Individualized prediction nomograms for disease progression in mild COVID‐19. Issue 10 (17th May 2020)

• Record Type: Journal Article
• Authors: Huang, Jiaofeng; Cheng, Aiguo; Lin, Su; Zhu, Yueyong; Chen, Gongping
• Other Names: Luo Guangxiang (George) guestEditor.; Ly Hinh guestEditor.; Gao Shou‐Jiang guestEditor.
• Is Part Of: Journal of medical virology. Volume 92:Issue 10(2020)
• Page Start: 2074
• Page End: 2080
• Publication Date: 2020-05-17
• Subjects: COVID‐19 -- mild -- nomogram -- progression -- risk factor; Virology -- Periodicals; 616
• DOI: 10.1002/jmv.25969
• Languages: English
• ISSNs: 0146-6615

Abstract

Abstract: The coronavirus disease 2019 (COVID‐19) has evolved into a pandemic rapidly. The majority of COVID‐19 patients are with mild syndromes. This study aimed to develop models for predicting disease progression in mild cases. The risk factors for the requirement of oxygen support in mild COVID‐19 were explored using multivariate logistic regression. Nomogram as visualization of the models was developed using R software. A total of 344 patients with mild COVID‐19 were included in the final analysis, 45 of whom progressed and needed high‐flow oxygen therapy or mechanical ventilation after admission. There were 188 (54.7%) males, and the average age of the cohort was 52.9 ± 16.8 years. When the laboratory data were not included in multivariate analysis, diabetes, coronary heart disease, T ≥ 38.5℃ and sputum were independent risk factors of progressive COVID‐19 (Model 1). When the blood routine test was included the CHD, T ≥ 38.5℃ and neutrophil‐to‐lymphocyte ratio were found to be independent predictors (Model 2). The area under the receiver operator characteristic curve of model 2 was larger than model 1 (0.872 vs 0.849, P = .023). The negative predictive value of both models was greater than 96%, indicating they could serve as simple tools for ruling out the possibility of disease progression. In conclusion, two models comprised common symptoms (fever and sputum), underlying diseases (diabetes and coronary heart disease) and blood routine test are developed for predicting the future requirement of oxygen support in mild COVID‐19 cases. Highlights: This study developed two models and visualized as nomogram for predicting the disease progression in mild COVID‐19. Model 1 only included symptoms and underlying diseases, which was more convenient for patients on self‐isolation to use. Blood routine tests have been added in model 2, which was mainly developed for doctors to assess the risk with simple laboratory examination and facilitated early decision making.