IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

COVID-19 has grown into a global pandemic that has strained healthcare throughout the world. There is a sense of urgency in finding a cure for this deadly virus. In this study, we reviewed the empiric options used in common practice for COVID-19, based on the literature available online, with an emphasis on human experiences with these treatments on severe acute respiratory syndrome-associated coronavirus (SARS-COV-1) and other viruses. Convalescent blood products are the most promising potential treatment for use in COVID-19. The use of chloroquine or hydroxychloroquine (HCQ), remdesivir, and tocilizumab are some of the other promising potential therapies; however, they are yet to be tested in randomized clinical trials (RCTs). The use of lopinavir-ritonavir did not prove beneficial in a large RCT. The use of corticosteroids should be avoided in COVID-19 pneumonia unless used for other indications, based on the suggestion of harm in patients with SARS-COV-1 and Middle Eastern Respiratory Syndrome (MERS) infection. The reviews of this paper are available via the supplemental material section.

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BACKGROUND Studies have suggested that atrial fibrillation (AF) in patients with rheumatic diseases (RD) may be due to inflammation. AIM To determine the highest association of AF among hospitalized RD patients and to determine morbidity and mortality associated with AF in hospitalized patients with RD. METHODS The National inpatient sample database from October 2015 to December 2017 was analyzed to identify hospitalized patients with RD with and without AF. A subgroup analysis was performed comparing outcomes of AF among different RD. RESULTS The prevalence of AF was 23.9% among all patients with RD ( n = 3949203). Among the RD subgroup, the prevalence of AF was highest in polymyalgia rheumatica (33.2%), gout (30.2%), and pseudogout (27.1%). After adjusting for comorbidities, the odds of having AF were increased with gout (1.25), vasculitis (1.19), polymyalgia rheumatica (1.15), dermatopolymyositis (1.14), psoriatic arthropathy (1.12), lupus (1.09), rheumatoid arthritis (1.05) and pseudogout (1.04). In contrast, enteropathic arthropathy (0.44), scleroderma (0.96), ankylosing spondylitis (0.96), and Sjorgen’s syndrome (0.94) had a decreased association of AF. The mortality, length of stay, and hospitalization costs were higher in patients with RD having AF vs without AF. Among the RD subgroup, the highest mortality was found with scleroderma (4.8%), followed by vasculitis (4%) and dermatopolymyositis (3.5%). CONCLUSION A highest association of AF was found with gout followed by vasculitis, and polymyalgia rheumatica when compared to other RD. Mortality was two-fold higher in patients with RD with AF.

Objectives To investigate whether COVID-19 patients with pulmonary embolism had higher mortality and assess the utility of d-dimer in predicting acute pulmonary embolism. Patients and methods Using the National Collaborative COVID-19 retrospective cohort, a cohort of hospitalized COVID-19 patients was studied to compare 90-day mortality and intubation outcomes in patients with and without pulmonary embolism in a multivariable cox regression analysis. The secondary measured outcomes in 1:4 propensity score-matched analysis included length of stay, chest pain incidence, heart rate, history of pulmonary embolism or DVT, and admission laboratory parameters. Results Among 31,500 hospitalized COVID-19 patients, 1117 (3.5%) patients were diagnosed with acute pulmonary embolism. Patients with acute pulmonary embolism were noted to have higher mortality (23.6% vs.12.8%; adjusted Hazard Ratio (aHR) = 1.36, 95% CI [1.20–1.55]), and intubation rates (17.6% vs. 9.3%, aHR = 1.38[1.18–1.61]). Pulmonary embolism patients had higher admission D-dimer FEU (Odds Ratio(OR) = 1.13; 95%CI [1.1–1.15]). As the d-dimer value increased, the specificity, positive predictive value, and accuracy of the test increased; however, sensitivity decreased (AUC 0.70). At cut-off d-dimer FEU 1.8 mcg/ml, the test had clinical utility (accuracy 70%) in predicting pulmonary embolism. Patients with acute pulmonary embolism had a higher incidence of chest pain and history of pulmonary embolism or deep vein thrombosis. Conclusions Acute pulmonary embolism is associated with worse mortality and morbidity outcomes in COVID-19. We present d-dimer as a predictive risk tool in the form of a clinical calculator for the diagnosis of acute pulmonary embolism in COVID-19.

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Background: Previous observational studies suggest that inferior vena cava filter placement in pulmonary embolism patients complicated with congestive heart failure, mechanical ventilation, and shock may have a mortality benefit. We sought to analyze the survival benefits of inferior vena cava filter in pulmonary embolism patients complicated with acute myocardial infarction, acute respiratory failure, shock, or requiring treatment with thrombolytics. Methods: This retrospective observational study used hospital discharge data from the National Inpatient Sample Data (NIS). ICD-9-CM coding was used to identify complicated pulmonary embolism patients (N = 254,465) in NIS from 2002 to 2014, including the subgroups of acute myocardial infarction, acute respiratory failure, shock, and thrombolytics. Inferior vena cava filter recipients were 1:1 propensity score-matched on age, sex, race, deep vein thrombosis, Elixhauser comorbidities, and other pulmonary embolism comorbidities (45 covariates) to non-inferior vena cava filter recipients in complicated pulmonary embolism patients and separately in each subgroup. Clinical outcomes were compared between the inferior vena cava filter group and the non-inferior vena cava filter group. Results: Mortality rate in complicated pulmonary embolism patients with inferior vena cava filter placement was lower (20.9% vs. 33%; NNT = 8.28, 95% confidence interval (CI) 7.91–8.69, E-value = 2.53) and in the subgroups; acute myocardial infarction (17.9% vs. 30.1%; NNT = 8.19, 95% CI 7.52–8.92, E-value = 2.76), acute respiratory failure (19.5% vs. 29.7%; NNT = 9.76, 95% CI 8.67–11.16, E-value = 2.38), shock (30.7% vs. 47.1%; NNT = 6.08, 95% CI 5.73–6.47, E-value = 2.43), and with the use of thrombolytics (7% vs. 12.9 %; NNT 17.1, 95% CI 14.88–20.12, E-value = 3.01) ( p < 0.001 for all). Conclusion: Inferior vena cava filter placement in pulmonary embolism complicated with acute myocardial infarction, acute respiratory failure, shock, or requiring thrombolytic therapy was associated with reduced mortality.

COVID-19 infection primarily targets the lungs, which in severe cases progresses to cytokine storm, acute respiratory distress syndrome, multiorgan dysfunction, and shock. Survivors are now presenting evidence of cardiopulmonary sequelae such as persistent right ventricular dysfunction, chronic thrombosis, lung fibrosis, and pulmonary hypertension. This review will summarize the current knowledge on long-term cardiopulmonary sequelae of COVID-19 and provide a framework for approaching the diagnosis and management of these entities. We will also identify research priorities to address areas of uncertainty and improve the quality of care provided to these patients.