Different Circulation Pattern of Multiple Respiratory Viruses in Southern China During the COVID-19 Pandemic.

China implemented stringent non-pharmaceutical interventions (NPIs) in spring 2020, which has effectively suppressed SARS-CoV-2. In this study, we utilized data from routine respiratory virus testing requests from physicians and examined circulation of 11 other respiratory viruses in Southern China, from January 1, 2018 to December 31, 2020. A total of 58,169 throat swabs from patients with acute respiratory tract infections (ARTIs) were collected and tested. We found that while the overall activity of respiratory viruses was lower during the period with stringent NPIs, virus activity rebounded shortly after the NPIs were relaxed and social activities resumed. Only influenza was effectively suppressed with very low circulation which extended to the end of 2020. Circulation of other respiratory viruses in the community was maintained even during the period of stringent interventions, especially for rhinovirus. Our study shows that NPIs against COVID-19 have different impacts on respiratory viruses.

Elevated SARS-Cov-2-Specific IgM Levels Indicate Clinically Unfavorable Outcomes in Patients with COVID-19: A Retrospective Cohort Study

Background COVID-19 outbreak began in Wuhan and pandemics occur. Although SARS-CoV-2-specific immunoglobulins have been detected in serum of COVID-19 patients, their dynamics and association with outcomes have not been fully characterized. Methods This retrospective cohort study investigated the association between SARS-CoV-2-specific immunoglobulins and clinical outcomes of COVID-19 patients. We recruited 137 participants who were diagnosed with COVID-19 in four wards of the Tongji Hospital in Wuhan, China. Among the 137 participants, 81 patients were recovered, 23 patients died, and 33 patients remained hospitalized by the end of the study. SARS-CoV-2-specific immunoglobulins were analyzed by chemiluminescence assays. Laboratory and radiological characteristics, and clinical outcomes were compared between the recovered group and the deceased group. Furthermore, a matched cohort study was conducted in which each non-survivor was matched to two recovered patients of similar age. Results SARS-CoV-2-specific IgM levels peaked in the fourth week after the onset of COVID-19, while serum IgG levels rose earlier and remained high up to the eighth week. In the age-matched cohort study, the serum IgM, but not IgG levels, were higher among the non-survivors than in the recovered group (P = 0.006). The area under the ROC curve for the IgM and IgG levels was 0.702 (95% CI: 0.560–0.845, P = 0.006) and 0.596 (95% confidence interval: 0.449–0.744, P = 0.194), respectively. We also showed that patients with COVID-19 who had high IgM or IgG levels (stratified according to best cut-off) exhibited significantly lower overall survival (Kaplan–Meier survival curves, P < 0.05). Discussion These results indicate the association between immunoglobulins and outcome in patients with COVID-19 and demonstrated that elevated serum IgM levels could indicate poor outcomes in patients with COVID-19. Further, the information about the profile of SARS-CoV-2-specific IgGs may be useful for the future epidemiological investigations of COVID-19 therapies.

Clinical characteristics and risk factors of fatal patients with COVID-19: a retrospective cohort study in Wuhan, China.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has caused a global pandemic, resulting in considerable mortality. The risk factors, clinical treatments, especially comprehensive risk models for COVID-19 death are urgently warranted. METHODS: In this retrospective study, 281 non-survivors and 712 survivors with propensity score matching by age, sex, and comorbidities were enrolled from January 13, 2020 to March 31, 2020. RESULTS: Higher SOFA, qSOFA, APACHE II and SIRS scores, hypoxia, elevated inflammatory cytokines, multi-organ dysfunction, decreased immune cell subsets, and complications were significantly associated with the higher COVID-19 death risk. In addition to traditional predictors for death risk, including APACHE II (AUC = 0.83), SIRS (AUC = 0.75), SOFA (AUC = 0.70) and qSOFA scores (AUC = 0.61), another four prediction models that included immune cells subsets (AUC = 0.90), multiple organ damage biomarkers (AUC = 0.89), complications (AUC = 0.88) and inflammatory-related indexes (AUC = 0.75) were established. Additionally, the predictive accuracy of combining these risk factors (AUC = 0.950) was also significantly higher than that of each risk group alone, which was significant for early clinical management for COVID-19. CONCLUSIONS: The potential risk factors could help to predict the clinical prognosis of COVID-19 patients at an early stage. The combined model might be more suitable for the death risk evaluation of COVID-19.

