Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C)

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.

Comparative Effectiveness of Dexamethasone in Treatment of Hospitalized COVID-19 Patients during the First Year of the Pandemic: The N3C Data Repository

Background

Dexamethasone, a widely available glucocorticoid, was approved for use in hospitalized COVID-19 patients early in the pandemic based on the RECOVERY trial; however, evidence is still needed to support its real-world effectiveness in patients with a wide range of comorbidities and in diverse care settings.

Objectives

To conduct a comparative effectiveness analysis of dexamethasone use with and without remdesivir in hospitalized COVID-19 patients using electronic health record data.

Methods

We conducted a retrospective real-world effectiveness analysis using the harmonized, highly granular electronic health record data of the National COVID Cohort Collaborative (N3C) Data Enclave. Analysis was restricted to COVID-19 patients in an inpatient setting, prior to vaccine availability. Primary outcome was in-hospital death; secondary outcome was combined in-hospital death and severe outcome as defined by use of ECMO or mechanical ventilation during stay. Missing data were imputed with single imputation. Matching of dexamethasone-treated patients to non-dexamethasone-treated controls was accomplished using propensity score (PS) matching, stratified by remdesivir treatment and based on demographics, baseline laboratory values, and comorbidities. 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

Regression analysis revealed a statistically significant association between dexamethasone use and reduced risk of in-hospital mortality for those not receiving remdesivir (OR=0.77, 95% CI:0.62 to 0.95, p=0.017), and a borderline statistically significant risk for those receiving remdesivir (OR=0.74, 95% CI: 0.53 to 1.02, p=0.054). Treatment also showed secondary outcome benefit. In sensitivity analyses, treatment effect size generally remained similar with some heterogeneity of benefit across strata of PS.

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.

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 US took nearly two 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 = 21,072), 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, high education, and high access to medical care. 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.

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.