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.

COVID-19 Vaccine-Associated Pneumonitis in the Republic of Korea: A Nationwide Multicenter Survey.

BACKGROUND: Recent reports have suggested that pneumonitis is a rare complication following vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, its clinical features and outcomes are not well known. The aim of this study was to identify the clinical characteristics and outcomes of patients with vaccine-associated pneumonitis following vaccination against SARS-CoV-2. METHODS: In this nationwide multicenter survey study, questionnaires were distributed to pulmonary physicians in referral hospitals. They were asked to report cases of development or exacerbation of interstitial lung disease (ILD) associated with the coronavirus disease 2019 vaccine. Vaccine-associated pneumonitis was defined as new pulmonary infiltrates documented on chest computed tomography within 4 weeks of vaccination and exclusion of other possible etiologies. RESULTS: From the survey, 49 cases of vaccine-associated pneumonitis were identified between February 27 and October 30, 2021. After multidisciplinary discussion, 46 cases were analyzed. The median age was 66 years and 28 (61%) were male. The median interval between vaccination and respiratory symptoms was 5 days. There were 20 (43%), 17 (37%), and nine (19%) patients with newly identified pneumonitis, exacerbation of pre-diagnosed ILD, and undetermined pre-existing ILD, respectively. The administered vaccines were BNT162b2 and ChAdOx1 nCov-19/AZD1222 each in 21 patients followed by mRNA-1273 in three, and Ad26.COV2.S in one patient. Except for five patients with mild disease, 41 (89%) patients were treated with corticosteroid. Significant improvement was observed in 26 (57%) patients including four patients who did not receive treatment. However, ILD aggravated in 9 (20%) patients despite treatment. Mortality was observed in eight (17%) patients. CONCLUSION: These results suggest pneumonitis as a potentially significant safety concern for vaccines against SARS-CoV-2. Clinical awareness and patient education are necessary for early recognition and prompt management. Additional research is warranted to identify the epidemiology and characterize the pathophysiology of vaccine-associated pneumonitis.

Use of the Monoclonal Antibody Regdanvimab to Treat Patients Hospitalized with COVID-19: Real-World Data during the Delta Variant Predominance.

Regdanvimab is the only monoclonal antibody available in Korea that targets severe acute respiratory syndrome coronavirus 2. We retrospectively evaluated the clinical characteristics of 374 adults hospitalized with coronavirus disease 2019 (COVID-19) who were treated with regdanvimab from September through December 2021. In total, 322 (86.1%) patients exhibited risk factors for disease progression. Most patients (91.4%) improved without additional treatment. No patient died or was transferred to intensive care. This study shows that regdanvimab prevented disease progression in high-risk patients with mild to moderate COVID-19 infections during Delta variant predominance.

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.