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

Wenjuan Guo,Minmin Wu,Duoduo Li,Zhenyu Dai,Ye Zheng,Ao Wang,Yanling Feng,Shu Song,Liangping Zhao,Dawei Cui,Xinguo Zhao,Dong Zeng,Yuhan Shi,Jingjing Xu,Yuexiang Yang,Dawei Wang,Hongzhou Lu

doi:10.3389/fpubh.2020.574915

Abstract

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 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 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.

Highlights

In December 2019, an increasing number of patients with pneumonia of unknown cause were found in Wuhan, China [1, 2]
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
Based on the above independent risk factors associated with the severity of COVID-19, we developed a predictive nomogram and validated it using the bootstrap method (Figure 4A)

Summary

Introduction

In December 2019, an increasing number of patients with pneumonia of unknown cause were found in Wuhan, China [1, 2]. On January 12, 2020, the World Health Organization (WHO) named the virus “2019-nCoV” [3], and on February 11, 2020, the WHO renamed it severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it caused coronavirus disease 2019 (COVID-19) [4]. It has been shown that COVID-19 is more contagious than SARS-CoV seen in 2003, and that medical staff were infected during the epidemic [5, 6]. Wu et al [7] first reported that timely antiviral treatment may slow the progression of COVID-19 caused by SARS-CoV2 and improve the prognosis. Nahama et al [8] found that the use of resiniferatoxin could improve patient outcomes in those with advanced COVID-19. Xiao et al [12] developed an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19

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Conclusion