Clinical characteristics and risk factors of COVID-19 patients with chronic hepatitis B: a multi-center retrospective cohort study.

The Coronavirus disease 2019 (COVID-19) has spread globally. Although mixed liver impairment has been reported in COVID-19 patients, the association of liver injury caused by specific subtype especially chronic hepatitis B (CHB) with COVID-19 has not been elucidated. In this multi-center, retrospective, and observational cohort study, 109 CHB and 327 non-CHB patients with COVID-19 were propensity score matched at an approximate ratio of 3:1 on the basis of age, sex, and comorbidities. Demographic characteristics, laboratory examinations, disease severity, and clinical outcomes were compared. Furthermore, univariable and multivariable logistic and Cox regression models were used to explore the risk factors for disease severity and mortality, respectively. A higher proportion of CHB patients (30 of 109 (27.52%)) developed into severe status than non-CHB patients (17 of 327 (5.20%)). In addition to previously reported liver impairment markers, such as alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and total bilirubin, we identified several novel risk factors including elevated lactate dehydrogenase (⩾ 245 U/L, hazard ratio (HR) = 8.639, 95% confidence interval (CI) = 2.528-29.523; P < 0.001) and coagulation-related biomarker D-dimer (⩾ 0.5 µg/mL, HR = 4.321, 95% CI = 1.443-12.939; P = 0.009) and decreased albumin (< 35 g/L, HR = 0.131, 95% CI = 0.048-0.361; P < 0.001) and albumin/globulin ratio (< 1.5, HR = 0.123, 95% CI = 0.017-0.918; P = 0.041). In conclusion, COVID-19 patients with CHB were more likely to develop into severe illness and die. The risk factors that we identified may be helpful for early clinical surveillance of critical progression.

Characteristics of patients with kidney injury associated with COVID-19.

To explore the characteristics of COVID-19 infection related kidney injury, we retrospectively collected cases of COVID-19 patients with definite clinical outcomes (discharge or death) and relevant laboratory results from Jan 3 to Mar 30, 2020 in Tongji hospital, Wuhan, China. 1509 patients were included, 1393 cases with normal baseline serum creatinine, and 116 cases with elevated baseline serum creatinine (EBSC). On admission, the prevalence of elevated serum creatinine, elevated blood urea nitrogen (BUN) and estimated glomerular filtration (eGFR) under 60 ml/min/1.73 m2 were 7.7%, 6.6% and 7.2%, respectively. The incidence of in-hospital death in the patients with EBSC was 7.8%, which was significantly higher than those with normal serum creatinine (1.2%). Inflammatory, immunological, and organ damage indices were relatively higher in the EBSC group, in which lymphocytes, albumin, and hemoglobin were significantly lower. Kaplan-Meier analysis revealed age above 65 years, males, comorbidities (especially for cardiovascular disease and tumor patients), lymphocyte count < 1.5 × 109/L, leukocyte count > 10 × 109/L, EBSC, eGFR < 60 ml/min/1.73 m2 were associated with in-hospital death. Multivariate Cox proportional hazard regression confirmed that EBSC (HR: 2.643, 95% CI: 1.111-6.285, P = 0.028), eGFR < 60 ml/min/1.73 m2 (HR: 3.889, 95% CI: 1.634-9.257, P = 0.002), were independent risk factors after adjusting for age, sex, any comorbidity, leukocyte and lymphocyte count. Therefore, the prevalence of kidney injury in patients with COVID-19 was high and associated with in-hospital mortality. Early detection and effective intervention of kidney injury may reduce COVID-19 deaths.

