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Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Objective: The effects of Coronavirus disease 2019 (COVID-19) infection and altered processes of care on nonelective coronary artery bypass grafting (CABG) outcomes remain unknown. We hypothesized that patients with COVID-19 infection would have longer hospital lengths of stay and greater mortality compared with COVID-negative patients, but that these outcomes would not differ between COVID-negative and pre-COVID controls. Methods: The National COVID Cohort Collaborative 2020-2022 was queried for adult patients undergoing CABG. Patients were divided into COVID-negative, COVID-active, and COVID-convalescent groups. Pre-COVID control patients were drawn from the National Surgical Quality Improvement Program database. Adjusted analysis of the 3 COVID groups was performed via generalized linear models. Results: A total of 17,293 patients underwent nonelective CABG, including 16,252 COVID-negative, 127 COVID-active, 367 COVID-convalescent, and 2254 pre-COVID patients. Compared to pre-COVID patients, COVID-negative patients had no difference in mortality, whereas COVID-active patients experienced increased mortality. Mortality and pneumonia were higher in COVID-active patients compared to COVID-negative and COVID-convalescent patients. Adjusted analysis demonstrated that COVID-active patients had higher in-hospital mortality, 30- and 90-day mortality, and pneumonia compared to COVID-negative patients. COVID-convalescent patients had a shorter length of stay but a higher rate of renal impairment. Conclusions: Traditional care processes were altered during the COVID-19 pandemic. Our data show that nonelective CABG in patients with active COVID-19 is associated with significantly increased rates of mortality and pneumonia. The equivalent mortality in COVID-negative and pre-COVID patients suggests that pandemic-associated changes in processes of care did not impact CABG outcomes. Additional research into optimal timing of CABG after COVID infection is warranted.

Objectives To evaluate the effectiveness of COVID-19 vaccinations (initial and booster) during pre-Delta, Delta and Omicron dominant periods among pregnant people via (1) COVID-19 incident and severe infections among pregnant people who were vaccinated versus unvaccinated and (2) post-COVID-19 vaccination breakthrough infections and severe infections among vaccinated females who were pregnant versus non-pregnant.Design Retrospective cohort study using nationally sampled electronic health records data from the National COVID Cohort Collaborative, 10 December 2020 –7 June 2022.Participants Cohort 1 included pregnant people (15–55 years) and cohort 2 included vaccinated females of reproductive age (15–55 years).Exposures (1) COVID-19 vaccination and (2) pregnancy.Main outcome measures Adjusted HRs (aHRs) for COVID-19 incident or breakthrough infections and severe infections (ie, COVID-19 infections with related hospitalisations).Results In cohort 1, 301 107 pregnant people were included. Compared with unvaccinated pregnant people, the aHRs for pregnant people with initial vaccinations during pregnancy of incident COVID-19 were 0.77 (95% CI 0.62 to 0.96) and 0.88 (95% CI 0.73 to 1.07) and aHRs of severe COVID-19 infections were 0.65 (95% CI 0.47 to 0.90) and 0.79 (95% CI 0.51 to 1.21) during the Delta and Omicron periods, respectively. Compared with pregnant people with full initial vaccinations, the aHR of incident COVID-19 for pregnant people with booster vaccinations was 0.64 (95% CI 0.58 to 0.71) during the Omicron period. In cohort 2, 934 337 vaccinated people were included. Compared with vaccinated non-pregnant females, the aHRs of severe COVID-19 infections for people with initial vaccinations during pregnancy was 2.71 (95% CI 1.31 to 5.60) during the Omicron periods.Conclusions Pregnant people with initial and booster vaccinations during pregnancy had a lower risk of incident and severe COVID-19 infections compared with unvaccinated pregnant people across the pandemic stages. However, vaccinated pregnant people still had a higher risk of severe infections compared with non-pregnant females.

The relationship between post-recovery COVID-19 and incident heart failure has not been investigated at scale. Here, the authors use electronic health records for ~600,000 patients in the US and find a higher rate of post-discharge incident heart failure in those hospitalised with COVID-19 than without.

