Evaluating the Value of Anti-SARS-CoV-2 Antibody-Based Tests for COVID-19 Diagnosis.

BACKGROUND: The early detection of COVID-19 patients is fundamental for containing the pandemic. A reverse-transcriptase quantitative polymerase chain reaction (RT-PCR), which detects SARS-CoV-2 RNA, is the gold standard diagnostic test, although it can contribute to false-negative results. Consequently, supplementary diagnostic tests are urgently needed. METHODS: To assess the value of anti-SARS-CoV-2 antibody-based tests for confirming COVID-19, a retrospective study was conducted on 3120 inbound overseas travelers who underwent a 14-day government quarantine in Xiamen from August 2020 to October 2020. The diagnostic accuracy of the total antibody that detected the anti-SARS-CoV-2 antibody and the RT-PCR that detected SARS-CoV-2 RNA was determined in comparison to the clinical diagnosis. RESULTS: The COVID-19 positive rate was 3.14% (98/3120). The sensitivity and specificity of the RT-PCR test on the first day of quarantine were 14.29% and 100%, respectively, and the sensitivity and specificity of the total antibody were 93.88% and 99.40%, respectively. The kappa value between an RT-PCR on the first day of quarantine and a clinical diagnosis was 0.24 (95% CI, 0.14-0.35), indicating poor consistency. The kappa value between total antibodies and a clinical diagnosis was 0.88 (95% CI, 0.83-0.93), indicating perfect consistency. There were no differences in the positive rates of an RT-PCR in symptomatic COVID-19 (7.41% (2/27)) and asymptomatic COVID-19 (16.90 (12/71) (p = 0.338). Similarly, the positive rate of the total antibody tests showed no difference in symptomatic COVID-19 (96.30% (26/27)) and asymptomatic COVID-19 (92.96% (66/71)) (p = 0.676). CONCLUSION: SARS-CoV-2 antibodies are developed by the body in response to an infection or after vaccination; this can easily lead to a missed diagnosis. In the context of low sensitivity for an RT-PCR, SARS-CoV-2 antibody detection is an effective adjunct to RT-PCR detection, which can improve the diagnostic accuracy of COVID-19 and provide an effective complement to the false-negative results of an RT-PCR.

Impact of COVID-19 vaccination on the use of PD-1 inhibitor in treating patients with cancer: a real-world study.

Anti-COVID-19 vaccination may have functional implications for immune checkpoint inhibitor treatment in patients with cancer. This study was undertaken to determine whether the safety or efficacy of anti-PD-1 therapy is reduced in patients with cancer during COVID-19 vaccination. A large multicenter observational study was conducted in 83 Chinese hospitals between January 28, 2021 and September 30, 2021. A total of 3552 patients were screened and 2048 eligible patients with cancer receiving PD-1 inhibitor treatment were recruited. All enrolled patients had received camrelizumab treatment alone or in conjunction with other cancer therapies. Among these, 1518 (74.1%) patients received the BBIBP-CorV vaccine and were defined as the vaccinated subgroup. The remaining 530 (25.9%) patients did not receive anti-COVID-19 vaccination and were defined as the non-vaccinated subgroup. For all participants, Response Evaluation Criteria in Solid Tumor and Common Terminology Criteria for Adverse Events criteria were used to evaluate the efficacy and safety of camrelizumab treatment, respectively. Propensity score match analysis with the optimal pair matching was used to compare these criteria between the vaccinated and non-vaccinated subgroups. A total of 2048 eligible patients with cancer were included (median age 59 years, 27.6% female). Most patients (98.8%) had metastatic cancer of the lung, liver or intestinal tract. Aside from the PD-1 inhibitor treatment, 55.9% of patients received additional cancer therapies. 1518 (74.1%) patients received the BBIBP-CorV vaccine with only mild side effects reported. The remaining patients did not receive COVID-19 vaccination and had a statistically greater percentage of comorbidities. After matching for age, gender, cancer stage/types, comorbidity and performance status, 1060 patients (530 pairs) were selected for propensity score match analysis. This analysis showed no significant differences in overall response rate (25.3% vs 28.9%, p=0.213) and disease control rate (64.6% vs 67.0%, p=0.437) between vaccinated and non-vaccinated subgroups. Immune-related adverse events (irAEs) were reported in both subgroups after camrelizumab treatment. Among vaccinated patients who experienced irAEs, the median interval between the first dose of camrelizumab treatment and the first vaccine shot was ≤16 days. Compared with the non-vaccinated subgroup, irAEs in vaccinated patients were more frequently reported as mild (grade 1 or 2 irAEs; 33.8% vs 19.8%, p<0.001) and these patients were less likely to discontinue the PD-1 inhibitor treatment (4.2% vs 20.4%, p<0.001). Severe irAEs (grade 3 irAE or higher) related to camrelizumab treatment were reported, however no significant differences in the frequency of such events were observed between the vaccinated and non-vaccinated subgroups. The COVID-19 vaccine, BBIBP-CorV, did not increase severe anti-PD-1-related adverse events nor did it reduce the clinical efficacy of camrelizumab in patients with cancer. Thus, we conclude that patients with cancer need not suspend anti-PD-1 treatment during COVID-19 vaccination.

