A Nomogram for Predicting the Severity of COVID-19 Using Laboratory Examination and CT Findings

Kuang, Yani and He, Susu and Lin, Shuangxiang and Zhu, Rui and Zhou, Rongzhen and Wang, Jian and Li, Renzhan and Lin, Haiyong and Zhang, Zhibang and Pang, Peipei and Ji, Wenbin (2020) A Nomogram for Predicting the Severity of COVID-19 Using Laboratory Examination and CT Findings. International Journal of Clinical Medicine, 11 (12). pp. 786-809. ISSN 2158-284X

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Abstract

Background: The outbreak of COVID-19 has a significant impact on the health of people around the world. In the clinical condition of COVID-19, the condition of critical cases changes rapidly with a high mortality rate. Therefore, early prediction of disease severity and active intervention play an important role in the prognosis of severe patients. Methods: All the patients with COVID-19 in Taizhou city were retrospectively included and segregated into the non-severe and severe group according to the severity of the disease. The clinical manifestations, laboratory examination results, and imaging findings of the 2 groups were analyzed for comparing the differences between the 2 groups. Univariate and multivariate logistic regression were used for screening the factors that could predict the disease, and the nomogram was constructed. Results: A total of 143 laboratory-confirmed cases were included in the study, including 110 non-severe patients and 33 severe patients. The median age of patients was 47 years (range, 4 - 86 years). Fever (73.4%) and cough (63.6%) were the most common initial clinical symptoms. By using the method of multivariate logistic regression, the variables to construct nomogram include age (OR: 1.052, 95% CI: 1.020 - 1.086, P = 0.001), body temperature (OR: 2.252, 95% CI: 1.139 - 4.450, P = 0.020), lymphocyte count (OR: 1.128, 95% CI: 1.000 - 1.272, P = 0.049), ADA (OR: 1.163, 95% CI: 1.023 - 1.323, P = 0.021), PaO2 (OR: 0.972, 95% CI: 0.953 - 0.992, P = 0.007), IL-10 (OR: 1.184, 95% CI: 1.037 - 1.351, P = 0.012), and bronchiectasis (OR: 3.818, 95% CI: 1.694 - 8.605, P = 0.001). The AUC of the established nomogram was 0.877. Conclusions: This study analyzed the cases of patients with COVID-19 in Taizhou city and constructed a model to predict the illness severity. When patients showed the features including older age, high body temperature, low lymphocyte count, low ADA value, low PaO2, high IL-10, and bronchiectasis sign in CT predicts a greater likelihood of severe COVID-19.

Item Type: Article
Subjects: Eurolib Press > Medical Science
Depositing User: Managing Editor
Date Deposited: 19 Jan 2023 09:46
Last Modified: 01 Mar 2024 04:00
URI: http://info.submit4journal.com/id/eprint/896

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