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

Importance: Little is known about COVID vaccine breakthrough infections and their risk factors. Objective: To identify risk factors associated with COVID19 breakthrough infections among vaccinated individuals and to reassess the effectiveness of COVID19 vaccination against severe outcomes using realworld data. Design, Setting, and Participants: We 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 Measures: Poisson 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 COVID19 associated severe outcomes by comparing breakthrough cohort and two matched unvaccinated infection cohorts. Results: Individuals vaccinated with Pfizer/BNT162b2 (IRR against Moderna/mRNA1273 [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 COVID19, vaccination significantly reduced the risk of death (adj. HR: 0.20 [0.08 - 0.49]). Conclusion and Relevance: While we found both mRNA vaccines were effective, Moderna/mRNA1273 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 SARSCoV2, continued monitoring and a generalizable analysis pipeline are warranted to inform quick updates on vaccine effectiveness in real time.

COHD-COVID: Columbia Open Health Data for COVID-19 Research (preprint)

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 (preprint)

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on global healthcare systems. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient's 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 patient's historical EHR to enable proactive risk management at the time of hospital admission