Acute kidney injury (AKI) has been noticed after both COVID-19 vaccination and infection, affecting risk-benefit evaluations and vaccine hesitancy. We conducted a large-scale N3C cohort study to compare AKI incidence following COVID-19 vaccination and infection. Participants from December 2020 to August 2023 were divided into two groups based on their initially observed COVID-19 antigen exposure: COVID-19 vaccination group (n = 2,953,219) and COVID-19 infection group (n = 3,616,802). AKI was defined by diagnostic codes and serum creatinine changes within a 30 day follow-up window after exposure. The absolute risk of AKI was 0.66% in the vaccination group versus 4.88% in the infection group. After adjusting for various confounders, COVID-19 infection was associated with a significantly higher risk of AKI than COVID-19 vaccination (aHR = 10.31, P < 0.001). Our study reveals that COVID-19 vaccination is associated with a significant lower AKI risk compared to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease. [ABSTRACT FROM AUTHOR]

Background: Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus particles in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication. Methods: In an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before acute COVID-19 and not ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using nonparametric targeted maximum likelihood estimation to aggressively adjust for high-dimensional covariates. Results: We analyzed a sample (n = 302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, 0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription 1 to 4 months before acute COVID-19 with no prior history of SSRI use) to nonusers (adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)). Conclusions: These findings suggest that SSRI use during acute COVID-19 may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The coronavirus 2019 (COVID-19) pandemic has evolved over time by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, disease severity, treatment, and prevention. There is evidence of an elevated risk of incident diabetes after COVID-19; our objective was to evaluate whether this association is consistent across time and with contemporary viral variants. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using National COVID Cohort Collaborative (N3C) data to evaluate incident diabetes risk among COVID-positive adults compared with COVID-negative patients or control patients with acute respiratory illness (ARI). Cohorts were weighted on demographics, data site, and Charlson comorbidity index score. The primary outcome was the cumulative incidence ratio (CIR) of incident diabetes for each viral variant era. RESULTS: Risk of incident diabetes 1 year after COVID-19 was increased for patients with any viral variant compared with COVID-negative control patients (ancestral CIR 1.16 [95% CI 1.12–1.21]; Alpha CIR 1.14 [95% CI 1.11–1.17]; Delta CIR 1.17 [95% CI 1.13–1.21]; Omicron CIR 1.13 [95% CI 1.10–1.17]) and control patients with ARI (ancestral CIR 1.17 [95% CI 1.11–1.22]; Alpha CIR 1.14 [95% CI 1.09–1.19]; Delta CIR 1.18 [95% CI 1.11–1.26]; Omicron CIR 1.20 [95% CI 1.13–1.27]). There was latency in the timing of incident diabetes risk with the Omicron variant; in contrast with other variants, the risk presented after 180 days. CONCLUSIONS: Incident diabetes risk after COVID-19 was similar across different SARS-CoV-2 variants. However, there was greater latency in diabetes onset in the Omicron variant era. [ABSTRACT FROM AUTHOR]

To exploratorily test (1) the impact of HIV and aging process among PLWH on COVID-19 outcomes; and (2) whether the effects of HIV on COVID-19 outcomes differed by immunity level. The data used in this study was retrieved from the COVID-19 positive cohort in National COVID Cohort Collaborative (N3C). Multivariable logistic regression models were conducted on populations that were matched using either exact matching or propensity score matching (PSM) with varying age difference between PLWH and non-PLWH to examine the impact of HIV and aging process on all-cause mortality and hospitalization among COVID-19 patients. Subgroup analyses by CD4 counts and viral load (VL) levels were conducted using similar approaches. Among the 2,422,864 adults with a COVID-19 diagnosis, 15,188 were PLWH. PLWH had a significantly higher odds of death compared to non-PLWH until age difference reached 6 years or more, while PLWH were still at an elevated risk of hospitalization across all matched cohorts. The odds of both severe outcomes were persistently higher among PLWH with CD4 < 200 cells/mm3. VL ≥ 200 copies/ml was only associated with higher hospitalization, regardless of the predefined age differences. Age advancement in HIV might significantly contribute to the higher risk of COVID-19 mortality and HIV infection may still impact COVID-19 hospitalization independent of the age advancement in HIV. [ABSTRACT FROM AUTHOR]

