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JAMIA Open ; 5(3) (no pagination), 2022.
Article in English | EMBASE | ID: covidwho-2135374


Objectives: Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data. Material(s) and Method(s): An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis. Result(s): The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period. Discussion(s): An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time. Conclusion(s): The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation. Copyright © 2022 The Author(s).

Hepatology ; 2022.
Article in English | EMBASE | ID: covidwho-2074992


Background and Aims: Outcomes of breakthrough SARS-CoV-2 infections have not been well characterized in non-veteran vaccinated patients with chronic liver diseases (CLD). We used the National COVID Cohort Collaborative (N3C) to describe these outcomes. Approach and Results: We identified all CLD patients with or without cirrhosis who had SARS-CoV-2 testing in the N3C Data Enclave as of January 15, 2022. We used Poisson regression to estimate incidence rates of breakthrough infections and Cox survival analyses to associate vaccination status with all-cause mortality at 30 days among infected CLD patients. We isolated 278,457 total CLD patients: 43,079 (15%) vaccinated and 235,378 (85%) unvaccinated. Of 43,079 vaccinated patients, 32,838 (76%) were without cirrhosis and 10,441 (24%) with cirrhosis. Breakthrough infection incidences were 5.4 and 4.9 per 1000 person-months for fully vaccinated CLD patients without cirrhosis and with cirrhosis, respectively. Of the 68,048 unvaccinated and 10,441 vaccinated CLD patients with cirrhosis, 15% and 3.7%, respectively, developed SARS-CoV-2 infection. The 30-day outcome of mechanical ventilation or death after SARS-CoV-2 infection for unvaccinated and vaccinated CLD patients with cirrhosis were 15.2% and 7.7%, respectively. Compared to unvaccinated patients with cirrhosis, full vaccination was associated with a 0.34-times adjusted hazard of death at 30 days. Conclusion(s): In this N3C study, breakthrough infection rates were similar among CLD patients with and without cirrhosis. Full vaccination was associated with a 66% reduction in risk of all-cause mortality for breakthrough infection among CLD patients with cirrhosis. These results provide an additional impetus for increasing vaccination uptake in CLD populations. Copyright © 2022 American Association for the Study of Liver Diseases.

Patterns ; 2(1):100155, 2021.
Article in English | MEDLINE | ID: covidwho-1209447


Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks;the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.