心率变化有助于对系统性硬化症的表型进行分类
Stéphane Delliaux1,2,3,4, Abdou Khadir Sow5,6, Anass Echcherki7
1INSERM, INRAE, C2VN, Aix Marseille Univ, Marseille, France. stephane.delliaux@univ-amu.fr.
Scientific reports
|May 15, 2024
概括
这项研究开发了一种使用心率变化 (HRV) 来分类系统性硬化症 (SSc) 亚型的床边工具. 该工具使用非线性HRV标记器准确区分扩散皮肤SSc (dcSSc) 和有限皮肤SSc (lcSSc).
科学领域:
- 心脏病学 心脏病学
- 自主神经系统研究 自主神经系统研究
- 医学诊断 医学诊断 医学诊断
背景情况:
- 系统性硬化症 (SSc) 是一种复杂的自身免疫性疾病,具有不同的亚型.
- 精确的SSc亚型的分类,如扩散皮肤SSc (dcSSc) 和有限皮肤SSc (lcSSc),对于患者的管理至关重要.
- 目前的诊断方法可能缺乏用于快速分类型差异化的临床应用.
研究的目的:
- 在患者的床边开发和验证一种分类工具,以区分dcSSc和lcSSc亚型.
- 调查心率变化 (HRV) 参数在区分SSc亚型中的有用性.
- 确定最有效的基于HRV的模型,用于SSc亚型分类.
主要方法:
- 在58名SSc患者 (16 dcSSc,38 lcSSc) 中,比较了5分钟的休息和静止心率变化 (HRV).
- 从节拍到节拍RR间隔使用时间,频率和非线性域分析HRV.
- 评估了341个分类模型,使用各种HRV变量组合和算法,评估F1得分和准确性等性能指标.
主要成果:
- 与lcSSc组相比,dcSSc组在休息和静止条件下表现出更高的心率和更低的HRV.
- 在DCSSc和lcSSc组之间,站立动作明显改变了特定的HRV指数 (sd_HR,SD2,CorDim).
- 最好的分类模型使用4个HRV变量实现了0.947的F1最大得分,其中sd_HR,SD2和CorDim具有高度的区别性.
结论:
- 开发了高性能,床边适用的分类模型,以区分dcSSc和lcSSc.
- 这些模型利用1-5非线性HRV指数作为对心脏活动自主影响的标记.
- 这种基于HRV的方法为SSc亚型分类提供了一个有希望的,非侵入性的方法.
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