关节干扰的位置和程度将类风湿性关节炎分为不同的临床子集
Tjardo D Maarseveen1, Marc P Maurits1, Lavinia Agra Coletto2
1Department of Rheumatology, Leiden University Medical Center, Leiden, Netherlands.
NPJ digital medicine
|October 23, 2025
概括
研究人员使用深度学习确定了四个不同的类风湿性关节炎 (RA) 患者组,或联合参与模式 (JIPs). 这些RA现型表现出不同的治疗反应,并可能指导个性化医疗.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能在医学中的应用
- 临床数据分析 临床数据分析
背景情况:
- 类风湿性关节炎 (RA) 是一种复杂的疾病,症状,预后和治疗反应具有显著的异质性.
- 目前的分类方法可能无法完全捕捉RA中的不同患者亚组.
- 精细的患者分类对于推进研究和个性化治疗策略至关重要.
研究的目的:
- 通过多式模式深度学习和集群,识别不同的类风湿性关节炎 (RA) 表型.
- 为了在独立的队列中验证这些已识别的表型.
- 调查已识别的RA表型中疾病机制和治疗结果的潜在差异.
主要方法:
- 应用多模式深度学习和聚类到1,387名RA患者的基线临床数据.
- 确定了四种不同的关节参与模式 (JIPs):足部占主导地位的关节炎,血清阳性小关节性关节病,血清阴性手关节炎和多关节炎.
- 在临床试验数据 (n=307) 和独立的二级护理队列 (n=515) 中验证的发现;分析了突组织学 (n=194).
主要成果:
- 确定了四种稳定的RA表型 (JIP),显示了缓解率和甲醇失败的显著差异.
- 与JIP-foot和JIP-poly患者相比,JIP手患者显示出更好的结果,特别是ACPA阳性个体.
- 跨集群的明显的突组织学模式表明每个RA表型的潜在生物机制不同.
结论:
- 基于关节参与模式的验证的RA表型为风湿性关节炎提供了一个精细的分类系统.
- 这些独特的表型可能有助于对疾病机制的有针对性的研究和个性化治疗策略的开发.
- 这些发现强调了深度学习在揭示RA等复杂疾病中临床相关的患者亚组方面的潜力.
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