Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). METHODS: We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. RESULTS: Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. CONCLUSIONS: Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

Early Empiric Antibiotic Use in Patients Hospitalized With COVID-19: A Retrospective Cohort Study.

OBJECTIVE: To investigate temporal trends and outcomes associated with early antibiotic prescribing in patients hospitalized with COVID-19. DESIGN: Retrospective propensity-matched cohort study using the National COVID Cohort Collaborative (N3C) database. SETTING: Sixty-six health systems throughout the United States that were contributing to the N3C database. Centers that had fewer than 500 admissions in their dataset were excluded. PATIENTS: Patients hospitalized with COVID-19 were included. Patients were defined to have early antibiotic use if they received at least 3 calendar days of intravenous antibiotics within the first 5 days of admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 322,867 qualifying first hospitalizations, 43,089 patients received early empiric antibiotics. Antibiotic use declined across all centers in the data collection period, from March 2020 (23%) to June 2022 (9.6%). Average rates of early empiric antibiotic use (EEAU) also varied significantly between centers (deviance explained 7.33% vs 20.0%, p < 0.001). Antibiotic use decreased slightly by day 2 of hospitalization and was significantly reduced by day 5. Mechanical ventilation before day 2 (odds ratio [OR] 3.57; 95% CI, 3.42-3.72), extracorporeal membrane oxygenation before day 2 (OR 2.14; 95% CI, 1.75-2.61), and early vasopressor use (OR 1.85; 95% CI, 1.78-1.93) but not region of residence was associated with EEAU. After propensity matching, EEAU was associated with an increased risk for in-hospital mortality (OR 1.27; 95% CI, 1.23-1.33), prolonged mechanical ventilation (OR 1.65; 95% CI, 1.50-1.82), late broad-spectrum antibiotic exposure (OR 3.24; 95% CI, 2.99-3.52), and late Clostridium difficile infection (OR 1.60; 95% CI, 1.37-1.87). CONCLUSIONS: Although treatment of COVID-19 patients with empiric antibiotics has declined during the pandemic, the frequency of use remains high. There is significant inter-center variation in antibiotic prescribing practices and evidence of potential harm. Our findings are hypothesis-generating and future work should prospectively compare outcomes and adverse events.

Coding long COVID: characterizing a new disease through an ICD-10 lens.

BACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

Dupilumab use is associated with protection from COVID-19 mortality: A retrospective analysis.

We previously found that type 2 immunity promotes COVID-19 pathogenesis in a mouse model. To test relevance to human disease we used electronic health record databases and determined that patients on dupilumab (anti-IL-4R monoclonal antibody that blocks IL-13 and IL-4 signaling) at the time of COVID-19 infection had lower mortality.

Increased stroke severity and mortality in patients with SARS-CoV-2 infection: An analysis from the N3C database.

BACKGROUND: Studies from early in the COVID-19 pandemic showed that patients with ischemic stroke and concurrent SARS-CoV-2 infection had increased stroke severity. We aimed to test the hypothesis that this association persisted throughout the first year of the pandemic and that a similar increase in stroke severity was present in patients with hemorrhagic stroke. METHODS: Using the National Institute of Health National COVID Cohort Collaborative (N3C) database, we identified a cohort of patients with stroke hospitalized in the United States between March 1, 2020 and February 28, 2021. We propensity score matched patients with concurrent stroke and SARS-COV-2 infection and available NIH Stroke Scale (NIHSS) scores to all other patients with stroke in a 1:3 ratio. Nearest neighbor matching with a caliper of 0.25 was used for most factors and exact matching was used for race/ethnicity and site. We modeled stroke severity as measured by admission NIHSS and the outcomes of death and length of stay. We also explored the temporal relationship between time of SARS-COV-2 diagnosis and incidence of stroke. RESULTS: Our query identified 43,295 patients hospitalized with ischemic stroke (5765 with SARS-COV-2, 37,530 without) and 18,107 patients hospitalized with hemorrhagic stroke (2114 with SARS-COV-2, 15,993 without). Analysis of our propensity matched cohort revealed that stroke patients with concurrent SARS-COV-2 had increased NIHSS (Ischemic stroke: IRR=1.43, 95% CI:1.33-1.52, p<0.001; hemorrhagic stroke: IRR=1.20, 95% CI:1.08-1.33, p<0.001), length of stay (Ischemic stroke: estimate = 1.48, 95% CI: 1.37, 1.61, p<0.001; hemorrhagic stroke: estimate = 1.25, 95% CI: 1.06, 1.47, p=0.007) and higher odds of death (Ischemic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001; hemorrhagic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001). We observed the highest incidence of stroke diagnosis on the same day as SARS-COV-2 diagnosis with a logarithmic decline in counts. CONCLUSION: This retrospective observational analysis suggests that stroke severity in patients with concurrent SARS-COV-2 was increased throughout the first year of the pandemic.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

The impact of COVID-19 on clinical outcomes among acute myocardial infarction patients undergoing early invasive treatment strategy.