Solid organ transplant recipients (SOTR) are at increased risk from COVID-19. Over time, the absolute risk of adverse outcomes after COVID-19 has decreased in both the nonimmunosuppressed/immunocompromised (non-ISC) general population, and amongst SOTR. Using the N3C, we examined the absolute risk of mortality, major adverse renal or cardiac events, and hospitalization after COVID-19 diagnosis amongst non-ISC and SOTR populations over five waves of the pandemic (Wave 1: Ancestral COVID; Wave 2: Alpha; Wave 3: Delta; Wave 4: Omicron; Wave 5: Omicron). Within each wave, we determined the relative risk of each outcome for SOTR versus the non-ISC population based on crude event rates, and then used multivariable cox proportional hazards models and logistic regression to determine the adjusted risk of each outcome based on SOT status. Throughout the pandemic, including during the Omicron wave (Wave 5), SOTR were at greater absolute risk for each outcome than non-ISC patients (p-values all <0.001). The adjusted risk of SOT status for each outcome was relatively stable over time (aHR 1.28-1.61 for mortality; aHR 1.31-1.47 for MACE; aHR 1.72-1.90 for MARCE; aHR 1.75-2.07 for AKI; and aOR 1.53-1.81 for hospitalization). Despite a reduction in the absolute risk of COVID-19 complications, the relative risk for SOTR versus the non-ISC population has not improved. [ABSTRACT FROM AUTHOR]

Background Patients with coronavirus disease 2019 (COVID-19) often experience persistent symptoms, known as postacute sequelae of COVID-19 or long COVID, after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Chronic lung disease (CLD) has been identified in small-scale studies as a potential risk factor for long COVID. Methods This large-scale retrospective cohort study using the National COVID Cohort Collaborative data evaluated the link between CLD and long COVID over 6 months after acute SARS-CoV-2 infection. We included adults (aged ≥18 years) who tested positive for SARS-CoV-2 during any of 3 SARS-CoV-2 variant periods and used logistic regression to determine the association, considering a comprehensive list of potential confounding factors, including demographics, comorbidities, socioeconomic conditions, geographical influences, and medication. Results Of 1 206 021 patients, 1.2% were diagnosed with long COVID. A significant association was found between preexisting CLD and long COVID (adjusted odds ratio [aOR], 1.36). Preexisting obesity and depression were also associated with increased long COVID risk (aOR, 1.32 for obesity and 1.29 for depression) as well as demographic factors including female sex (aOR, 1.09) and older age (aOR, 1.79 for age group 40–65 [vs 18–39] years and 1.56 for >65 [vs 18–39] years). Conclusions CLD is associated with higher odds of developing long COVID within 6 months after acute SARS-CoV-2 infection. These data have implications for identifying high-risk patients and developing interventions for long COVID in patients with CLD. [ABSTRACT FROM AUTHOR]

(1) Background/Objectives: Dexmedetomidine is a sedative for patients receiving invasive mechanical ventilation (IMV) that previous single-site studies have found to be associated with improved survival in patients with COVID-19. The reported clinical benefits include dampened inflammatory response, reduced respiratory depression, reduced agitation and delirium, improved preservation of responsiveness and arousability, and improved hypoxic pulmonary vasoconstriction and ventilation-perfusion ratio. Whether improved mortality is evident in large, multi-site COVID-19 data is understudied. (2) Methods: The association between dexmedetomidine use and mortality in patients with COVID-19 receiving IMV was assessed. This retrospective multi-center cohort study utilized patient data in the United States from health systems participating in the National COVID Cohort Collaborative (N3C) from 1 January 2020 to 3 November 2022. The primary outcome was 28-day mortality rate from the initiation of IMV. Propensity score matching adjusted for differences between the group with and without dexmedetomidine use. Adjusted hazard ratios (aHRs) for 28-day mortality were calculated using multivariable Cox proportional hazards models with dexmedetomidine use as a time-varying covariate. (3) Results: Among the 16,357,749 patients screened, 3806 patients across 17 health systems met the study criteria. Mortality was lower with dexmedetomidine use (aHR, 0.81; 95% CI, 0.73–0.90; p < 0.001). On subgroup analysis, mortality was lower with earlier dexmedetomidine use—initiated within the median of 3.5 days from the start of IMV—(aHR, 0.67; 95% CI, 0.60–0.76; p < 0.001) as well as use prior to standard, widespread use of dexamethasone for patients on respiratory support (prior to 30 July 2020) (aHR, 0.54; 95% CI, 0.42–0.69; p < 0.001). In a secondary model that was restricted to 576 patients across six health system sites with available PaO2/FiO2 data, mortality was not lower with dexmedetomidine use (aHR 0.95, 95% CI, 0.72–1.25; p = 0.73); however, on subgroup analysis, mortality was lower with dexmedetomidine use initiated earlier than the median dexmedetomidine start time after IMV (aHR, 0.72; 95% CI, 0.53–0.98; p = 0.04) and use prior to 30 July 2020 (aHR, 0.22; 95% CI, 0.06–0.78; p = 0.02). (4) Conclusions: Dexmedetomidine use was associated with reduced mortality in patients with COVID-19 receiving IMV, particularly when initiated earlier, rather than later, during the course of IMV as well as use prior to the standard, widespread usage of dexamethasone during respiratory support. These particular findings might suggest that the associated mortality benefit with dexmedetomidine use is tied to immunomodulation. However, further research including a large randomized controlled trial is warranted to evaluate the potential mortality benefit of DEX use in COVID-19 and evaluate the physiologic changes influenced by DEX that may enhance survival. [ABSTRACT FROM AUTHOR]