Risk Factors for Mortality of COVID-19 Patient Based on Clinical Course: A Single Center Retrospective Case-Control Study.

Background: Epidemiological factors, clinical characteristics, and risk factors for the mortality of COVID-19 patients have been studied, but the role of complementary systems, possible inflammatory and immune response mechanisms, and detailed clinical courses are uncertain and require further study. Methods: In this single center, retrospective case-control study, we included all COVID-19 inpatients transferred or admitted to Wuhan Tongji Hospital from January 3 to March 30 2020 who had definite clinical outcomes (cured or deceased) with complete laboratory and radiological results. Clinical data were extracted from the electronic medical records, and compared between the cured and deceased patients. ROC curves were used to evaluate the prognostic value of the clinical parameters, and multivariable logistic regression analysis was performed to explore the risk factors for mortality. The correlation between the variables was evaluated by Spearman correlation analysis. Results: 208 patients were included in this study, 182 patients were cured and discharged, 26 patients died from COVID-2019. Most patients had comorbidities, with hypertension as the most common chronic disease (80; 38%). The most common symptoms at onset were fever (149; 72%), cough (137; 66%), and dyspnea (113; 54%). Elevated leucocytes, neutrophils, inflammatory biomarkers (CRP, ferritin, IL6, IL8, procalcitonin), PT, D-dimer, myocardial enzymes, BUN, decreased lymphocyte and subsets (T cells, CD4 T cells, CD8 T cells, NK cells, T cells + B cells + NK cells), and immunological factors (C3, C4) indicated poor outcome. PT, C3, and T cells were confirmed as independent prognostic factors for mortality by logistic regression models. IL6 and CPR were positively correlated with neutrophils, but negatively with lymphocytes and lymphocyte subsets except B cells. IL8 and ferritin were negatively related to T cells and CD4 T cells. Positive associations existed between C3 and T cells, CD4 T cells, and CD8 T cells, whereas there was no significant correlation between C4 and lymphocyte subsets. PT was found positively correlated with IL6, IL8, and CRP. Reverse correlations were explored between C3, C4, and PT, CK-MB, total bilirubin. Conclusions: T cells, C3, and PT were identified as independent prognostic factors for mortality. Decreased C3 and C4, dysregulation of lymphocyte subsets and cytokines may lead to death after SARS-CoV-2 infection.

Persistence of SARS-CoV-2-specific antibodies in COVID-19 patients.

To better understand humoral immunity following SARS-CoV-2 infection, 114 hospitalised COVID-19 patients with antibody monitored over 8 weeks from symptom onset were retrospectively investigated. A total of 445 serum samples were assessed via chemiluminescence immunoassay. Positive rate of virus-specific IgM reached up to over 80% from the second week to the eighth week after symptom onset, then declined quickly to below 30% in the twelfth week. Concentrations of IgG remained high for at least 3 months before subsequently declining. As compared with the non-severe group, serum IgM level from week 3 to week 8 was significantly higher among the patients with severe clinical symptoms (P = 0.012) but not IgG (P = 0.053). Serum IgM level from week 3 to week 8 was correlated with positive virus RNA test (r = 0.201, P = 0.044), albumin level (r = -0.295, P = 0.003), lactic dehydrogenase (LDH) level (r = 0.292, P = 0.003), alkaline phosphatase (ALP) level (r = 0.254, P = 0.010), C-reactive protein (CRP) level (r = 0.281, P = 0.004) during the same course, while serum IgG level was correlated with age (r = 0.207, P = 0.038). This presented results provide insight into duration of SARS-CoV-2 antibodies and interaction between the virus and host systems.

