The present study identified seven risk factors that were highly associated with the severity of COVID-19, which were dyspnea, age, decreased lymphocytes, elevated CRP, exposure to a high-transmission setting, elevated AST, and decreased calcium. Severe cases usually had dyspnea or hypoxemia one week after the onset of symptoms and progressed to acute respiratory distress syndrome (ARDS), septic shock, and multiple organ failures. Judging from the cases currently being treated, most patients had a good prognosis, and a few patients were critically ill. Considering the rapid progression of severe COVID-19, it was critical to predict and prevent the severe condition.
The univariate regression analysis illustrated the severity was associated with age, sex, comorbidities (hypertension and diabetes), dyspnea, respiratory rate, white blood cell, lymphocyte, CRP, hypokalemia, and LDH, which is consistent with the findings in several previous studies (Table 1, Table 2 and Figure 1) [12, 13]. As mentioned already, most of the previous studies individually looked into the possible risk factors, which is disadvantageous in ranking the risk factors according to OR values, as compared with the current multiple logistic regression model that investigated the interactive effects by the possible risk factors. The observed independent variables in this study could simultaneously reflect the possible injuries of multiple organs caused by COVID-19. The injuries contributed to the outcomes in the form of moderate or severe condition. Therefore, this model could control confounding factors and screen out the significant risk factors that might have an impact on the severity of COVID-19.
The present study firstly observed a higher prevalence of hypocalcemia in the severe COVID-19 cases. This association (OR = 5.79) was among the two most powerful risk factors, besides dyspnea (OR=5.91). The finding suggested that hypocalcemia might be the early warning factor for the trend of critical illness in patients with COVID-19. A recent review about Ca2+ and virus infection explained that during the process of virus infection, the virus uses the host cell’s environment to replicate and induce host cell dysfunction by capturing the calcium signal system in the host cell [14]. Due to the hijack of Ca2+ system of the host's cells, the virus can inhibit T-cell reactivity, anti-apoptosis, and other functions and affect the occurrence and progression of the disease. Notably, this study also found that the severity was associated with the exposure to a high-transmission setting (OR=5.04) due to the repeat exposure to multiple points of transmission sources that might result in a stronger immune response, as previously reported [15].
The final prediction model included seven risk factors that were very comprehensive in terms of patients’ systemic responses to SARS-CoV-2. Age was widely reported as a major demographic feature that is highly related to severe COVID-19 [16]. The high transmission setting exposure was representative of transmission [15]. Dyspnea represented the affected respiratory function. CRP and reduced lymphocytes represented the inflammatory response. AST represented the liver and cardiovascular tissues that often attacked by the virus. Calcium represented the unbalanced electrolytes. The comprehensive representation was an important advantage of the multiple logistic regression over the univariate association. Although sex, cough, sputum, fatigue, hypertension, and diabetes were also associated with COVID-19 severity, as illustrated by univariate analysis. However, these variables were not finally included in the multiple regression model, which might be due to the small number of cases and the relatively low prevalence of these risk factors concerning these variables in moderate cases. Anyway, several risk factors, such as hypertension and diabetes, are well-accepted risk factors of bad progression of COVID-19 and should be taken into consideration in predicting the prognosis of COVID-19.
The major shortcoming in this study was the limited number of samples. Other possible risk factors, such as obesity, diabetes, and hypertension, were not included due to the low prevalence of these comorbidities. The seven risk factors included in the prediction model were indicative of the systemic responses to the SARS-CoV-2 infection. The combination of seven risk factors could partly represent comorbidities with low prevalence.
To summarize, the present study has identified that age, dyspnea, exposure to a high-transmission setting, reduced lymphocyte, elevated CRP, elevated AST, and decreased calcium were highly associated with the severity of COVID-19. Based on the multiple analysis of the risk factors, we developed a multiple logistic regression prediction modeling for severe COVID-19 cases. This quantitative prognosis prediction model can provide a theoretical basis for the early formulation of individualized diagnosis, treatment programs, and prevention of severe conditions.