Amid the ongoing global repercussions of SARS-CoV-2, it is crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab-negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-19 positive (n = 219,264), and COVID-19 negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-19 negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations. [ABSTRACT FROM AUTHOR]

Background: Observed activity of metformin in reducing the risk of severe COVID-19 suggests a potential use of the anti-hyperglycemic in the prevention of post-acute sequelae of SARS-CoV-2 infection (PASC). We assessed the 3-month and 6-month risk of PASC among patients with type 2 diabetes mellitus (T2DM) comparing metformin users to sulfonylureas (SU) or dipeptidyl peptidase-4 inhibitors (DPP4i) users. Methods: We used de-identified patient level electronic health record data from the National Covid Cohort Collaborative (N3C) between October 2021 and April 2023. Participants were adults ≥ 18 years with T2DM who had at least one outpatient healthcare encounter in health institutions in the United States prior to COVID-19 diagnosis. The outcome of PASC was defined based on the presence of a diagnosis code for the illness or using a predicted probability based on a machine learning algorithm. We estimated the 3-month and 6-month risk of PASC and calculated crude and weighted risk ratios (RR), risk differences (RD), and differences in mean predicted probability. Results: We identified 5596 (mean age: 61.1 years; SD: 12.6) and 1451 (mean age: 64.9 years; SD 12.5) eligible prevalent users of metformin and SU/DPP4i respectively. We did not find a significant difference in risk of PASC at 3 months (RR = 0.86 [0.56; 1.32], RD = − 3.06 per 1000 [− 12.14; 6.01]), or at 6 months (RR = 0.81 [0.55; 1.20], RD = − 4.91 per 1000 [− 14.75, 4.93]) comparing prevalent users of metformin to prevalent users of SU/ DPP4i. Similar observations were made for the outcome definition using the ML algorithm. Conclusion: The observed estimates in our study are consistent with a reduced risk of PASC among prevalent users of metformin, however the uncertainty of our confidence intervals warrants cautious interpretations of the results. A standardized clinical definition of PASC is warranted for thorough evaluation of the effectiveness of therapies under assessment for the prevention of PASC. Plain Language Summary: Previous research suggests that metformin, due to its anti-viral, anti-inflammatory, and anti-thrombotic properties may reduce the risk of severe COVID-19. Given the shared etiology of COVID-19 and the post-acute sequelae of SARS-CoV-2 (PASC), and the proposed inflammatory processes of PASC, metformin may also be a beneficial preventive option. We investigated the benefit of metformin for PASC prevention in a population of type 2 diabetes mellitus patients with a COVID-19 diagnosis who were on metformin or two other anti-hyperglycemic medications prior to infection with SARS-CoV-2. Our results were consistent with a reduction in the risk of PASC with the use of metformin, however, the imprecise confidence intervals obtained warrants further investigation of this association of the potential beneficial effect of metformin for preventing PASC in patients with medication-managed diabetes. [ABSTRACT FROM AUTHOR]

Introduction: People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). Results: Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56–0.72) and SGLT-2i (OR 0.62, 95% CI 0.57–0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81–1.05 and OR 0.88, 95% CI 0.76–1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. Conclusion: Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article. [ABSTRACT FROM AUTHOR]