Clinical characteristics of confirmed and clinically diagnosed patients with 2019 novel coronavirus pneumonia: a single-center, retrospective, case-control study.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2. At the peak of the outbreak in Wuhan (January and February), there are two types of COVID-19 patients: laboratory confirmation and clinical diagnosis. This study aims to compare and analyze the clinical outcomes and characteristics of confirmed and clinically diagnosed COVID-19 patients to determine whether they are of the same type and require equal treatment. More importantly, the prognostic factors of COVID-19 patients are explored. METHODS: A total of 194 hospitalized patients with COVID-19 pneumonia were retrospectively studied. Demographic data, clinical characteristcs, laboratory results and prognostic information were collected by electronic medical record system and analyzed. RESULTS: Among 194 subjects included, 173 were confirmed and 21 were clinically diagnosed. There were no significant differences in clinical outcomes (mortality rate 39[22.54%] vs 7[33.33%], P = 0.272) and hospital stay (19.00 vs 16.90 days, P = 0.411) between the confirmed and clinically diagnosed group, and prognostic factors were similar between them. Older age, lower albumin levels, higher serum Lactate dehydrogenase (LDH) levels, higher D-D levels, longer prothrombin time (PT), higher IL-6 levels, lower T cells indicated poor prognosis in patients with COVID-19 pneumonia. NK cell has the highest AUC among all measured indicators (NK AUC = 0.926, P < 0.001). CONCLUSION: Laboratory-confirmed and clinically diagnosed COVID-19 patients are similar in clinical outcomes and most clinical characteristics. They are of the same type and require equal treatment. Age, AST, LDH, BUN, PT, D-D, IL6, white blood cell and neutrophil counts, T cell and T cell subset counts can efficiently predict clinical outcomes.

ANTECEDENTES: El nuevo coronavirus 2019 (COVID-19) es una nueva enfermedad infecciosa causada por el virus SARS-CoV-2. Durante el pico del brote en Wuhan (enero y febrero 2020), se detectaron dos tipos de pacientes portadores del COVID-19: pacientes confirmados a través de pruebas de laboratorio y pacientes confirmados por diagnóstico clínico. El objetivo de este estudio es comparar y analizar los resultados clínicos y las características de los pacientes con COVID-19 confirmados y clínicamente diagnosticados para determinar si son del mismo tipo y si necesitan el mismo tratamiento. El estudio es importante también para explorar los factores pronósticos de los pacientes con COVID-19. MÉTODOS: Un total de 194 pacientes hospitalizados con neumonía COVID-19 fueron estudiados retrospectivamente. Se utilizó un sistema de registro médico electrónico para recopilar los datos demográficos, las características clínicas, los resultados de laboratorio y la información pronóstica, para luego ser analizada. RESULTADOS: De los 194 pacientes incluidos, 173 dieron positivo y 21 fueron diagnosticados clínicamente. No se presentaron diferencias significativas en los resultados clínicos (tasa de mortalidad 39 [22,54%] vs. 7 [33,33%], p = 0,272) y la estancia hospitalaria (19,00 vs. 16,90 días, p = 0,411) entre el grupo de confirmados y el grupo diagnosticado clínicamente, y los factores pronósticos fueron similares entre ellos. Edad avanzada, niveles más bajos de albúmina, niveles más altos de lactato deshidrogenasa (LDH) en suero, niveles más altos de D-D, mayor tiempo de protrombina (PT), altos niveles de IL-6, células T más bajas indicaban mal pronóstico en pacientes con neumonía por COVID-19. La célula NK tiene el AUC más alto entre todos los indicadores medidos (NK AUC = 0,926, p < 0,001). CONCLUSIÓN: Los grupos de pacientes COVID-19 confirmados en laboratorio y diagnosticados clínicamente arrojan resultados clínicos similares y tienen la mayoría de las características clínicas. Son del mismo tipo y requieren el mismo tratamiento. La edad, AST, LDH, BUN, PT, D-D, IL6, los recuentos de glóbulos blancos y neutrófilos, recuentos de subgrupos de células T y células T pueden predecir los resultados clínicos de forma eficaz.

Leucocyte Subsets Effectively Predict the Clinical Outcome of Patients With COVID-19 Pneumonia: A Retrospective Case-Control Study.

