ABSTRACT
Background: Data on the effectiveness of the bivalent booster vaccine against COVID-19 breakthrough infection and severe outcomes is limited. Method(s): Using patient-level data from 54 sites in the U.S. National COVID Cohort Collaborative (N3C), we estimated bivalent booster effectiveness against breakthrough infection and outcomes between 09/01/2022 (bivalent vaccine approval date) to 12/15/2022 (most recent data release of N3C) among patients completed 2+ doses of mRNA vaccine. Bivalent booster effectiveness was evaluated among all patients and patients with and without immunosuppressed/compromised conditions (ISC;HIV infection, solid organ/ bone marrow transplant, autoimmune diseases, and cancer). We used logistic regression models to compare the odds of breakthrough infection (COVID-19 diagnosis after the last dose of vaccine) and outcomes (hospitalization, ventilation/ECMO use, or death <=28 days after infection) in the bivalent boosted vs. non-bivalent boosted groups. Models controlled for demographics, comorbidities, geographic region, prior SARS-CoV-2 infection, months since the last dose of non-bivalent vaccine, and prior non-bivalent booster. Result(s): By 12/15/2022, 2,414,904 patients had received 2+ doses of mRNA vaccination, 75,873 of them had received a bivalent booster vaccine, and 24,046 of them had a breakthrough infection. At baseline, the median age was 52 (IQR 36-67) years, 40% male, 63% white, 10% Black, 12% Latinx, 3.5% Asian American/Pacific Islander, and 14% were patients with ISC. Patients received a bivalent booster were more likely to be female and had comorbidities. Bivalent booster was significantly associated with reduced odds of breakthrough infection and hospitalization (Figure). The adjusted odds ratios comparing bivalent vs. non-bivalent group were 0.28 (95% CI 0.25, 0.32) for all patients and 0.33 (95% CI: 0.26, 0.41) for patients with ISC. Compared to the nonbivalent group, the bivalent group had a lower incidence of COVID-19-related hospitalization (151 vs. 41 per 100,000 persons), invasive ventilation/ECMO use (7.5 vs. 1.3 per 100,000 persons), or death (11 vs. 1.3 per 100,000 persons) in all patients during the study period;the incidence of severe outcomes after bivalent boosting was similar among patients with and without ISC. Conclusion(s): A bivalent booster vaccine was highly effective against COVID-19 breakthrough infection and severe outcomes among patients received 2+ doses of mRNA vaccine and offered similar protection in patients with and without ISC. (Figure Presented).
ABSTRACT
Background Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.Methods We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.Findings We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
ABSTRACT
Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C)1 to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis. © 2022 IEEE.
ABSTRACT
The Mondo Disease Ontology (Mondo) represents cross-species diseases, which integrates several source disease terminologies to represent cross-species diseases, and provides precise semantic mappings to the original sources. Mondo spans both rare and 'common' diseases, as well as monogenic, acquired, neoplasms, infectious diseases, and more. Mondo is a community resource and is continuously updated and iteratively curated. Recent efforts sought to improve the representation of viral infectious diseases in Mondo, to properly represent primary infections, diseases caused by reactivation of a latent virus, such as shingles and diseases caused by aftereffects of a primary infection such as long COVID-19. This included the addition of new classes and new relations (object properties), and the creation of new design patterns. © 2021 Copyright for this paper by its authors.
ABSTRACT
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.