Background: Dexamethasone was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial, but evidence is still needed to support its real-world effectiveness in heterogeneous populations of patients with a wide range of comorbidities. Methods: COVID-19 inpatients represented within the National COVID Cohort Collaborative (N3C) Data Enclave, prior to vaccine availability, were studied. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome defined by use of ECMO or mechanical ventilation. Missing data were imputed with single imputation. Dexamethasone-treated patients were propensity score (PS) matched to non-dexamethasone-treated controls, stratified by remdesivir treatment and based on demographics, baseline laboratory values, comorbidities, and amount of missing data before imputation. Treatment benefit was quantified using logistic regression. Further sensitivity analyses were performed using clinical adjusters in matched groups and in strata defined by quartiles of PS. Results: Dexamethasone treatment was associated with reduced risk of in-hospital mortality for n = 1,263 treated, matched 1:3 to untreated, patients not receiving remdesivir (OR = 0.77, 95% CI: 0.62 to 0.95, p = 0.017), and for n = 804 treated, matched 1:1 to untreated, patients receiving remdesivir (OR = 0.74, 95% CI: 0.53 to 1.02, p = 0.054). Treatment showed secondary outcome benefit. In sensitivity analyses, treatment effect generally remained similar with some heterogeneity of benefit across quartiles of PS, possibly reflecting concentration of benefit among the more severely affected. Conclusions: We add evidence that dexamethasone provides benefit with respect to mortality and severe outcomes in a diverse, national hospitalized sample, prior to vaccine availability. [ABSTRACT FROM AUTHOR]

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency. [ABSTRACT FROM AUTHOR]

Background Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. Methods We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Results Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19–related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI},.48–.53] to 0.63 [95% CI,.56–.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI,.41–.52] to 0.63 [95% CI,.50–.79]) during both periods. Conclusions Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status. [ABSTRACT FROM AUTHOR]

Background: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. Methods: This was a retrospective case–control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33–1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05–4.73), long (8–30 days, OR 1.69, 95% CI 1.31–2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45–4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18–1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40–1.60), chronic lung disease (OR 1.63, 95% CI 1.53–1.74), and obesity (OR 1.23, 95% CI 1.16–1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course. [ABSTRACT FROM AUTHOR]

Little data is available regarding the incidence of gastrointestinal bleeding in adults hospitalized with COVID‐19 infection and the influence of patient comorbidities and demographics, COVID‐19 therapies, and typical medications used. In this retrospective study, we utilized the National COVID Cohort Collaborative to investigate the primary outcome of the development of gastrointestinal bleeding in 512 467 hospitalized US adults (age >18 years) within 14 days of a COVID‐19 infection and the influence of demographics, comorbidities, and selected medications. Gastrointestinal bleeding developed in 0.44% of patients hospitalized with COVID‐19. Comorbidities associated with gastrointestinal bleeding include peptic ulcer disease (adjusted odds ratio [aOR] 10.2), obesity (aOR 1.27), chronic kidney disease (aOR 1.20), and tobacco use disorder (aOR 1.28). Lower risk of gastrointestinal bleeding was seen among women (aOR 0.76), Latinx (aOR 0.85), and vaccinated patients (aOR 0.74). Dexamethasone alone or with remdesivir was associated with lower risk of gastrointestinal bleeding (aOR 0.69 and aOR 0.83, respectively). Remdesivir monotherapy was associated with upper gastrointestinal bleeding (aOR 1.25). Proton pump inhibitors were more often prescribed in patients with gastrointestinal bleeding, likely representing treatment for gastrointestinal bleeding rather than a risk factor for its development. In adult patients hospitalized with COVID‐19, the use of dexamethasone alone or in combination with remdesivir is negatively associated with gastrointestinal bleeding. Remdesivir monotherapy is associated with increased risk of upper gastrointestinal bleeding. [ABSTRACT FROM AUTHOR]