Numerical evaluation of face masks for prevention of COVID-19 airborne transmission.

The COVID-19 pandemic has forced governments around the globe to apply various preventive measures for public health. One of the most effective measures is wearing face masks, which plays a vital role in blocking the transmission of droplets and aerosols. To understand the protective mechanism of face masks, especially in indoor environments, we apply a computational fluid dynamics technique to predict the lifetime of cough droplets. Therefore, we can assess the exposure risk in a ventilated room where an infected individual wears a face mask or not. We focus on the dynamic evaporation and diffusion of droplets in a human-cough process, which is a major cause for the spread of the virus. We find that wearing a face mask can effectively reduce the total mass and Sauter mean diameter of the residual droplets after a single cough. The mass concentration of virus-carrying droplets in the ventilated room decreases by 201, 43,786, and 307,060 times, corresponding to wearing cotton face masks, surgical face masks, and N95 face masks, respectively. However, the maximum travel distance of 80% droplets is insensitive to wearing a face mask or not. Therefore, the residual droplets are widely distributed due to the influence of indoor airflow. Furthermore, we study aerosol exposure risks in different areas of the room and find that high concentrations of aerosols occur in the streamline through an infected individual, especially next to the individual within 1.5 m. This strongly suggests a social distance despite the fact that the majority of droplets are filtered by face masks. This study explains the impact of face masks and airflow on indoor exposure risks and further inspires potential measures for public health, for example, no individuals should sit near the air supply opening.

Health Issues and Immunological Assessment Related to Wuhan's COVID-19 Survivors: A Multicenter Follow-Up Study.

Background: Currently, a large number of hospitalized coronavirus infectious disease-2019 (COVID-19) patients have met the clinical discharge criteria and have been discharged. Little is known about the sequelae and herd immunity, two important factors influencing the life quality and safety of COVID-19 survivors. Methods: Discharged COVID-19 patients from four medical facilities in Wuhan, China, were followed in order to record and investigate possible post-COVID-19 sequelae and herd immunity. After hospital discharge, patients reported to Fangcang shelter hospitals for an initial 14-day period of mandatory clinical monitoring. After release from these shelter hospitals, patients returned home for self-quarantine. Real-time quantitative PCR (RT-qPCR) was used for severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) detection. Colloidal gold-based immunochromatographic strip assay (ICGSA) was used for anti-SARS-CoV-2 immunoglobulin G (IgG) and immunoglobulin M (IgM) antibody testing. The data for this study are derived from case reports, medical records, and self-reports. Results: A total of 3,677 COVID-19 survivors [median age = 59 years, interquartile range (IQR) = 47-68, range = 10-98; 55.5% female] who were released from four hospitals in Wuhan, China, between January 18 and March 29, 2020 were followed for a median of 144 days (IQR = 135-157). During follow-up, 976 (26.5%) patients had at least one post-COVID-19 sequela. The incidence of post-COVID-19 sequelae among elderly COVID-19 survivors (age ≥60 years) was slightly increased compared to that of young COVID-19 survivors (age <60 years; relative risk = 1.05, 95% CI = 1.02-1.10, p = 0.007). During follow-up, a dramatic reduction of anti-SARS-CoV-2 IgG (88.0%, 95% CI = 84.2-90.4) and IgM (93.2%, 95% CI = 88.5-96.4) antibodies was observed. Among these COVID-19 survivors, 1.2% (n = 45) retested positive for SARS-CoV-2 and 1.0% (n = 37) died during follow-up. Of those who died during follow-up, 70.3% were male and all were negative for both IgG and IgM, except for one person who was IgG-positive. Conclusions: Our study documents significant post-COVID-19 sequelae that impair functions of multiple organ systems in COVID-19 survivors, suggesting that the long-term effects of this disease will negatively impact survivors' quality of life, continue to strain health care systems, and result in extended periods of lost productivity. Furthermore, female gender and anti-SARS-CoV-2 immunity may play an essential role in the survival after COVID-19 infection.

