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Multicenter Development and Validation of a Novel Risk Nomogram for Early Prediction of Severe 2019-Novel Coronavirus Pneumonia

28 Pages Posted: 19 Mar 2020

See all articles by Jiao Gong

Jiao Gong

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine

Jingyi Ou

Guangzhou Medical University - Department of Laboratory Medicine

Xueping Qiu

Wuhan University - Center for Gene Diagnosis

Yusheng Jie

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Yaqiong Chen

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine

Lianxiong Yuan

Sun Yat-Sen University (SYSU) - Department of Science and Research

Jing Cao

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Mingkai Tan

Guangzhou Medical University - Department of Laboratory Medicine

Wenxiong Xu

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Fang Zheng

Wuhan University - Center for Gene Diagnosis

Yaling Shi

Guangzhou Medical University - Department of Laboratory Medicine

Bo Hu

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine

More...

Multiple version iconThere are 2 versions of this paper

Abstract

Background: Severe cases of coronavirus disease 2019 (COVID-19) rapidly develop acute respiratory distress leading to respiratory failure, with remarkably high short-term mortality rates. At present, there is no reliable risk stratification tool for COVID-19 patients. We aimed to construct and validate a model for early identification of severe cases of COVID-19.

Methods: SARS-CoV-2 infected patients from two centers in Guangzhou and one center in Wuhan were included retrospectively, and divided into the train and external validation cohorts. All patients with non-severe COVID-19 during hospitalization were followed for more than 15 days following admission and patients who deteriorated to severe COVID-19 were assigned to the severe group. Least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression model were used to construct a nomogram for risk prediction in the train cohort. The predictive accuracy and discriminative ability of nomogram were evaluated by area under the curve (AUC) and calibration curve. Decision curve analysis (DCA) and clinical impact curve analysis (CICA) were conducted to evaluate the clinical applicability of our nomogram.

Findings: The train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We generated the nomogram containing one clinical and six serological indicators (age, serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, albumin, direct bilirubin) that could early identify severe COVID-19 patients. The nomogram showed remarkably high diagnostic accuracy in distinguishing individuals with severe COVID-19 from non-severe COVID-19 (AUC 0.914 [95% CI 0.852–0.976] in the train cohort; 0.856 [0.795-0.916] in validation cohort 1. The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. DCA and CICA further indicated that our nomogram conferred significantly high clinical net benefit.

Interpretation: Our nomogram is a potentially useful prediction tool for risk assessment of COVID-19 patients and early identification of severe COVID-19 patients. Risk stratification will enable better management and optimal use of medical resources via patient prioritization and thus significantly reduce mortality rates.

Funding Statement: Science and Technology Program of Guangzhou, China (201804010474)

Declaration of Interests: The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.

Ethics Approval Statement: The study was approved by the Ethics Committee of the Eighth People's Hospital of Guangzhou (20200547). Written informed consent was waived by the Ethics Commission of the Third Affiliated Hospital of Sun Yat-sen University for emerging infectious diseases.

Keywords: COVID-19; Nomogram; Severe COVID-19 prediction; Risk stratification

Suggested Citation

Gong, Jiao and Ou, Jingyi and Qiu, Xueping and Jie, Yusheng and Chen, Yaqiong and Yuan, Lianxiong and Cao, Jing and Tan, Mingkai and Xu, Wenxiong and Zheng, Fang and Shi, Yaling and Hu, Bo, Multicenter Development and Validation of a Novel Risk Nomogram for Early Prediction of Severe 2019-Novel Coronavirus Pneumonia (3/9/2020). Available at SSRN: https://ssrn.com/abstract=3551365 or http://dx.doi.org/10.2139/ssrn.3551365

Jiao Gong (Contact Author)

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine

Guangzhou, 510080
China

Jingyi Ou

Guangzhou Medical University - Department of Laboratory Medicine

Guangzhou
China

Xueping Qiu

Wuhan University - Center for Gene Diagnosis

Wuhan
China

Yusheng Jie

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Guangzhou
China

Yaqiong Chen

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine

Guangzhou, 510080
China

Lianxiong Yuan

Sun Yat-Sen University (SYSU) - Department of Science and Research

Guangzhou
China

Jing Cao

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Guangzhou
China

Mingkai Tan

Guangzhou Medical University - Department of Laboratory Medicine

Guangzhou
China

Wenxiong Xu

Sun Yat-Sen University (SYSU) - Key Laboratory of Liver Disease of Guangdong Province

Guangzhou
China

Fang Zheng

Wuhan University - Center for Gene Diagnosis ( email )

Wuhan
China

Yaling Shi

Guangzhou Medical University - Department of Laboratory Medicine ( email )

Guangzhou
China

Bo Hu

Sun Yat-Sen University (SYSU) - Department of Laboratory Medicine ( email )

Guangzhou, 510080
China

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