COVID-19 relapse associated with SARS-CoV-2 evasion from CD4+ T-cell recognition in an agammaglobulinemia patient

A 25-year-old patient with a primary immunodeficiency lacking immunoglobulin production experienced a relapse after a 239-day period of persistent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Viral genetic sequencing demonstrated that SARS-CoV-2 had evolved during the infection period, with at least five mutations associated with host cellular immune recognition. Among them, the T32I mutation in ORF3a was found to evade recognition by CD4+ T cells. The virus found after relapse showed an increased proliferative capacity in vitro. SARS-CoV-2 may have evolved to evade recognition by CD4+ T cells and increased in its proliferative capacity during the persistent infection, likely leading to relapse. These mutations may further affect viral clearance in hosts with similar types of human leukocyte antigens. The early elimination of SARS-CoV-2 in immunocompromised patients is therefore important not only to improve the condition of patients but also to prevent the emergence of mutants that threaten public health. Graphical

A Randomised trial of anti-GM-CSF Otilimab in severe COVID-19 pneumonia (OSCAR).

Abstract BACKGROUND: Granulocyte-macrophage colony-stimulating factor (GM-CSF) and dysregulated myeloid cell responses are implicated in the pathophysiology and severity of coronavirus disease 2019 (COVID-19). METHODS: In this randomised, sequential, multicentre, placebo-controlled, double-blind study, adults aged 18-79 years (Part 1) or ≥70 years (Part 2) with severe COVID-19, respiratory failure, and systemic inflammation (elevated C-reactive protein/ferritin) received a single intravenous infusion of otilimab 90 mg (human anti-GM-CSF monoclonal antibody) plus standard care (NCT04376684). The primary outcome was the proportion of patients alive and free of respiratory failure at Day 28. RESULTS: In Part 1 (N=806 randomised 1:1 otilimab:placebo), 71% of otilimab-treated patients were alive and free of respiratory failure at Day 28 versus 67% who received placebo; the model-adjusted difference of 5.3% was not statistically significant (95% CI -0.8, 11.4; p=0.09). A nominally significant model-adjusted difference of 19.1% (95% CI 5.2, 33.1; p=0.009) was observed in the predefined 70-79 years subgroup, but this was not confirmed in Part 2 (N=350 randomised) where the model-adjusted difference was 0.9% (95% CI -9.3, 11.2; p=0.86). Compared with placebo, otilimab resulted in lower serum concentrations of key inflammatory markers, including the putative pharmacodynamic biomarker CCL17, indicative of GM-CSF pathway blockade. Adverse events were comparable between groups and consistent with severe COVID-19. CONCLUSIONS: There was no significant difference in the proportion of patients alive and free of respiratory failure at Day 28. However, despite the lack of clinical benefit, a reduction in inflammatory markers was observed with otilimab, in addition to an acceptable safety profile.

Life-threatening pneumothorax after release from isolation with COVID-19 pneumonia.

Pneumothorax was previously considered as a complication of severe coronavirus disease 2019 (COVID-19) pneumonia. However, it is now known that pneumothorax can develop in other cases. Here, we describe the case of a patient who developed tension pneumothorax after release from isolation from COVID-19 pneumonia. The patient was admitted to our hospital with severe COVID-19 pneumonia on the 10th day after onset. Ventilatory management was carried out on the first day of admission; however, the patient was weaned off the next day. The treatment course was uneventful. On the morning of discharge from the hospital, the patient experienced sudden dyspnea. Chest radiography revealed a large left-tension pneumothorax with a mediastinal shift to the right. As this finding required immediate attention, a chest tube was inserted. Chest computed tomography (CT) showed an airspace in the left thoracic cavity and subpleural thin-walled cystic lesions, such as bullae in the left lobe. One month later, chest CT showed resolution of the cystic lesions. The development of pneumothorax in COVID-19 pneumonia should be considered not only in cases of severe illness, but also after release from isolation. Recently, revisions to measures against COVID-19 have been considered worldwide, including shortening of the isolation period and reviewing the identification of all cases. This is an educational report demonstrating that life-threatening pneumothorax may develop after release from isolation due to COVID-19 pneumonia.

Impact of diabetes and Krebs von den Lungen-6 on coronavirus disease 2019 severity: A single-center study from Japan.