Background Anticoagulation (AC) utilization patterns and their predictors among hospitalized coronavirus disease 2019 (COVID-19) patients have not been well described. Methods Using the National COVID Cohort Collaborative, we conducted a retrospective cohort study (2020–2022) to assess AC use patterns and identify factors associated with therapeutic AC employing modified Poisson regression. Results Among 162 842 hospitalized COVID-19 patients, 64% received AC and 24% received therapeutic AC. Therapeutic AC use declined from 32% in 2020 to 12% in 2022, especially after December 2021. Therapeutic AC predictors included age (relative risk [RR], 1.02; 95% confidence interval [CI], 1.02–1.02 per year), male (RR, 1.29; 95% CI, 1.27–1.32), non-Hispanic black (RR, 1.16; 95% CI, 1.13–1.18), obesity (RR, 1.48; 95% CI, 1.43–1.52), increased length of stay (RR, 1.01; 95% CI, 1.01–1.01 per day), and invasive ventilation (RR, 1.64; 95% CI, 1.59–1.69). Vaccination (RR, 0.88; 95% CI, 84–.92) and higher Charlson Comorbidity Index (CCI) (RR, 0.98; 95% CI,.97–.98) were associated with lower therapeutic AC. Conclusions Overall, two-thirds of hospitalized COVID-19 patients received any AC and a quarter received therapeutic dosing. Therapeutic AC declined after introduction of the Omicron variant. Predictors of therapeutic AC included demographics, obesity, length of stay, invasive ventilation, CCI, and vaccination, suggesting AC decisions driven by clinical factors including COVID-19 severity, bleeding risks, and comorbidities. [ABSTRACT FROM AUTHOR]

Background Identifying individuals with a higher risk of developing severe coronavirus disease 2019 (COVID-19) outcomes will inform targeted and more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of preexisting autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. Methods A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease and hospitalization, were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. Results Of the 2 453 799 adults diagnosed with COVID-19, 191 520 (7.81%) had a preexisting AID diagnosis and 278 095 (11.33%) had a preexisting IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a preexisting AID (odds ratio [OR], 1.13; 95% confidence interval [CI]: 1.09–1.17; P <.001), IS exposure (OR, 1.27; 95% CI: 1.24–1.30; P <.001), or both (OR, 1.35; 95% CI: 1.29–1.40; P <.001) were more likely to have a life-threatening disease. These results were consistent when hospitalization was evaluated. A sensitivity analysis evaluating specific IS revealed that tumor necrosis factor inhibitors were protective against life-threatening disease (OR, 0.80; 95% CI:.66–.96; P =.017) and hospitalization (OR, 0.80; 95% CI:.73–.89; P <.001). Conclusions Patients with preexisting AID, IS exposure, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19. [ABSTRACT FROM AUTHOR]

Simple Summary: This study analyzed data from the National COVID Cohort Collaborative, the largest US COVID-19 database, to determine if an association exists between COVID-19 and blood levels of high-density lipoprotein, and high-density lipoprotein's main protein component, apolipoprotein A1. Our findings suggest that individuals with higher levels of high-density lipoprotein and apolipoprotein A1 were less likely to be infected with SARS-CoV-2 and were less likely to develop severe COVID-19 requiring hospitalization or invasive medical interventions. They were also less likely to develop acute kidney injury. On the other hand, the presence of underlying health issues (comorbidities) increased the risk of developing severe COVID-19 and acute kidney injury. African American and Hispanic populations were more likely to experience severe disease, while smoking and being male appeared to reduce the incidence of infection but increased the risk of developing severe disease and acute kidney injury. This study is the first to explore the association of high-density lipoprotein and apolipoprotein A1 with COVID-19 outcomes using US population data. This study analyzed data from the National COVID Cohort Collaborative (N3C) database to investigate whether high-density lipoprotein (HDL) and its major protein component, apolipoprotein A1 (apoA1), are associated with severe COVID-19 sequelae, specifically acute kidney injury (AKI) and severe COVID-19 disease as defined by the infection resulting in hospitalization, extracorporeal membrane oxygenation (ECMO), invasive ventilation, or death. Our study included a total of 1,415,302 subjects with HDL values and 3589 subjects with apoA1 values. Higher levels of both HDL and apoA1 were associated with a lower incidence of infection as well as a lower incidence of severe disease. Higher HDL levels were also associated with a lower incidence of developing AKI. Most comorbidities were negatively correlated with SARS-CoV-2 infection, presumably due to the behavioral changes that occurred as a result of the precautions taken by individuals with underlying comorbidities. The presence of comorbidities, however, was associated with developing severe COVID-19 disease and AKI. African American and Hispanic populations experienced worse outcomes, including a higher incidence of infection and the development of severe disease, as well as AKI. Smoking and being male were associated with a lower incidence of infection, while they were risk factors for the development of severe disease and AKI. The results on cholesterol and diabetes drugs warrant further research, given that the database included multiple drugs in each category impeding for analysis of specific medications. Despite the current limitations in the N3C data, this study is the first to investigate the roles of HDL and apoA1 on the outcomes of COVID-19 using the US population data. [ABSTRACT FROM AUTHOR]