Background: The clinical characteristics of coronavirus disease 2019 (COVID-19) have been well-studied, while effective predictors for clinical outcome and research on underlying mechanisms are scarce. Methods: Hospitalized COVID-19 pneumonia patients with definitive clinical outcome (cured or died) were retrospectively studied. The diagnostic performance of the leucocyte subsets and other parameters were compared using the area under the receiver operating characteristic curve (AUC). Further, the correlations between leucocyte subsets and inflammation-related factors associated with clinical outcome were subsequently investigated. Results: Among 95 subjects included, 56 patients were cured, and 39 died. Older age, elevated aspartate aminotransferase, total bilirubin, serum lactate dehydrogenase, blood urea nitrogen, prothrombin time, D-dimer, Procalcitonin, and C-reactive protein levels, decreased albumin, elevated serum cytokines (IL2R, IL6, IL8, IL10, and TNF-α) levels, and a decreased lymphocyte count indicated poor outcome in patients with COVID-19 pneumonia. Lymphocyte subset (lymphocytes, T cells, helper T cells, suppressor T cells, natural killer cells, T cells+B cells+NK cells) counts were positively associated with clinical outcome (AUC: 0.777; AUC: 0.925; AUC: 0.900; AUC: 0.902; AUC: 0.877; AUC: 0.918, resp.). The neutrophil-to-lymphocyte ratio (NLR), neutrophil to T lymphocyte count ratio (NTR), neutrophil percentage to T lymphocyte ratio (NpTR) effectively predicted mortality (AUC: 0.900; AUC: 0.905; AUC: 0.932, resp.). Binary logistic regression showed that NpTR was an independent prognostic factor for mortality. Serum IL6 levels were positively correlated with leucocyte count, neutrophil count, and eosinophil count and negatively correlated with lymphocyte count. Conclusion: These results indicate that leucocyte subsets predict the clinical outcome of patients with COVID-19 pneumonia with high efficiency. Non-self-limiting inflammatory response is involved in the development of fatal pneumonia.

Effectiveness of Arbidol for COVID-19 Prevention in Health Professionals.

Background: Frontline health professionals are a COVID-19-susceptible population during the outbreak of COVID-19, but prophylactic drugs against SARS-CoV-2 infection are to be explored. Method: Frontline health professionals diagnosed with COVID-19 before February 9, 2020 in Tongji Hospital, Wuhan, China and the same amount of controls in the uninfected group were included in this study. Clinical and laboratory data were collected with standardized forms. Results: A total of 164 subjects were included in this study, 82 cases in the infected group and 82 controls in the uninfected group, with a median age of 37 years, including 63 males and 101 females. Nineteen (23.2%) patients in the infected group were administered oral arbidol, and 48 (58.5%) in the uninfected group (OR = 0.214, 95% CI 0.109-0.420). The cumulative uninfected rate of health professionals in the arbidol group was significantly higher than that of individuals in the non-arbidol group (log-rank test, χ2 = 98.74; P < 0.001). Forty-eight patients (58.5%) in the infection group were hospitalized, with a median age of 39 (31-49) years, of whom 7 (14.6%) were prophylactically administered arbidol. Thirty-four patients (41.5%) with mild symptoms were treated outside the hospital, among which the median age was 34 (30-39) years, and twelve patients (35.3%) took prophylactic oral arbidol. The hospitalization rate was significantly associated with age (P = 0.024) and oral arbidol administration (OR = 0.313, 95% CI 0.108-0.909). In the age-matched case-control study, the hospitalization rate was not significantly associated with arbidol administration (P = 0.091). Conclusion: Prophylactic oral arbidol was associated with a lower incidence of SARS-CoV-2 infection but not hospitalization rate in health professionals, providing a basis for the selection of prophylactic drugs for high-risk populations.

Clinical characteristics and risk factors associated with COVID-19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study.

