Over 1-year duration and age difference of SARS-CoV-2 antibodies in convalescent COVID-19 patients.

Anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglouilin G (IgG) and immunoglouilin M (IgM) antibodies have been widely used to assist clinical diagnosis. Our previous study reported a discrepancy in SARS-CoV-2 antibody response between male and female coronavirus disease 2019 (COVID-19) patients. However, the duration and discrepancy between ages as well as sexes of SARS-CoV-2 antibody in convalescent COVID-19 patients have not been clarified. In this study, a total of 538 health-examination individuals who were confirmed with SARS-CoV-2 infection a year ago were enrolled. Blood samples were collected and detected for IgM and IgG antibodies. Among these convalescent patients, 12.80% were detected positive for IgM antibodies. The positive rates for IgM antibody were close between sexes: for males, this is 9.17% and for females 13.75%. However, the IgG antibody was detected positive in as much as 82.90% convalescent patients and the positive rates were nearly the same between males (82.57%) and females (82.98%). Besides this, the level of IgM and IgG antibodies showed no difference between male and female convalescent patients. The level of IgG antibodies showed a significant difference between ages. The elder patients (over 35 years old) maintained a higher level of IgG antibody than the younger patients (under or equal 35 years old) after recovering for 1 year. In addition, IgG antibody was more vulnerable to disappear in younger patients than in elder patients. Overall, our study identified over 1-year duration of SARS-CoV-2 antibody and age difference of IgG antibody response in convalescent COVID-19 patients. These findings may provide new insights into long-term humoral immune response, vaccines efficacy and age-based personalized vaccination strategies.

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = -0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

Changes of quantitative CT-based airway wall dimensions in patients with COVID-19 during early recovery.

BACKGROUND: As the coronavirus disease 19 (COVID-19) pandemic evolves, the need for recognizing the structural pulmonary changes of the disease during early convalescence has emerged. Most studies focus on parenchymal destruction of the disease; but little is known about whether the disease affects the airway. This study was conducted to investigate the changes in airway dimensions and explore the associated factors during early convalescence in patients with COVID-19. METHODS: We retrospectively analyzed quantitative computed tomography (CT)-based airway measures of 69 patients with COVID-19 from 5 February to 17 March 2020, and 32 non-COVID-19 participants from 1 January 2018 to 31 December 2019 from Guangzhou, China. The well-established measures of wall area fraction and the square root of the wall area of a hypothetical bronchus with an inner perimeter of 10 mm, were used to describe airway wall dimensions. We described the characteristics of the dimensions and inner area of airways in 66 patients with COVID-19 at the initial and convalescent stages of the disease, and compared them with the non-COVID-19 group. Linear regression models were constructed to investigate the association of airway dimensions with duration of hospitalization or disease severity after recovery. Partial correlation coefficients were calculated to investigate whether inflammatory markers were related to airway dimensions. RESULTS: Among 66 patients with COVID-19, airway dimensions were greater during disease initiation than early convalescence, which was significantly greater than in non-COVID-19 participants. No significant difference was found between the patients with COVID-19 at the initial stage and the non-COVID-19 controls regarding the first to eighth generations of the inner area. In adjusted regression models, duration of hospitalization was negatively associated with wall area fraction of the first to the sixth generation of airways. No significant associations exist between airway dimensions and disease severity, or airway dimensions with inflammatory markers. CONCLUSIONS: Airway dimensions in patients with COVID-19 during disease initiation are greater than those in non-COVID-19 participants. Such structural airway changes continue to remain significantly greater during early convalescence. No evidence shows that disease severity or inflammatory markers are associated with airway dimensions, implying that the primary lesion attacked by COVID-19 might not be the airways.

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

BACKGROUND: To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness. METHODS: A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC). RESULTS: The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000). CONCLUSIONS: A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

A comparison study of SARS-CoV-2 IgG antibody between male and female COVID-19 patients: A possible reason underlying different outcome between sex.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China at the end of 2019 has spread throughout the world and caused many thousands of deaths. The previous study reported a higher severe status rate and mortality rate in male patients in China. However, the reason underlying this difference has not been reported. The convalescent plasma containing a high level of SARS-CoV-2 immunoglobulin G (IgG) antibody has been used in clinical therapy and achieved good effects in China. In this study, to compare the differences of the SARS-CoV-2 IgG antibody between male and female patients, a total number of 331 patients confirmed SARS-CoV-2 infection were enrolled. The serum of these patients was collected during hospitalization and detected for the SARS-CoV-2 IgG antibody. Our data showed that the concentration of IgG antibody in mild, general, and recovering patients showed no difference between male and female patients. In severe status, compared with male patients, there were more female patients having a relatively high concentration of serum SARS-CoV-2 IgG antibody. In addition, the generation of IgG antibody in female patients was stronger than male patients in disease early phase. Our study identified a discrepancy in the SARS-CoV-2 IgG antibody level in male and female patients, which may be a potential cause leading to a different outcome of Coronavirus Disease 2019 between sex.