BACKGROUND: The implications of coronavirus disease 2019 (COVID-19) infection on outcomes after invasive therapeutic strategies among patients presenting with acute myocardial infarction (AMI) are not well studied. HYPOTHESIS: To assess the outcomes of COVID-19 patients presenting with AMI undergoing an early invasive treatment strategy. METHODS: This study was a cross-sectional, retrospective analysis of the National COVID Cohort Collaborative database including all patients presenting with a recorded diagnosis of AMI (ST-elevation myocardial infarction (MI) and non-ST elevation MI). COVID-19 positive patients with AMI were stratified into one of four groups: (1a) patients who had a coronary angiogram with percutaneous coronary intervention (PCI) within 3 days of their AMI; (1b) PCI within 3 days of AMI with coronary artery bypass graft (CABG) within 30 days; (2a) coronary angiogram without PCI and without CABG within 30 days; and (2b) coronary angiogram with CABG within 30 days. The main outcomes were respiratory failure, cardiogenic shock, prolonged length of stay, rehospitalization, and death. RESULTS: There were 10 506 COVID-19 positive patients with a diagnosis of AMI. COVID-19 positive patients with PCI had 8.2 times higher odds of respiratory failure than COVID-19 negative patients (p = .001). The odds of prolonged length of stay were 1.7 times higher in COVID-19 patients who underwent PCI (p = .024) and 1.9 times higher in patients who underwent coronary angiogram followed by CABG (p = .001). CONCLUSION: These data demonstrate that COVID-19 positive patients with AMI undergoing early invasive coronary angiography had worse outcomes than COVID-19 negative patients.

Stroke and COVID Population: A Health Equity Analysis

OBJECTIVES/GOALS: Observational studies suggest unequal effects of COVID-19 on the population of the U.S. distinguished by race and ethnicity. Our primary research question: what are the demographic differences among patients identified with concurrent ischemic stroke and COVID-19 positivity? METHODS/STUDY POPULATION: The National Covid Cohort Collaboration (N3C) data was used to identify patients with concurrent COVID-19 and stroke, operationally defined as those with a COVID diagnosis and inpatient admission for ischemic stroke 1 week before or 6 weeks after their COVID diagnosis. The data was further age restricted (18-65 years) and a categorical variable was created representing payer plans (Medicaid, Medicare, Other insurance). Data on patients race/ethnicity, comorbidities, treatments administered (Remdesivir and ECMO) and insurance information was analyzed using various exploratory data methods and visualizations. Logistic regression was implemented to model the relationship between variables (dependent/independent) in the cohorts. Model complexity was analyzed using the F test of significance. RESULTS/ANTICIPATED RESULTS: Taken as a whole, the data contained over 7 billion rows and around 6.4 million persons (~ 2.15 million of whom were COVID+). The main cohort of individuals with concurrent COVID positivity and ischemic stroke made up around 0.29% of the original COVID+ group, and the payer plan sub-cohort consists of around 29.26% of our main cohort. Black/African American (AA) and the Hispanic/Latino any Race have younger distributions (median ~ 65 years), while the White Non-Hispanic group has the oldest distribution (median ~ 70 years). Black/AA had the highest average number of comorbidities per patient (4.49), compared to white non-Hispanic (3.39) and Asian non-Hispanic (2.59). In our analysis, Medicaid patients had lower odds of obtaining ECMO (p < .01), there was no significant difference in Remdesivir treatment. DISCUSSION/SIGNIFICANCE: We found the N3C data to be useful in studying a distinct group of patients, and exploring COVID-19 and ischemic stroke treatment across patients’ race/ethnicity identities and insurance status. Our exploratory analysis provides a foundation for further insight into demographic trends and discrepancies in apportionment of treatment.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

IL-13 is a driver of COVID-19 severity.

Immune dysregulation is characteristic of the more severe stages of SARS-CoV-2 infection. Understanding the mechanisms by which the immune system contributes to COVID-19 severity may open new avenues to treatment. Here, we report that elevated IL-13 was associated with the need for mechanical ventilation in 2 independent patient cohorts. In addition, patients who acquired COVID-19 while prescribed Dupilumab, a mAb that blocks IL-13 and IL-4 signaling, had less severe disease. In SARS-CoV-2-infected mice, IL-13 neutralization reduced death and disease severity without affecting viral load, demonstrating an immunopathogenic role for this cytokine. Following anti-IL-13 treatment in infected mice, hyaluronan synthase 1 (Has1) was the most downregulated gene, and accumulation of the hyaluronan (HA) polysaccharide was decreased in the lung. In patients with COVID-19, HA was increased in the lungs and plasma. Blockade of the HA receptor, CD44, reduced mortality in infected mice, supporting the importance of HA as a pathogenic mediator. Finally, HA was directly induced in the lungs of mice by administration of IL-13, indicating a new role for IL-13 in lung disease. Understanding the role of IL-13 and HA has important implications for therapy of COVID-19 and, potentially, other pulmonary diseases. IL-13 levels were elevated in patients with severe COVID-19. In a mouse model of the disease, IL-13 neutralization reduced the disease and decreased lung HA deposition. Administration of IL-13-induced HA in the lung. Blockade of the HA receptor CD44 prevented mortality, highlighting a potentially novel mechanism for IL-13-mediated HA synthesis in pulmonary pathology.