开发和验证一个预后模型,以评估在Omicron波之后的长期COVID风险 - - 一项基于人口的大规模队列研究
Lu-Cheng Fang1,2, Xiao-Ping Ming1,2, Wan-Yue Cai1,2
1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Virology journal
|June 1, 2024
概括
一种新的名图模型可以预测住院患者的长期COVID风险. 该工具有助于早期识别和长期COVID的临床管理,改善COVID-19感染后患者的治疗结果.
科学领域:
- 传染性疾病 传染性疾病
- 临床医学 临床医学
- 流行病学 流行病学
背景情况:
- 长期冠状病毒疾病 (COVID) 构成了全球健康威胁.
- 在住院患者中缺乏长期COVID风险的有效预测模型.
- 早期风险识别对于管理COVID-19患者至关重要.
研究的目的:
- 开发和验证长期COVID风险的可靠预测模型.
- 在住院COVID-19患者中识别长期COVID的关键风险因素.
- 为了帮助临床决策对长期COVID管理.
主要方法:
- 分析了1905名住院的COVID-19患者.
- 长期COVID状态在出院4-8周后进行评估.
- 他们使用了拉索回归,后勤回归和名ogram可视化.
- 用AUC,校准曲线和DCA.来评估模型性能.
主要成果:
- 34.5%的患者出现了长期COVID症状,主要是疲劳,睡眠困难和咳.
- 开发了一个包含年龄,糖尿病,CKD,疫苗接种,前素,白细胞,淋巴细胞,IL-6和D-二次体的诺莫图.
- 该模型表现出良好的预测性能,AUC为0.762 (训练) 和0.713 (验证).
- 校准曲线和DCA证实了模型的准确性和临床实用性.
结论:
- 为预测住院患者的长期COVID风险建立了一种经过验证的诺莫格拉姆模型.
- 该模型显示了相对较好的预测性能,并有助于早期识别高风险个体.
- 这种工具可以显著帮助长期COVID的临床管理.
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