BACKGROUND: COVID-19 has spread globally. Epidemiological susceptibility to COVID-19 has been reported in patients with cancer. We aimed to systematically characterise clinical features and determine risk factors of COVID-19 disease severity for patients with cancer and COVID-19. METHODS: In this multicentre, retrospective, cohort study, we included all adult patients (aged ≥18 years) with any type of malignant solid tumours and haematological malignancy who were admitted to nine hospitals in Wuhan, China, with laboratory-confirmed COVID-19 between Jan 13 and March 18, 2020. Enrolled patients were statistically matched (2:1) with patients admitted with COVID-19 who did not have cancer with propensity score on the basis of age, sex, and comorbidities. Demographic characteristics, laboratory examinations, illness severity, and clinical interventions were compared between patients with COVID-19 with or without cancer as well as between patients with cancer with non-severe or severe COVID-19. COVID-19 disease severity was defined on admission on the basis of the WHO guidelines. Univariable and multivariable logistic regression, adjusted for age, sex, comorbidities, cancer type, tumour stage, and antitumour treatments, were used to explore risk factors associated with COVID-19 disease severity. This study was registered in the Chinese Clinical Trial Register, ChiCTR2000030807. FINDINGS: Between Jan 13 and March 18, 2020, 13 077 patients with COVID-19 were admitted to the nine hospitals in Wuhan and 232 patients with cancer and 519 statistically matched patients without cancer were enrolled. Median follow-up was 29 days (IQR 22-38) in patients with cancer and 27 days (20-35) in patients without cancer. Patients with cancer were more likely to have severe COVID-19 than patients without cancer (148 [64%] of 232 vs 166 [32%] of 519; odds ratio [OR] 3·61 [95% CI 2·59-5·04]; p<0·0001). Risk factors previously reported in patients without cancer, such as older age; elevated interleukin 6, procalcitonin, and D-dimer; and reduced lymphocytes were validated in patients with cancer. We also identified advanced tumour stage (OR 2·60, 95% CI 1·05-6·43; p=0·039), elevated tumour necrosis factor α (1·22, 1·01-1·47; p=0·037), elevated N-terminal pro-B-type natriuretic peptide (1·65, 1·03-2·78; p=0·032), reduced CD4+ T cells (0·84, 0·71-0·98; p=0·031), and reduced albumin-globulin ratio (0·12, 0·02-0·77; p=0·024) as risk factors of COVID-19 severity in patients with cancer. INTERPRETATION: Patients with cancer and COVID-19 were more likely to deteriorate into severe illness than those without cancer. The risk factors identified here could be helpful for early clinical surveillance of disease progression in patients with cancer who present with COVID-19. FUNDING: China National Natural Science Foundation.

2019 Novel coronavirus disease (COVID-19) in hemodialysis patients: A report of two cases.

OBJECTIVE: To analyze the diagnosis and treatment of patients with chronic renal failure complicated with novel coronavirus pneumonia, and to evaluate the effect of blood purification technology on the treatment and prognosis of such patients. METHODS: Two COVID-19 cases undergoing hemodialysis with chronic renal failure were retrospectively analysed in our hospital. RESULTS: Two COVID-19 patients were admitted to hospital due to cough, with or without fever. Laboratory tests showed decreased lymphocyte count, elevated PCT, IL-10, IL-6, TNF-α, IL-2R, high-sensitivity cardiac troponin I, NT-proBNP, creatinine, and urea nitrogen. Chest CT scan showed multiple blurred plaques and patchy shadows in both patients. Two patients received continuous venovenous hemodiafiltration (CVVHDF) every other day for 4-6 h everytime, in addition to the standard treatment. After CVVHDF, not only cytokines were reduced, but also liver function and cardiac function significantly improved. Both patients did not develop severe pneumonia. They were discharged on March 1, 2020 when meeting the discharge criteria. CONCLUSION: Two COVID-19 patients on maintenance hemodialysis discharged after a month of hospitalization. The removal of cytokines through blood purification technology may be beneficial for the recovery of COVID-19 patients.