综合机器学习和贝叶斯分析揭示了心房动作为严重COVID-19结果的关键预测因素
Young Sook Ku1,2, Go Woon Lee1, Hyundeok Seo3
1College of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Clinical and translational science
|November 17, 2025
概括
在接受雷梅西维尔治疗的住院COVID-19患者中确定了关键结果的预测因素. 晚年,心房动和病显著增加死亡风险,心房动是一致的预测因素.
科学领域:
- 关键护理医学 关键护理医学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- COVID-19仍然是一个重大的全球健康挑战,需要确定严重后果的风险因素.
- 雷梅西维尔是住院COVID-19患者的关键抗病毒治疗方法.
- 预测关键结果有助于风险分层和临床决策.
研究的目的:
- 确定接受remdesivir的住院COVID-19患者中关键结果的预测因素,包括死亡率.
- 为了比较统计,机器学习和贝叶斯方法的预测性能.
主要方法:
- 对1628名住院COVID-19患者的回顾性分析 (2021年1月 - 2022年8月).
- 使用多变量逻辑回归,机器学习 (LightGBM,弹性网) 和SHAP,以及贝叶斯逻辑回归.
- 从电子医疗记录中提取的临床数据.
主要成果:
- 14.5%的患者经历了严重的结果或死亡.
- 重要的预测因素包括晚年 (≥65岁),心房动和病.
- 机器学习模型显示预测性能中等 (AUROC ~0.70);SHAP将年龄和心房动确定为关键因素.
- 贝叶斯分析证实了心房动与不良结果之间的强烈关联 (aOR 5.121).
结论:
- 心房动是用雷梅西维尔治疗的COVID-19患者中关键结果的一致且强有力的预测因素.
- 晚年和脏疾病也预测不良事件.
- 这些发现强调了心房动在COVID-19患者临床风险评估中的重要性.
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