长期COVID作为肌痛性骨髓灰质炎恶化的一个独立预测因素:一项整合机器学习进行风险分层的前性队列研究
Yan Li1, Yimin Tao2, Zhilan Zhao3
1Faculty of Chinese Medicine & State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau, China; Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China; The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.
长期的COVID显著增加了12个月的Myasthenia Gravis恶化风险. 机器学习,特别是随机森林,有效地识别高风险患者,以便更好地管理.
科学领域:
- 神经学 神经学
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
背景情况:
- 骨髓灰质炎 (MG) 是一种慢性自身免疫神经肌肉疾病.
- COVID-19 感染可以引发或加剧MG 症状.
- 长期COVID,COVID-19的后急性后续,可能会影响MG等慢性疾病.
研究的目的:
- 为了评估长期COVID对Myasthenia Gravis恶化的影响.
- 为了确定MG患者在COVID-19后患有恶化的高风险.
- 利用机器学习进行预测风险分层.
主要方法:
- 研究了一组180名患有COVID-19联合感染的MG患者.
- 结构化采访评估了长期COVID和MG恶化在感染后的1,6个月和12个月.
- 考克斯回归和机器学习 (随机森林) 用于相关性和预测.
主要成果:
- 34.4%的MG患者在COVID-19后12个月内经历了恶化.
- 长期COVID是MG恶化最强的预测因素 (aHR=2.55).
- 随机森林模型实现了0.83的AUC,确定长期COVID作为一个关键的预测特征.
结论:
- 长期的COVID显着与肌痛严重症恶化增加有关.
- 机器学习模型,特别是随机森林,对分层MG恶化风险显示出希望.
- 早期识别高风险患者可以指导主动管理策略.
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