Survival Factors and Metabolic Pathogenesis in Elderly Patients (≥65) With COVID-19: A Multi-Center Study.

Background: Elderly patients infected with COVID-19 are reported to be facing a substantially increased risk of mortality. Clinical characteristics, treatment options, and potential survival factors remain under investigation. This study aimed to fill this gap and provide clinically relevant factors associated with survival of elderly patients with COVID-19. Methods: In this multi-center study, elderly patients (age ≥65 years old) with laboratory-confirmed COVID-19 from 4 Wuhan hospitals were included. The clinical end point was hospital discharge or deceased with last date of follow-up on Jul. 08, 2020. Clinical, demographic, and laboratory data were collected. Univariate and multivariate analysis were performed to analyze survival and risk factors. A metabolic flux analysis using a large-scale molecular model was applied to investigate the pathogenesis of SARS-CoV-2 with regard to metabolism pathways. Results: A total of 223 elderly patients infected with COVID-19 were included, 91 (40.8%) were discharged and 132 (59.2%) deceased. Acute respiratory distress syndrome (ARDS) developed in 140 (62.8%) patients, 23 (25.3%) of these patients survived. Multivariate analysis showed that potential risk factors for mortality were elevated D-Dimer (odds ratio: 1.13 [95% CI 1.04 - 1.22], p = 0.005), high immune-related metabolic index (6.42 [95% CI 2.66-15.48], p < 0.001), and increased neutrophil-to-lymphocyte ratio (1.08 [95% 1.03-1.13], p < 0.001). Elderly patients receiving interferon atmotherapy showed an increased probability of survival (0.29 [95% CI 0.17-0.51], p < 0.001). Based on these factors, an algorithm (AlgSurv) was developed to predict survival for elderly patients. The metabolic flux analysis showed that 12 metabolic pathways including phenylalanine (odds ratio: 28.27 [95% CI 10.56-75.72], p < 0.001), fatty acid (15.61 [95% CI 6.66-36.6], p < 0.001), and pyruvate (12.86 [95% CI 5.85-28.28], p < 0.001) showed a consistently lower flux in the survivors vs. the deceased subgroup. This may reflect a key pathogenic mechanism of COVID-19 infection. Conclusion: Several factors such as interferon atmotherapy and recreased activity of specific metabolic pathways were found to be associated with survival of elderly patients. Based on these findings, a survival algorithm (AlgSurv) was developed to assist the clinical stratification for elderly patients. Dysregulation of the metabolic pathways revealed in this study may aid in the drug and vaccine development against COVID-19.

Development and validation of prognostic model for predicting mortality of COVID-19 patients in Wuhan, China.

Novel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell's C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.

Clinical challenges in cancer patients with COVID-19: Aging, immunosuppression, and comorbidities.

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 has developed into a global pandemic. COVID-19 poses a huge threat to health care, and the shortage of medical resources caused by COVID-19 brought serious secondary disasters to elderly cancer patients who are particularly dependent on medical resources. The clinical challenges of cancer management, including aging, immunosuppression, and comorbidities, make cancer patients more vulnerable to COVID-19 with different clinical manifestations, disease severity, and outcomes. The review comprehensively analyzed the characteristics of the cancer patients under the pandemic and concluded that cancer patients were more susceptible to COVID-19, and also concluded that they were more likely to develop poor outcomes and the severe form of the disease. Three basic management strategies have been proposed to protect susceptible elderly cancer patients, find reliable indicators to monitor the course of disease, and implement effective prevention measures.