类分析应用于来自莱登早期关节炎诊所 (1993-2006) 的310名自身抗体阴性UA患者队列.
- 根据临床特征的组合确定了子组,并评估了它们与自发解脱和RA发展的关联.
主要成果:
- 确定了5个不同的UA子组,主要通过胀关节的位置和数量来区分.
- 这些子组包括多关节炎,小关节炎和各种形式的单关节炎 (手腕,小关节,大关节) 的模式.
- 单关节子组显示高特异性 (>90%) 预测没有自发解脱 (持续性疾病) 和低敏感性 (<7%) 预测RA发展.
结论:
- 一种数据驱动的,无监督的方法成功地在不分化关节炎中确定了五个临床相关的子组.
- 这些子组表现出不同的自然疾病过程,为预测关节炎的持续性和类风湿性关节炎的发展提供了洞察力.
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