Risk factors related to the severity of COVID-19 in Wuhan

Abstract

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test ( P <0.01) and univariate logistic regression analysis ( P <0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.

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“…After a full-text revision of the remaining 14 articles, one was excluded because of missing information, leaving 13 studies for further analysis (Fig. 1 ) [ 30 – 42 ]. The characteristics of these studies, all conducted in China, are described in Tables 1 , 2 .…”

The Severity of COVID-19 and Its Determinants: A Systematic Review and Meta-Analysis in China

“…Other researchers also found that D-dimer levels were higher in severe patients [97]. Additionally, PCT could be a risk factor to predict the severity of COVID-19 [98]. AST [99][100][101], creatinine [102][103][104], fibrinogen [105][106][107], neutrophils [108,109], and respiratory rate [14,110] were reported to be positively related to the severity of COVID-19.…”

Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

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