开发一种用于COVID-19感染的新型临床风险评分
James B Baker1, Arnab Ghatak1, Mark R Cullen2
1Clinica AI, New York, New York.
The American journal of medicine
|September 13, 2023
概括
一个新的机器学习模型准确地预测了严重的COVID-19结果,即使是像Omicron这样的新病毒菌株. 它确定了关键的风险因素,并通过更精确地定义严重疾病来改善患者护理策略.
科学领域:
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 新型严重急性呼吸综合征冠状病毒2 (SARS-CoV-2) 菌株的出现,如Omicron,需要精确预测严重的COVID-19结果.
- 现有的预测模型可能无法解释病毒的演变性质及其对疾病严重程度的影响.
研究的目的:
- 开发和验证一种机器学习模型,以准确预测严重的COVID-19结果.
- 识别新型风险因素并完善严重COVID-19的定义,以提高预测准确度.
- 为在不断变化的SARS-CoV-2菌株的背景下提供更好的资源配置和干预策略的工具.
主要方法:
- 从2020年3月到2022年9月使用电子健康记录对超过100万名美国COVID-19诊断的回顾性分析.
- 开发一种机器学习模型来预测严重的结果,定义为急性呼吸衰竭,重症监护室入院或呼吸机使用.
- 评估模型性能并与现有预测因素进行比较,重点关注其绕过住院偏差的能力.
主要成果:
- 机器学习模型在没有实验室数据的情况下预测患者风险时,实现了0.83的曲线下的面积.
- 发现的新风险因素包括急性护理史,医疗保健遭遇和特定药物使用.
- 严重的结局在新菌株时代开始时增加了2-3倍,在不同SARS-CoV-2变种中存在一致的风险因素.
结论:
- 介绍了一种改进的机器学习模型和风险评分,用于预测不同菌株时期的严重COVID-19结果.
- 在临床上更精确地定义严重的结果可以提高对资源配置和干预的洞察力.
- 定期更新模型对于在快速发展的COVID-19大流行中保持相关性至关重要.
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