The rapid and extensive spread of SARS-CoV-2 infection in China and in the world has resulted in a tremendous loss of safety in peoples’ lives (15). Therefore, identifying risk factors on admission to predict the likelihood of disease progression, would be beneficial to physicians when they are making a reasonable decision on patient management. Our study provides comprehensive data on the epidemiological, demographic, clinical and laboratory characteristics of 239 hospitalized patients with COVID-19 in the first hospital of Changsha, which is the largest local dedicated hospital for treating COVID-19 patients. Hence, it may represent the general situation of COVID-19 infection, except for severely affected areas, such as Wuhan. In this study, the risk factors, including age, COPD, shortness of breath, fatigue, creatine kinase, D-dimer, lymphocyte and hCRP were selected to formulate a predictive nomogram to predict the severe risk of COVID-19 patients on admission. The good performance of this novel nomogram model was confirmed by the ROC curve, calibration plots and decision curve.
Previous studies have reported several clinical characteristics in severe cases and patents with adverse outcomes following COVID-19 infection. Older age, comorbidities such as hypertension, respiratory disease, diabetes, cancer, cardiovascular disease, high LDH level, and lymphocytopenia have all been associated with an increased risk of mortality (4, 9, 16–18). Obesity and smoking have been reported to correlate with increased risks in other studies(4, 17). In a study from Italy, men were at higher risk than women, which could be partly due to their higher smoking rates and subsequent comorbidities (19). In our study, we also found that old age and comorbidities such as COPD were associated with severe risk in patients with COVID-19 infection. Patients who are 65 years old scored more than 30 points, and if with COPD, another 85 points will be added. Our model also included other factors, such as symptoms like shortness of breath and fatigue, laboratory data like creatine kinase, D-dimer, lymphocyte, and hCRP. If the points added exceeded 150, the patient was noted to have the risk of progressing to the severe status, and perhaps requiring early intervention and more active treatment or even intensive care. The higher the points calculated, the higher the risk for the patient. The nomogram scoring system with 8 clinical parameters seemed to be simpler than the 12- parameter MuLBSTA score proposed in the study by Guo L et al (20).
Our study also showed that the level of D-dimer was higher in the severe group and can be used as a parameter to predict disease progression. High levels of D-dimer were correlate with 28-day mortality in patients with infection or sepsis identified in the emergency department (21). Mechanisms involved included systemic pro-inflammatory cytokine responses and local inflammation, which mediate atherosclerosis and plaque rupture, predisposing the patient to ischemia and thrombosis. This indicates that the severe patients may have high risk of embolism, thus close monitoring and early intervention are needed (22–24). Additionally, angiotensin converting enzyme 2 (ACE2), the cellular receptor for SARS-CoV-2 entry, is expressed on myocytes and vascular endothelial cells (25, 26), hence there is, at least, a theoretical basis for direct cardiac and vascular involvement in SARS-CoV-2 infection. Our study also shows that the severe group has elevated creatine kinase, which is possibly associated with myocardial injury, as reported in several studies (27). Currently, the specific mechanism remains exclusive. Therefore, in patients with SARS-CoV-2 infection, cardiac and vascular damage cannot be ignored depending on the situation, and dynamic monitoring is recommended. As an acute phase reactive protein, CRP usually correlates positively to the severity of inflammation in many diseases. CRP has been used as a factor to predict the severity of patients with SARS and SARS-COV-2 previously and recently (23). It was confirmed again in our study that CRP level is a risk factor to predict disease severity.
There are several limitations in our study. Firstly, the sample size was relatively small. It included only 239 patients in a single center outside Hubei province and may not be suitable for predicting the outcomes of patients in areas most severely affected by the pandemic, such as Wuhan, or regions that are experiencing large-scale outbreaks of COVID-19. Secondly, a prospective study is required to confirm the reliability of this novel nomogram model. Finally, adding other specific markers might further improve the sensitivity and specificity of the model.