Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.

Background: We aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19). Methods: This was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation. Results: A total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively. Conclusions: Our prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.

Impact of Changes in Forest Use Caused by the COVID-19 Pandemic on the Perception of Forest Ecosystem Services in the Republic of Korea

Ecosystem services (ESs) play an important role in improving human well-being. This study identified the changes in people's perceived importance of forest ecosystem services (FESs) due to changes in forest use caused by the coronavirus disease-19 (COVID-19) pandemic. We measured the changes in people's perceived importance of FESs during the pandemic compared to before its outbreak. We analyzed how the decrease in frequency of visits to urban greenspaces and forests and the purchasing of wood products and non-timber forest products (NTFPs) during the pandemic affected changes in the perceived importance of FESs using a multiple linear regression model. Data were collected from 1000 participants through an online survey conducted in the Republic of Korea. Results showed that respondents commonly perceived that all types of FES, particularly regulating and cultural services, were more important during the COVID-19 outbreak than before its onset. Results suggest that people who had decreased their frequency of visits to urban greenspaces and forests had a perception of higher importance for regulating and cultural services than those who maintained it. This study proposes that it is necessary to change urban greenspace and forest management policies reflecting the public's changed importance of FESs.

Differential neutralization and inhibition of SARS-CoV-2 variants by antibodies elicited by COVID-19 mRNA vaccines.

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the emergence of new variant lineages that have exacerbated the COVID-19 pandemic. Some of those variants were designated as variants of concern/interest (VOC/VOI) by national or international authorities based on many factors including their potential impact on vaccine-mediated protection from disease. To ascertain and rank the risk of VOCs and VOIs, we analyze the ability of 14 variants (614G, Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Theta, Iota, Kappa, Lambda, Mu, and Omicron) to escape from mRNA vaccine-induced antibodies. The variants show differential reductions in neutralization and replication by post-vaccination sera. Although the Omicron variant (BA.1, BA.1.1, and BA.2) shows the most escape from neutralization, sera collected after a third dose of vaccine (booster sera) retain moderate neutralizing activity against that variant. Therefore, vaccination remains an effective strategy during the COVID-19 pandemic.

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

Background: Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters. Methods: Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between -600 and -250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2). Results: A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters. Conclusions: The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.

Impact of prior vaccination on clinical outcomes of patients with COVID-19.

Fully vaccinated people remain at risk of Coronavirus Disease 2019 (COVID-19). We examined association between prior vaccination and clinical outcomes in patients with COVID-19. Overall, 387 patients with mild-to-severe COVID-19 were enrolled. Patients were considered fully vaccinated at least 14, 7, and 14 days after receiving the second dose of ChAdOx1 nCoV-19 or mRNA-1273, second dose of BNT162b2, or single dose of Ad26.COV2.S, respectively. The primary outcomes (risk of pneumonia, requirement of supplemental oxygen, and progression to respiratory failure) were compared between vaccinated and unvaccinated patients. Logistic regression analysis was performed to identify factors associated with the outcomes. There were 204 and 183 patients in the vaccinated and unvaccinated groups, respectively. The vaccinated group was significantly older and had more comorbidities than the unvaccinated group. Patients in the unvaccinated group were significantly more likely to develop pneumonia (65.6% vs. 36.8%) or require supplemental oxygen (29.0 vs. 15.7%) than the vaccinated group. The vaccinated group had a significantly shorter time from symptom onset to hospital discharge than the unvaccinated group (10 vs. 11 days; p<0.001). The proportion of patients who progressed to respiratory failure did not differ significantly between groups. In multivariable analyses, vaccination was associated with an approximately 70% and 82% lower likelihood of pneumonia and supplemental oxygen requirement, respectively. Being vaccinated was associated with a significantly lower risk of pneumonia and severe disease when breakthrough infection developed. Our findings support continuous efforts to increase vaccine coverage in populations.

Comparative Effectiveness and Antibody Responses to Moderna and Pfizer-BioNTech COVID-19 Vaccines among Hospitalized Veterans - Five Veterans Affairs Medical Centers, United States, February 1-September 30, 2021.

