At present, the most difficulty challenge in treating patients and saving lives is the extreme shortage of medical resources, especially critical care resources. Therefore, the differentiation of patients with severe and nonsevere COVID-19 is key to providing treatment at different levels as needed . The rational allocation of medical resources is an important means of improving diagnosis and treatment efficiency and reducing patient mortality. The use of routine blood testing, an economical and simple-to-operate tool with a short turnaround time, to determine disease severity can largely accelerate the pace and reduce the cost of COVID-19 diagnosis and treatment. The proposed approach uses a common laboratory parameters to assist clinicians in preliminarily classifying COVID-19 patients and properly allocating medical resources. This will ensure that patients with early-stage severe COVID-19 can be treated in a timely manner.
In the present study, the investigation of the epidemiology and clinical symptoms of COVID-19 patients revealed that, consistent with previous reports, patients with severe disease were older than those with nonsevere disease. This may be related to the weakening of the body's defense system caused by the declination of immune function or the presence of underlying diseases (e.g., hypertension, chronic renal failure, and diabetes mellitus) in elderly patients . Therefore, clinicians should closely monitor the disease progression of middle-aged and elderly patients to avoid missing the optimal treatment time. In addition, 72.3% of the 159 COVID-19 patients developed clinical symptoms, including fever, and patients with severe COVID-19 were more likely to develop fever than those with nonsevere COVID-19.
In this study, two parameters, Lym% and HGB, were integrated to form a joint parameter Lym%&HGB by binary logistic regression. Both Lym% and HGB were statistically significant when used as independent variables in the model (p < 0.001, Table 2), indicating that both parameters had a significant impact on the possibility of developing severe COVID-19. The joint parameter Lym%&HGB obtained by integrating the above two parameters had an accuracy of 84.4% in identifying the data of different groups, suggesting great potential of this parameter in the differential diagnosis of severe and nonsevere COVID-19.
The grouping analysis of the results from 1503 routine blood tests found that Lym% and HGB continued to decline and the joint parameter Lym%&HGB continued to rise as the disease progressed in COVID-19 patients. The data from 1503 routine blood tests were divided into the severe sample group and the nonsevere sample group and were subject to Mann-Whitney U nonparametric tests. Compared with that in the nonsevere sample group, Lym% and HGB were significantly lower while the joint parameter Lym%&HGB was significantly higher in the severe sample group (p < 0.001). This indicated that the number of lymphocytes and the concentration of hemoglobin gradually decreased with disease progression. Therefore, Lym%, HGB, and the joint parameter Lym%&HGB are all potential tools for distinguishing severe from nonsevere COVID-19.
Subsequently, ROC analysis was conducted to assess the diagnostic performance of the three parameters in identifying patients with severe or nonsevere COVID-19. The results showed that both Lym% and HGB were good predictors, as evidenced by the AUCs of 0.89 and 0.79, respectively. When using 18.8% as the cutoff point for Lym% and 116 g/L for HGB, the sensitivity rates were 85.6% and 71.1%, and the specificity rates were 77.5% and 77.2%, respectively; moreover, the AUC for Lym%&HGB was 0.92. When using 0.481 as the cutoff point for Lym%&HGB, the sensitivity was 88.9%, and the specificity was 79.8%, suggesting that Lym%&HGB has advantages in distinguishing patient with severe COVID-19 from those with nonsevere COVID-19.
To more intuitively present the effectiveness of the joint parameter Lym%&HGB in distinguishing patients with severe COVID-19 from those with nonsevere COVID-19, a two-dimensional scatter plot of the results from 1503 tests was generated with Lym% as the horizontal axis and HGB as the vertical axis. As Fig. 2 shows, the data points for patients with severe disease are scattered mostly below the cutoff for Lym%&HGB, while those for patients with nonsevere disease are mostly above the line, indicating that the joint parameter, as a predictor, is superior to the single parameters Lym% and HBG for distinguishing patients with severe disease from those with nonsevere disease.
The dynamic profile demonstrated that Lym% was significantly lower in the severe patient group than that in the nonsevere patient group and that the median Lym% in the severe patient group began to fall below the cutoff point of 18.8% on the 4th day after disease onset, suggesting a high possibility of developing severe disease. Similarly, HGB declined progressively beginning at the end of the second week after disease onset, and the median fell below the cutoff point of 116 g/L in the third week, showing a decrease by 24% from 133 g/L to 100 g/L. This indicated that the disease was likely to progress to a severe stage. However, the joint parameter Lym%&HGB showed an opposite change trend to that of Lym% and HGB. Compared with patients with nonsevere COVID-19 whose Lym%&HGB slightly fluctuated and increased, patients with severe COVID-19 had a higher Lym%&HGB level, above the cutoff point of 0.481, throughout the entire disease course. This observation can help clinicians more easily identify patients with severe disease.
Lymphocytes play a decisive role in maintaining systemic immune balance and regulating the inflammatory response in the body. Currently, there are four possible explanations for the decrease in the number of lymphocytes caused by novel coronavirus infection. (1) The virus directly attacks and kills lymphocytes. In the early stage of infection, B lymphocytes produce antibodies that directly bind to and kill the virus, and T lymphocytes engulf the virus-infected cells, thereby clearing the virus. Therefore, the reduction in lymphocytes in COVID-19 patients might be attributed to the massive consumption of lymphocytes . (2) The virus may directly destroy lymphatic organs. The attack of lymphatic organs, including the thymus and spleen, by the novel coronavirus affects lymphocyte production, resulting in a drastic decline in the number of lymphocytes. This view is supported by the autopsy report published by Hanley . In previous reports, SARS and MERS patients showed similar changes, i.e., their lymphatic organs were attacked or even destroyed by the virus with disease progression . (3) Inflammatory factors induce lymphocyte apoptosis. Basic research confirmed that tumor necrosis factor (TNF)-α, interleukin 6 (IL-6), and other pro-inflammatory cytokines can induce lymphocyte apoptosis, leading to an acute decrease in the number of lymphocytes . (4) The metabolic molecules produced in metabolic diseases, such as hyperlactic acidemia, inhibit lymphocytes. In patients with severe COVID-19, a continuous increase in blood lactic acid levels might inhibit the proliferation of lymphocytes . The abovementioned mechanisms may jointly cause lymphopenia; however, this claim requires further research. The significant change in HGB may be explained by the fact that the virus adheres to the surface of hematopoietic cells through the angiotensin converting enzyme (ACE) 2 receptor  and enters the hematopoietic system. The substances released by the virus, viremia, and endotoxins jointly influence the release of immune factors and immune regulatory function, affect hematopoietic stem/progenitor cells, and lead to an abnormal hematopoietic microenvironment, thereby inhibiting the hematopoietic function of bone marrow. This ultimately affects the compensatory production of HGB, causing a continuous decrease in HGB and even hematopoietic failure or aplastic anemia . Among the 99 COVID-19 patients admitted to Wuhan Jinyintan Hospital, China, 51% experienced decrease in HGB , consistent with the finding in the present study. In this study, two parameters, Lym% and HGB, were linearly integrated into a joint parameter, Lym%&HGB, using binary logistic regression.