A Retrospective Analysis of COVID-19 mRNA Vaccine Breakthrough Infections - Risk Factors and Vaccine Effectiveness

ImportanceLittle is known about COVID vaccine breakthrough infections and their risk factors. ObjectiveTo identify risk factors associated with COVID-19 breakthrough infections among vaccinated individuals and to reassess the effectiveness of COVID-19 vaccination against severe outcomes using real-world data. Design, Setting, and ParticipantsWe conducted a series of observational retrospective analyses using the electronic health records (EHRs) of Columbia University Irving Medical Center/New York Presbyterian (CUIMC/NYP) up to September 21, 2021. New York adult residence with PCR test records were included in this analysis. Main Outcomes and MeasuresPoisson regression was used to assess the association between breakthrough infection rate in vaccinated individuals and multiple risk factors - including vaccine brand, demographics, and underlying conditions - while adjusting for calendar month, prior number of visits and observational days. Logistic regression was used to assess the association between vaccine administration and infection rate by comparing a vaccinated cohort to a historically matched cohort in the pre-vaccinated period. Infection incident rate was also compared between vaccinated individuals and longitudinally matched unvaccinated individuals. Cox regression was used to estimate the association of the vaccine and COVID-19 associated severe outcomes by comparing breakthrough cohort and two matched unvaccinated infection cohorts. ResultsIndividuals vaccinated with Pfizer/BNT162b2 (IRR against Moderna/mRNA-1273 [95% CI]: 1.66 [1.17 - 2.35]); were male (1.47 [1.11 - 1.94%]); and had compromised immune systems (1.48 [1.09 - 2.00]) were at the highest risk for breakthrough infections. Vaccinated individuals had a significant lower infection rate among all subgroups. An increased incidence rate was found in both vaccines over the time. Among individuals infected with COVID-19, vaccination significantly reduced the risk of death (adj. HR: 0.20 [0.08 - 0.49]). Conclusion and RelevanceWhile we found both mRNA vaccines were effective, Moderna/mRNA-1273 had a lower incidence rate of breakthrough infections. Both vaccines had increased incidence rates over the time. Immunocompromised individuals were among the highest risk groups experiencing breakthrough infections. Given the rapidly changing nature of the SARS-CoV-2, continued monitoring and a generalizable analysis pipeline are warranted to inform quick updates on vaccine effectiveness in real time. Key PointsO_ST_ABSQuestionC_ST_ABSWhat risk factors contribute to COVID-19 breakthrough infections among mRNA vaccinated individuals? How do clinical outcomes differ between vaccinated but still SARS-CoV-2 infected individuals and non-vaccinated, infected individuals? FindingsThis retrospective study uses CUIMC/NYP EHR data up to September 21, 2021. Individuals who were vaccinated with Pfizer/BNT162b2, male, and had compromised immune systems had significantly higher incidence rate ratios of breakthrough infections. Comparing demographically matched pre-vaccinated and unvaccinated individuals, vaccinated individuals had a lower incidence rate of SARS-CoV-2 infection among all subgroups. MeaningLeveraging real-world EHR data provides insight on who may optimally benefit from a booster COVID-19 vaccination.

COHD-COVID: Columbia Open Health Data for COVID-19 Research

Massive research efforts have been made in response to the COVID-19 (coronavirus disease-2019) pandemic. Utilization of clinical data can accelerate these research efforts to fight against the pandemic since important characteristics of the patients are often found by examining the clinical data. To provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized COVID-19 patients. COHD-COVID also provides data on hospitalized influenza patients and general hospitalized patients as comparator cohorts. The data used in COHD-COVID were obtained from Columbia University Irving Medical Centers electronic health records. We expect COHD-COVID will provide researchers and clinicians quantitative measures of COVID-19 related clinical features to better understand and fight against the pandemic.

Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on the global healthcare systems. Many prediction models have been proposed to fight against the pandemic. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patients historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patients historical EHR so that it can enable proactive risk management before or at the time of hospital admission.