雷姆德西维尔与住院COVID-19患者的死亡率降低有关:使用真实世界的数据和自然语言处理的治疗有效性
José Ramón Arribas López1, María Pilar Ruiz Seco2, Francisco Fanjul3
1Infectious Diseases Unit, Internal Medicine Service, Hospital Universitario La Paz, Hospital La Paz Institute for Health Research (IdiPAZ), Ciber Enfermedades Infecciosas (CIBERINFEC), Madrid, Spain. joser.arribas@salud.madrid.org.
BMC infectious diseases
|April 11, 2025
概括
在中度至重度肺炎的住院COVID-19患者中,雷梅西维尔 (RDV) 治疗显著降低了住院患者的死亡率. 这项研究强调了人工智能技术在产生真实世界的证据,在健康危机期间验证治疗的潜力.
科学领域:
- 人工智能在医学中的应用
- 临床药理学 临床药理学
- 医疗信息学 医疗信息学
背景情况:
- 雷梅西维尔 (RDV) 是早期批准的COVID-19抗病毒药物,在临床试验中显示出改善的结果.
- 人工智能产生的真实世界证据 (RWE) 可以在健康危机期间加快治疗验证.
- 评估RDV对COVID-19死亡率和出院时间等结果的影响至关重要.
研究的目的:
- 在住院COVID-19患者中评估Remdesivir (RDV) 的有效性.
- 评估RDV对住院死亡率和出院时间的影响.
- 证明自然语言处理 (NLP) 和机器学习 (ML) 在生成治疗评估的RWE中的实用性.
主要方法:
- 从84,408名患者的7,651,773个记录中对电子健康记录 (EHR) 的回顾性分析.
- 使用NLP (EHRead®) 识别4882名具有中度至重度COVID-19肺炎的RDV合格患者.
- 倾向性得分匹配 (PSM) 与812名RDV治疗 (RDV+) 患者和2,703名RDV未治疗 (RDV-) 患者进行了比较.
主要成果:
- 治疗RDV与28天住院死亡风险比率显著降低 (HR 0.73; p=0.022).
- 风险差异和风险比率有利于RDV+组,表明死亡率降低.
- 在RDV治疗和未治疗组之间,没有观察到医院住院时间的显著差异.
结论:
- NLP和ML成功生成了RWE,证实了RDV在降低COVID-19肺炎住院患者死亡率方面的有效性.
- 遵守临床指南和利用技术来识别患者对于优化治疗结果至关重要.
- 这种方法在未来的流行病中对评估治疗方法充满希望.
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