Association of blood glucose level and prognosis of inpatients with coexistent diabetes and COVID-19.

Type 2 diabetes (T2D) increases the risk of coronavirus disease (COVID-19). This study investigates the association between glucose control of COVID-19 patients with T2D in first 7 days after hospital admission and prognosis. A total of 252 infected inpatients with T2D in China were included. Well-controlled blood glucose was defined as stable fasting blood glucose (FBG) levels in the range of 3.9-7.8 mmol/L during first 7 days using indicators of average (FBGA), maximum (FBGM) or first-time (FBG1) FBG levels. The primary endpoint was admission to intensive care unit or death. Hazard ratio (HR) of poorly controlled glucose level group compared with well-controlled group were 4.96 (P = 0.021) for FBGM and 5.55 (P = 0.014) for FBGA. Well-controlled blood glucose levels in first 7 days could improve the prognosis of COVID-19 inpatients with diabetes.

Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients.

Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

Sex-based clinical and immunological differences in COVID-19.

BACKGROUND: Males and females differ in their immunological responses to foreign pathogens. However, most of the current COVID-19 clinical practices and trials do not take the sex factor into consideration. METHODS: We performed a sex-based comparative analysis for the clinical outcomes, peripheral immune cells, and severe acute respiratory syndrome coronavirus (SARS-CoV-2) specific antibody levels of 1558 males and 1499 females COVID-19 patients from a single center. The lymphocyte subgroups were measured by Flow cytometry. The total antibody, Spike protein (S)-, receptor binding domain (RBD)-, and nucleoprotein (N)- specific IgM and IgG levels were measured by chemiluminescence. RESULTS: We found that male patients had approximately two-fold rates of ICU admission (4.7% vs. 2.7% in males and females, respectively, P = 0.005) and mortality (3% vs. 1.4%, in males and females, respectively, P = 0.004) than female patients. Survival analysis revealed that the male sex is an independent risk factor for death from COVID-19 (adjusted hazard ratio [HR] = 2.22, 95% confidence interval [CI]: 1.3-3.6, P = 0.003). The level of inflammatory cytokines in peripheral blood was higher in males during hospitalization. The renal (102/1588 [6.5%] vs. 63/1499 [4.2%], in males and females, respectively, P = 0.002) and hepatic abnormality (650/1588 [40.9%] vs. 475/1499 [31.7%], P = 0.003) were more common in male patients than in female patients. By analyzing dynamic changes of lymphocyte subsets after symptom onset, we found that the percentage of CD19+ B cells and CD4+ T cells was generally higher in female patients during the disease course of COVID-19. Notably, the protective RBD-specific IgG against SARS-CoV-2 sharply increased and reached a peak in the fourth week after symptom onset in female patients, while gradually increased and reached a peak in the seventh week after symptom onset in male patients. CONCLUSIONS: Males had an unfavorable prognosis, higher inflammation, a lower percentage of lymphocytes, and indolent antibody responses during SARS-CoV-2 infection and recovery. Early medical intervention and close monitoring are important, especially for male COVID-19 patients.

Comparative Characterization and Risk Stratification of Asymptomatic and Presymptomatic Patients With COVID-19.

The identification of asymptomatic, non-severe presymptomatic, and severe presymptomatic coronavirus disease 2019 (COVID-19) in patients may help optimize risk-stratified clinical management and improve prognosis. This single-center case series from Wuhan Huoshenshan Hospital, China, included 2,980 patients with COVID-19 who were hospitalized between February 4, 2020 and April 10, 2020. Patients were diagnosed as asymptomatic (n = 39), presymptomatic (n = 34), and symptomatic (n = 2,907) upon admission. This study provided an overview of asymptomatic, presymptomatic, and symptomatic COVID-19 patients, including detection, demographics, clinical characteristics, and outcomes. Upon admission, there was no significant difference in clinical symptoms and CT image between asymptomatic and presymptomatic patients for diagnosis reference. The mean area under the receiver operating characteristic curve (AUC) of the differential diagnosis model to discriminate presymptomatic patients from asymptomatic patients was 0.89 (95% CI, 0.81-0.98). Importantly, the severe and non-severe presymptomatic patients can be further stratified (AUC = 0.82). In conclusion, the two-step risk-stratification model based on 10 laboratory indicators can distinguish among asymptomatic, severe presymptomatic, and non-severe presymptomatic COVID-19 patients on admission. Moreover, single-cell data analyses revealed that the CD8+T cell exhaustion correlated to the progression of COVID-19.

High Expression of ACE2 and TMPRSS2 at the Resection Margin Makes Lung Cancer Survivors Susceptible to SARS-CoV-2 With Unfavorable Prognosis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide. Systematic analysis of lung cancer survivors at molecular and clinical levels is warranted to understand the disease course and clinical characteristics. METHODS: A single-center, retrospective cohort study was conducted in 65 patients with COVID-19 from Wuhan Huoshenshan Hospital, of which 13 patients were diagnosed with lung cancer. The study was conducted from February 4 to April 11, 2020. RESULTS: During the course of treatment, lung cancer survivors infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) had shorter median time from symptom onset to hospitalization (P = 0.016) and longer clinical symptom remission time (P = 0.020) than non-cancer individuals. No differences were observed among indicators such as time from symptom onset to hospitalization and symptom remission time between medium-term and short-term survivors. The expression of ACE2 (P = 0.013) and TMPRSS2 (P <0.001) was elevated in lung cancer survivors as compared with that in non-cancer individuals. CONCLUSIONS: ACE2 and TMPRSS2 levels were higher at resection margins of lung cancer survivors than those in normal tissues of non-cancerous individuals and may serve as factors responsible for the high susceptibility to COVID-19 among lung cancer survivors. Lung cancer patients diagnosed with COVID-19, including medium-term survivors, have worse outcomes than the general population.

Implications of cardiac markers in risk-stratification and management for COVID-19 patients.

BACKGROUND: COVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients. METHODS: A total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression. RESULTS: Among 2954 COVID-19 patients in the analysis, the median age was 60 years (50-68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4-4.8]). CONCLUSIONS: COVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.

Implications of liver injury in risk-stratification and management of patients with COVID-19.

BACKGROUND: Infection with SARS-CoV-2 has been associated with liver dysfunction, aggravation of liver burden, and liver injury. This study aimed to assess the effects of liver injuries on the clinical outcomes of patients with COVID-19. METHODS: A total of 1520 patients with severe or critical COVID-19 from Huoshenshan Hospital, Wuhan, were enrolled. Chronic liver disease (CLD) was confirmed by consensus diagnostic criteria. Laboratory test results were compared between different groups. scRNA-seq data and bulk gene expression profiles were used to identify cell types associated with liver injury. RESULTS: A total of 10.98% of patients with severe or critical COVID-19 developed liver injury after admission that was associated with significantly higher rates of mortality (21.74%, p < 0.001) and intensive care unit admission (26.71%, p < 0.001). Pre-existing CLDs were not associated with a higher risk. However, fatty liver disease and cirrhosis were associated with higher risks, supported by evidences from single cell and bulk transcriptome analysis that showed more TMPRSS2+ cells in these tissues. By generating a model, we were able to predict the risk and severity of liver injury during hospitalization. CONCLUSION: We demonstrate that liver injury occurring during therapy as well as pre-existing CLDs like fatty liver disease and cirrhosis in patients with COVID-19 is significantly associated with the severity of disease and mortality, but the presence of other CLD is not associated. We provide a risk-score model that can predict whether patients with COVID-19 will develop liver injury or proceed to higher-risk stages during subsequent hospitalizations.

Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19.

Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.