对于COVID-19疾病结果的预测模型
Cynthia Y Tang1,2,3,4, Cheng Gao1,2,3,5, Kritika Prasai1,2,3,5
1Center for Influenza and Emerging Infectious Diseases, University of Missouri, Columbia, Missouri, USA.
Emerging microbes & infections
|June 3, 2024
概括
个性化COVID-19风险模型预测住院,ICU入院和长期COVID. 整合宿主,环境和病毒数据可以改善针对性干预的患者结果预测.
科学领域:
- 传染性疾病 传染性疾病
- 基因组学就是基因组学.
- 机器学习 机器学习
背景情况:
- 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 已造成数百万人的死亡和长期健康问题.
- COVID-19具有广泛的疾病严重程度,从无症状病例到死亡病例.
- 有效的管理需要准确预测个体患者的结果.
研究的目的:
- 开发个性化的风险评估模型,用于预测COVID-19患者的临床结果.
- 确定住院,重症监护室 (ICU) 住院和长期COVID的关键预测因素.
- 为了告知有针对性的干预措施,并加强患者监测.
主要方法:
- 在密苏里州对4450个人进行了回顾性横截面研究,使用了测序的SARS-CoV-2样本.
- 将病毒基因组数据与临床病史,病程和城乡分类相结合.
- 机器学习模型的开发,以预测住院,ICU入院和长期COVID.
主要成果:
- 住院预测因素包括免疫抑制,心血管疾病,老年,特定症状,农村居住和病毒遗传标记.
- 集中治疗室的入院与急性呼吸窘迫综合征,通风,细菌联合感染,农村居住和非野生型SARS-CoV-2变种有关.
- 长期COVID与住院,通风和女性性有关.
结论:
- 开发的风险评估模型可以识别需要加强监测或早期干预的COVID-19患者.
- 整合病毒,宿主和环境因素对于准确的患者结果预测至关重要.
- 这些模型为可适应其他传染病的个性化医疗提供了一个平台.
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