As the coronavirus disease 2019 (COVID-19) pandemic progresses to an endemic phase, a greater number of patients with a history of COVID-19 will undergo surgery. Major adverse cardiovascular and cerebrovascular events (MACE) are the primary contributors to postoperative morbidity and mortality; however, studies assessing the relationship between a previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and postoperative MACE outcomes are limited. Here, we analyzed retrospective data from 457,804 patients within the N3C Data Enclave, the largest national, multi-institutional data set on COVID-19 in the United States. However, 7.4% of patients had a history of COVID-19 before surgery. When comorbidities, age, race, and risk of surgery were controlled, patients with preoperative COVID-19 had an increased risk for 30-day postoperative MACE. MACE risk was influenced by an interplay between COVID-19 disease severity and time between surgery and infection; in those with mild disease, MACE risk was not increased even among those undergoing surgery within 4 wk following infection. In those with moderate disease, risk for postoperative MACE was mitigated 8 wk after infection, whereas patients with severe disease continued to have elevated postoperative MACE risk even after waiting for 8 wk. Being fully vaccinated decreased the risk for postoperative MACE in both patients with no history of COVID-19 and in those with breakthrough COVID-19 infection. Together, our results suggest that a thorough assessment of the severity, vaccination status, and timing of SARS-CoV-2 infection must be a mandatory part of perioperative stratification. NEW & NOTEWORTHY With an increasing proportion of patients undergoing surgery with a prior history of COVID-19, it is crucial to understand the impact of SARS-CoV-2 infection on postoperative cardiovascular/cerebrovascular risk. Our work assesses a large, national, multi-institutional cohort of patients to highlight that COVID-19 infection increases risk for postoperative major adverse cardiovascular and cerebrovascular events (MACE). MACE risk is influenced by an interplay between disease severity and time between infection and surgery, and full vaccination reduces the risk for 30-day postoperative MACE. These results highlight the importance of stratifying time-to-surgery guidelines based on disease severity. [ABSTRACT FROM AUTHOR]

Objective Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." Materials and Methods Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. Results Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. Discussion Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. Conclusion This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups. A big data approach to analyzing real-world impacts of therapies may confirm or supplement RCT evidence to further assess effectiveness of therapeutic options for rapidly evolving diseases such as COVID-19. Methods: Gradient Boosted Decision Tree, Deep and Convolutional Neural Network classifiers were implemented and trained on the National COVID Cohort Collaborative (N3C) data repository to predict the patients' outcome of death or discharge. Models leveraged the patients' characteristics, the severity of COVID-19 at diagnosis, and the calculated proportion of days on different treatment combinations after diagnosis as features to predict the outcome. Then, the most accurate model is utilized by eXplainable Artificial Intelligence (XAI) algorithms to provide insights about the learned treatment combination impacts on the model's final outcome prediction. Results: Gradient Boosted Decision Tree classifiers present the highest prediction accuracy in identifying patient outcomes with area under the receiver operator characteristic curve of 0.90 and accuracy of 0.81 for the outcomes of death or sufficient improvement to be discharged. The resulting model predicts the treatment combinations of anticoagulants and steroids are associated with the highest probability of improvement, followed by combined anticoagulants and targeted antivirals. In contrast, monotherapies of single drugs, including use of anticoagulants without steroid or antivirals are associated with poorer outcomes. Conclusions: This machine learning model by accurately predicting the mortality provides insights about the treatment combinations associated with clinical improvement in COVID-19 patients. Analysis of the model's components suggests benefit to treatment with combination of steroids, antivirals, and anticoagulant medication. The approach also provides a framework for simultaneously evaluating multiple real-world therapeutic combinations in future research studies. [ABSTRACT FROM AUTHOR]