Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients. The Cerner Real-World Database was used for this study. Data on adult patients (18 years or older) with cardiovascular and related circulatory diseases between 2017 and 2019 were retrieved and a total of 13 these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a single more powerful super learning model for predicting COVID-19 severity on admission to the hospital. LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159). Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Background/Objective: Millions of children were infected with SARS-CoV-2, and a small proportion progressed to severe disease, especially those with underlying risk factors. Adult COVID-19 studies showed mortality benefits with Remdesivir. Data on Remdesivir use in pediatrics are limited. We report on the safety and tolerability of Remdesivir in pediatric patients seen at our institution. Methods: This was a retrospective cohort study of patients <19 years old with acute SARS-CoV-2 infection who received at least one dose of Remdesivir. Patients followed strict institutional guidelines for safety monitoring including standard clinical and laboratory daily observations. Demographics and underlying conditions were reported as averages; for laboratory values, linear regression was applied within a generalized linear mixed-effects model framework to evaluate the significance of changes in average levels over time. Results: We enrolled 318 patients with acute SARS-CoV2 infection from May 2020 to December 2022. In total, 53% were male, and the age range was distributed broadly. In total, 61% were school-aged children (28% 5–11 and 33% 12–18 years of age). In total, 62% of cases were Hispanic. The most common reasons for Remdesivir treatment included respiratory distress (201; 63%) and having high-risk underlying conditions (109; 34%). Therapy was completed as planned in 91% and discontinued early in 9%. Mean baseline, peak, and end of treatment values for AST were 57 (95% CI 53, 61), 79 (95% CI 73, 84) (p < 0.001), and 55 (51, 59) (p = 0.479); for ALT, they were 42 (38, 47), 59 (95% CI 52, 66) (p < 0.001), and 46 (95% CI 41, 52) (p = 0.054); and for bilirubin, they were 0.56 (95% CI 0.50, 0.62), 0.67 (95% CI 0.61, 0.74) (p < 0.001), and 0.44 (95% CI 0.40, 0.48) (p < 0.001), respectively. During Remdesivir treatment, we did not observe marrow suppression or renal toxicity. Conclusions: No clinically significant hematological or renal toxicity was noted. Mean liver enzymes increased modestly and returned to baseline without interrupting treatment. Remdesivir was well tolerated in patients <19 years old.