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A Nomogram for Predicting the Severity of COVID-19 Using Laboratory Examination and CT Findings
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作者 Yani Kuang Susu He +8 位作者 Shuangxiang Lin Rui Zhu Rongzhen Zhou Jian Wang Renzhan Li Haiyong Lin Zhibang Zhang peipei pang Wenbin Ji 《International Journal of Clinical Medicine》 2020年第12期786-809,共24页
<strong>Background:</strong> 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 rapid... <strong>Background:</strong> 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. <strong>Methods:</strong> 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.<strong> Results:</strong> 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, <em>P </em>= 0.001), body temperature (OR: 2.252, 95% CI: 1.139 - 4.450, <em>P</em> = 0.020), lymphocyte count (OR: 1.128, 95% CI: 1.000 - 1.272, <em>P </em>= 0.049), ADA (OR: 1.163, 95% CI: 1.023 - 1.323, <em>P </em>= 0.021), PaO<sub>2</sub> (OR: 0.972, 95% CI: 0.953 - 0.992, P = 0.007), IL-10 (OR: 1.184, 95% CI: 1.037 - 1.351, <em>P</em> = 0.012), and bronchiectasis (OR: 3.818, 95% CI: 1.694 - 8.605, <em>P</em> = 0.001). The AUC of the established nomogram was 0.877. <strong>Conclusions: </strong>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 PaO<sub>2</sub>, high IL-10, and bronchiectasis sign in CT predicts a greater likelihood of severe COVID-19. 展开更多
关键词 COVID-19 SARS-CoV-2 Clinical Characteristics Severity Prediction
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