AIMS/INTRODUCTION: Diabetes mellitus is reported as a risk factor for increased coronavirus disease 2019 (COVID-19) severity and mortality, but there have been few reports from Japan. Associations between diabetes mellitus and COVID-19 severity and mortality were investigated in a single Japanese hospital. MATERIALS AND METHODS: Patients aged ≥20 years admitted to Osaka City General Hospital for COVID-19 treatment between April 2020 and March 2021 were included in this retrospective, observational study. Multivariable logistic regression analysis was carried out to examine whether diabetes mellitus contributes to COVID-19-related death and severity. RESULTS: Of the 262 patients included, 108 (41.2%) required invasive ventilation, and 34 (13.0%) died in hospital. The diabetes group (n = 92) was significantly older, more obese, had longer hospital stays, more severe illness and higher mortality than the non-diabetes group (n = 170). On multivariable logistic regression analysis, age (odds ratio [OR] 1.054, 95% confidence interval [CI] 1.023-1.086), body mass index (OR 1.111, 95% CI 1.028-1.201), history of diabetes mellitus (OR 2.429, 95% CI 1.152-5.123), neutrophil count (OR 1.222, 95% CI 1.077-1.385), C-reactive protein (OR 1.096, 95% CI 1.030-1.166) and Krebs von den Lungen-6 (OR 1.002, 95% CI 1.000-1.003) were predictors for COVID-19 severity (R2 = 0.468). Meanwhile, age (OR 1.104, 95% CI 1.037-1.175) and Krebs von den Lungen-6 (OR 1.003, 95% CI 1.001-1.005) were predictors for COVID-19-related death (R2 = 0.475). CONCLUSIONS: Diabetes mellitus was a definite risk factor for COVID-19 severity in a single Japanese hospital treating moderately-to-severely ill patients.

Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.

Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.

Accuracy of deep learning-based computed tomography diagnostic system for COVID-19: A consecutive sampling external validation cohort study.

Ali-M3, an artificial intelligence program, analyzes chest computed tomography (CT) and detects the likelihood of coronavirus disease (COVID-19) based on scores ranging from 0 to 1. However, Ali-M3 has not been externally validated. Our aim was to evaluate the accuracy of Ali-M3 for detecting COVID-19 and discuss its clinical value. We evaluated the external validity of Ali-M3 using sequential Japanese sampling data. In this retrospective cohort study, COVID-19 infection probabilities for 617 symptomatic patients were determined using Ali-M3. In 11 Japanese tertiary care facilities, these patients underwent reverse transcription-polymerase chain reaction (RT-PCR) testing. They also underwent chest CT to confirm a diagnosis of COVID-19. Of the 617 patients, 289 (46.8%) were RT-PCR-positive. The area under the curve (AUC) of Ali-M3 for predicting a COVID-19 diagnosis was 0.797 (95% confidence interval: 0.762‒0.833) and the goodness-of-fit was P = 0.156. With a cut-off probability of a diagnosis of COVID-19 by Ali-M3 set at 0.5, the sensitivity and specificity were 80.6% and 68.3%, respectively. A cut-off of 0.2 yielded a sensitivity and specificity of 89.2% and 43.2%, respectively. Among the 223 patients who required oxygen, the AUC was 0.825. Sensitivity at a cut-off of 0.5% and 0.2% was 88.7% and 97.9%, respectively. Although the sensitivity was lower when the days from symptom onset were fewer, the sensitivity increased for both cut-off values after 5 days. We evaluated Ali-M3 using external validation with symptomatic patient data from Japanese tertiary care facilities. As Ali-M3 showed sufficient sensitivity performance, despite a lower specificity performance, Ali-M3 could be useful in excluding a diagnosis of COVID-19.

Efficacy of Ninjin'yoeito in treating severe coronavirus disease 2019 in patients in an intensive care unit.

Coronavirus Disease-2019 (COVID-19), an infectious disease associated with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global emergency with high mortality. There are few effective treatments, and many severe patients are treated in an intensive care unit (ICU). The purpose of this study was to evaluate whether the Japanese Kampo medicine ninjin'yoeito (NYT) is effective in treating ICU patients with COVID-19. Nine patients with confirmed SARS-CoV-2 infection admitted to the ICU were enrolled in this study. All patients underwent respiratory management with invasive mechanical ventilation (IMV) and enteral nutrition. Four patients received NYT (7.5 g daily) from an elemental diet tube. We retrospectively examined the prognostic nutritional index (PNI), length of IMV, length of ICU stay, length of hospital stay, rate of tracheostomy, and mortality rate. The median age of the enrolled participants was 60.0 years (4 men and 5 women). The median body mass index was 27.6. The most common comorbidity was diabetes (4 patients, 44%), followed by hypertension (3 patients, 33%) and chronic kidney disease (2 patients, 22%). The median length of IMV, ICU stay, and hospital stay were all shorter in the NYT group than in the non-NYT group (IMV; 4.0 days vs 14.3 days, ICU; 5.3 days vs 14.5 days, hospital stay; 19.9 days vs 28.2 days). In the NYT and non-NYT groups, the median PNI at admission was 29.0 and 31.2, respectively. One week after admission, the PNI was 30.7 in the NYT group and 24.4 in non-NYT group. PNI was significantly (p = 0.032) increased in the NYT group (+13.6%) than in the non-NYT group (-22.0%). The Japanese Kampo medicine NYT might be useful for treating patients with severe COVID-19 in ICU. This study was conducted in a small number of cases, and further large clinical trials are necessary.