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.

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.

Increased Healthcare Delays in Tuberculosis Patients During the First Wave of COVID-19 Pandemic in Korea: A Nationwide Cross-Sectional Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused disruptions to healthcare systems, consequently endangering tuberculosis (TB) control. We investigated delays in TB treatment among notified patients during the first wave of the COVID-19 pandemic in Korea. METHODS: We systemically collected and analyzed data from the Korea TB cohort database from January to May 2020. Groups were categorized as 'before-pandemic' and 'during-pandemic' based on TB notification period. Presentation delay was defined as the period between initial onset of symptoms and the first hospital visit, and healthcare delay as the period between the first hospital visit and anti-TB treatment initiation. A multivariate logistic regression analysis was performed to evaluate factors associated with delays in TB treatment. RESULTS: Proportion of presentation delay > 14 days was not significantly different between two groups (48.3% vs. 43.7%, P = 0.067); however, proportion of healthcare delay > 5 days was significantly higher in the during-pandemic group (48.6% vs. 42.3%, P = 0.012). In multivariate analysis, the during-pandemic group was significantly associated with healthcare delay > 5 days (adjusted odds ratio = 0.884, 95% confidence interval = 0.715-1.094). CONCLUSION: The COVID-19 pandemic was associated with healthcare delay of > 5 days in Korea. Public health interventions are necessary to minimize the pandemic's impact on the national TB control project.

Impact of COVID-19 Pandemic on the National PPM Tuberculosis Control Project in Korea: the Korean PPM Monitoring Database between July 2019 and June 2020.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused disruptions to healthcare systems and endangered the control and prevention of tuberculosis (TB). We investigated the nationwide effects of COVID-19 on the national Public-Private Mix (PPM) TB control project in Korea, using monitoring indicators from the Korean PPM monitoring database. METHODS: The Korean PPM monitoring database includes data from patients registered at PPM hospitals throughout the country. Data of six monitoring indicators for active TB cases updated between July 2019 and June 2020 were collected. The data of each cohort throughout the country and in Daegu-Gyeongbuk, Seoul Metropolitan Area, and Jeonnam-Jeonbuk were collated to provide nationwide data. The data were compared using the χ² test for trend to evaluate quarterly trends of each monitoring indicator at the national level and in the prespecified regions. RESULTS: Test coverages of sputum smear (P = 0.622) and culture (P = 0.815), drug susceptibility test (P = 0.750), and adherence rate to initial standard treatment (P = 0.901) at the national level were not significantly different during the study period. The rate of loss to follow-up among TB cases at the national level was not significantly different (P = 0.088); however, the treatment success rate among the smear-positive drug-susceptible pulmonary TB cohort at the national level significantly decreased, from 90.6% to 84.1% (P < 0.001). Treatment success rate in the Seoul metropolitan area also significantly decreased during the study period, from 89.4% to 84.5% (P = 0.006). CONCLUSION: Our study showed that initial TB management during the COVID-19 pandemic was properly administered under the PPM project in Korea. However, our study cannot confirm or conclude a decreased treatment success rate after the COVID-19 pandemic due to limited data.