ABSTRACT
Background: SARS-CoV-2 continues to change over time due to genetic mutations and viral recombination.1 Given the changing landscape of COVID-19 variants and availability of COVID-19 vaccinations, disease severity during acute infection has also been variable. However, most research related to COVID-19 to date has not focused on evaluating differences in outcomes by the dominant variant and the impact it might have on post-acute sequalae of COVID-19 (PASC). Method(s): We developed a data mart of electronic health record data pertaining to COVID-19 in a single North American metropolitan health system (RUSH University Medical Center). Patients were selected for analysis if they had at least one documented infection of COVID-19. Date ranges were established per dominant variant, and the date of diagnosis was matched to variant. Variants were determined by the most prominent variant of concern (VOC) circulating in the city of Chicago. Variants were categorized by the following by date ranges: Wildtype+D614G (3/7/20-3/20/21), Alpha (3/21/21-6/19/21), Delta (6/20/21-12/11/21), Omicron BA.1 (12/12/21-3/19/22), Omicron BA.2 (3/20/22- 6/18/22), and Omicron BA.4/BA.5 (6/19/22-present (9/30/22). Subsequent clinical outcomes were examined, including hospitalization, intensive care unit admission, or death. We characterized our sample by conducting descriptive statistics including frequency and percent of outcome by variant. Result(s): 44,499 patients were included in this analysis with 30.23% requiring hospitalization, 4.25% being admitted to intensive care unit (ICU), and 2.35% resulting in death. The greatest percentage of hospitalizations occurred with the Alpha variant at 41.88% (N=928), and the greatest percentage of ICU admissions (6.43%) and death (3.15%) occurred with the Delta variant. The latest Omicron variant (Wave 6) showed an increase in hospitalizations (35.18%), as compared to early Omicron waves (Wave 4 and 5) but maintained similar ICU rates. Death rates continued to decline during the Omicron waves (Table 1). Conclusion(s): Although Alpha and Delta variants seem to have more severe outcomes compared to other variants, it is important to note that COVID-19 prevention, treatment access, and management continues to change, potentially influencing how outcomes may differ over time. Future work should determine factors to adjust for when examining variant-level differences.
ABSTRACT
Background: Accurate measurement of antibodies is a necessary tool for assessing exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and facilitating an understanding of the role antibodies play in overall immunity. Most available assays are qualitative in nature and employ a threshold to determine the presence of antibodies, however some-quantitative assays are now available. Using cross-sectional data collected as part of an ongoing longitudinal cohort study, we aim to assess the seroprevalence of SARSCoV-2 antibodies using the Abbott AdviseDX SARS-CoV-2 IgG II (anti-S) assay and compare these results to previously measured seroprevalence of anti-nucleoprotein (anti-N) IgG in this cohort of health care workers (HCWs) at an academic medical center in Boston. Method(s): A total of 1,743 HCWs at Boston Medical Center (BMC) provided serum samples that were analyzed for SARS-CoV-2 anti-S IgG and IgM using the Abbott AdviseDx SARS-CoV-2 IgG II and Abbott AdviseDx SARS-CoV-2 IgM assay, respectively. These results were compared to previously assessed anti-N IgG seroprevalence. Precision, linearity, and positive and negative concordance with prior reverse transcription-polymerase chain reaction (RT-PCR) test were evaluated for the anti-S IgG II assay. Seroprevalence and its association with demographic variables was also assessed. Result(s): Linearity and precision results were clinically acceptable. The anti-S IgG positive and negative concordance with RT-PCR results were 88.2% (95% CI: 79.4-94.2%) and 97.4% (95% CI: 95.2-98.8%), respectively. Overall, 126 (7.2%) of 1,743 participants were positive for anti-S IgG. The original agreement in this population with the qualitative, anti-N IgG assay was 70.6%. Upon optimizing the threshold from 1.4 to 0.49 signal to cut-off ratio (S/CO) of the anti-N IgG assay, the positive agreement of the assay increased to 84.7%. Conclusion(s): The anti-S IgG II assay demonstrated reproducible and reliable measurements. Higher anti-S IgG to anti-N IgG seroprevalence highlights the present differences between serum antibodies to different epitopes of the SARS-CoV-2 virus. Further, the greater seroprevalence of anti-S IgG compared to positive RT-PCR results points to a potential for asymptomatic infection among this group of HCWs. Our results also highlight the potential utility in optimizing thresholds of the qualitative SARS-CoV-2 anti-N IgG assay for better agreement with the anti-S IgG II assay by the same vendor.Copyright © 2022 by the Author(s).