基于双中心数据中心的COVID-19后肺膜异常的预测模型
Xiujuan Yao1,2, Jianman Wu1,3, Wei Zou1,2
1Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350001, China.
Scientific reports
|November 25, 2024
概括
一种新的预测模型可以识别2019年新冠病毒病 (COVID-19) 后患有持续肺部异常风险的患者. 该工具有助于管理COVID-19幸存者的长期肺部后果.
科学领域:
- 肺部病理学 肺部病理学
- 放射学 放射学是一门学科.
- 传染性疾病 传染性疾病
背景情况:
- 2019年冠状病毒病 (COVID-19) 幸存者经常表现出放射和生理异常.
- 及时识别剩余的肺膜异常对于管理慢性肺病的后果至关重要.
研究的目的:
- 开发一种预测模型,用于识别COVID-19后肺部异常高风险的患者.
- 创建一个有效管理长期肺部后果的工具.
主要方法:
- 501名住院COVID-19患者的回顾性队列研究 (2022年7月至2023年3月).
- 使用逐步回归 (Akaike信息标准) 开发预后模型,并使用临床和实验室参数.
- 模型在分割数据集上进行训练和验证; 240名患者在感染后3个月接受了胸部CT扫描.
主要成果:
- 随访CT扫描的患者中,肺部异常的发生率为33.3%.
- 预测模型表现出强大的区分能力 (AUC:0.885) 和出色的校准.
- 决策曲线分析显示了临床应用的实质性净益处.
结论:
- 已经开发出了一种可靠的预后工具,包括一个名图,以估计COVID-19后3个月残留的肺膜异常的概率.
- 这种高性能模型对指导临床干预和改善COVID-19相关肺部后续病例的管理具有前景.
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