The mRNA COVID-19 vaccines (Moderna and Pfizer-BioNTech) provide strong protection against severe COVID-19, including hospitalization, for at least several months after receipt of the second dose (1,2). However, studies examining immune responses and differences in protection against COVID-19-associated hospitalization in real-world settings, including by vaccine product, are limited. To understand how vaccine effectiveness (VE) might change with time, CDC and collaborators assessed the comparative effectiveness of Moderna and Pfizer-BioNTech vaccines in preventing COVID-19-associated hospitalization at two periods (14-119 days and ≥120 days) after receipt of the second vaccine dose among 1,896 U.S. veterans at five Veterans Affairs medical centers (VAMCs) during February 1-September 30, 2021. Among 234 U.S. veterans fully vaccinated with an mRNA COVID-19 vaccine and without evidence of current or prior SARS-CoV-2 infection, serum antibody levels (anti-spike immunoglobulin G [IgG] and anti-receptor binding domain [RBD] IgG) to SARS-CoV-2 were also compared. Adjusted VE 14-119 days following second Moderna vaccine dose was 89.6% (95% CI = 80.1%-94.5%) and after the second Pfizer-BioNTech dose was 86.0% (95% CI = 77.6%-91.3%); at ≥120 days VE was 86.1% (95% CI = 77.7%-91.3%) for Moderna and 75.1% (95% CI = 64.6%-82.4%) for Pfizer-BioNTech. Antibody levels were significantly higher among Moderna recipients than Pfizer-BioNTech recipients across all age groups and periods since vaccination; however, antibody levels among recipients of both products declined between 14-119 days and ≥120 days. These findings from a cohort of older, hospitalized veterans with high prevalences of underlying conditions suggest the importance of booster doses to help maintain long-term protection against severe COVID-19.†.

Serologic Testing of US Blood Donations to Identify Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-Reactive Antibodies: December 2019-January 2020.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), was first identified in Wuhan, China, in December 2019, with subsequent worldwide spread. The first US cases were identified in January 2020. METHODS: To determine if SARS-CoV-2-reactive antibodies were present in sera prior to the first identified case in the United States on 19 January 2020, residual archived samples from 7389 routine blood donations collected by the American Red Cross from 13 December 2019 to 17 January 2020 from donors resident in 9 states (California, Connecticut, Iowa, Massachusetts, Michigan, Oregon, Rhode Island, Washington, and Wisconsin) were tested at the Centers for Disease Control and Prevention for anti-SARS-CoV-2 antibodies. Specimens reactive by pan-immunoglobulin (pan-Ig) enzyme-linked immunosorbent assay (ELISA) against the full spike protein were tested by IgG and IgM ELISAs, microneutralization test, Ortho total Ig S1 ELISA, and receptor-binding domain/ACE2 blocking activity assay. RESULTS: Of the 7389 samples, 106 were reactive by pan-Ig. Of these 106 specimens, 90 were available for further testing. Eighty-four of 90 had neutralizing activity, 1 had S1 binding activity, and 1 had receptor-binding domain/ACE2 blocking activity >50%, suggesting the presence of anti-SARS-CoV-2-reactive antibodies. Donations with reactivity occurred in all 9 states. CONCLUSIONS: These findings suggest that SARS-CoV-2 may have been introduced into the United States prior to 19 January 2020.

Association of mental disorders with SARS-CoV-2 infection and severe health outcomes: nationwide cohort study.

BACKGROUND: Epidemiological data on the association between mental disorders and the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) severity are limited. AIMS: To evaluate the association between mental disorders and the risk of SARS-CoV-2 infection and severe outcomes following COVID-19. METHOD: We performed a cohort study using the Korean COVID-19 patient database based on national health insurance data. Each person with a mental or behavioural disorder (diagnosed during the 6 months prior to their first SARS-CoV-2 test) was matched by age, gender and Charlson Comorbidity Index with up to four people without mental disorders. SARS-CoV-2-positivity risk and the risk of death or severe events (intensive care unit admission, use of mechanical ventilation and acute respiratory distress syndrome) post-infection were calculated using conditional logistic regression analysis. RESULTS: Among 230 565 people tested for SARS-CoV-2, 33 653 (14.6%) had mental disorders; 928/33 653 (2.76%) tested SARS-CoV-2 positive and 56/928 (6.03%) died. In multivariable analysis using the matched cohort, there was no association between mental disorders and SARS-CoV-2-positivity risk (odds ratio OR = 0.95; 95% CI 0.87-1.04); however, a higher risk was associated with schizophrenia-related disorders (OR = 1.50; 95% CI 1.14-1.99). Among confirmed COVID-19 patients, the mortality risk was significantly higher in patients with than in those without mental disorders (OR = 1.99, 95% CI 1.15-3.43). CONCLUSIONS: Mental disorders are likely contributing factors to mortality following COVID-19. Although the infection risk was not higher for people with mental disorders overall, those with schizophrenia-related disorders were more vulnerable to infection.