Early triage of critically ill COVID-19 patients using deep learning.
Wenhua LiangJianhua YaoAilan ChenQingquan LvMark ZaninJun LiuSookSan WongYi-Min LiJiatao LuHengrui LiangGuoqiang ChenHaiyan GuoJun GuoRong ZhouLimin OuNiyun ZhouHanbo ChenFan YangXiao HanWenjing HuanWeimin TangWeijie GuanZisheng ChenYi ZhaoLing SangYuanda XuWei WangShiyue LiLigong LuNuofu ZhangNanshan ZhongJunzhou HuangJian-Xing HePublished in: Nature communications (2020)
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.
Keyphrases
- coronavirus disease
- emergency department
- deep learning
- end stage renal disease
- sars cov
- healthcare
- chronic kidney disease
- ejection fraction
- peritoneal dialysis
- prognostic factors
- public health
- machine learning
- early onset
- chronic pain
- case report
- pain management
- quality improvement
- health information
- patient reported