Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease

Frontiers in Public Health

article

Zhenyu Dai¹  Yuexiang Yang²  Xinguo Zhao³  Duoduo Li²  Ye Zheng²  Ao Wang²  Minmin Wu²  Shu Song²  Hongzhou Lu⁴  Dong Zeng²  Dawei Cui⁵  Dawei Wang⁶  Yanling Feng²  Yuhan Shi²  Liangping Zhao⁷  Jingjing Xu²  Wenjuan Guo² 

[1] Department of Invasive Technology, Yancheng Clinical Medical College of Nanjing Medical University;Department of Pathology, Shanghai Public Health Clinical Center, Fudan University;Department of Respiration, The Fifth People's Hospital of Wuxi;Department of Infectious Disease and Immunology, Shanghai Public Health Clinical Center, Fudan University;Department of Blood Transfusion, The First Affiliated Hospital, Zhejiang University School of Medicine;Department of Infectious Disease, The Second People's Hospital of Yancheng City;Department of Gynecology and Obstetrics, Tongji Medical College, Wuhan Central Hospital, Huazhong University of Science and Technology

关键词: COVID-19;    severity;    risk factors;    scoring model;    nomogram;   

DOI  :  10.3389/fpubh.2020.574915

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96–5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91–7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68–5.96), and age ≥60 years (HR 2.31, 95% CI 1.43–3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83–0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 ( R 2 = 0.89) in the 7-day prediction and 0.96 ( R 2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81–0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

【 授权许可】

CC BY