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BackgroundEmerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. Therefore, it is important to know how the different vaccines perform against diverse SARS-CoV-2 variants. MethodsIn a prospective cohort of 165 SARS-CoV-2 naive health care workers, vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S), we performed a head-to-head comparison of the ability of sera to recognize and neutralize SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma, Delta and Omicron). Repeated serum sampling was performed 5 times during a year (from January 2021 till January 2022), including before and after booster vaccination with BNT162b2. FindingsFour weeks after completing the initial vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of BNT162b2 and mRNA-1273 (geometric mean titers (GMT) of 197 [95% CI 149-260] and 313 [95% CI 218-448], respectively), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (GMT of 26 [95% CI 18-37] and 14 [95% CI 8-25] IU/ml, respectively). These findings were robust for adjustment to age and sex. VOCs neutralization was reduced in all vaccine groups, with the largest (9- to 80-fold) reduction in neutralization observed against the Omicron variant. The booster BNT162b2 vaccination increased neutralizing antibody titers for all groups with substantial improvement against the VOCs including the Omicron variant. Study limitations include the lack of cellular immunity data. ConclusionsOverall, this study shows that the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after initial vaccination and after booster vaccination.
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Emerging SARS-CoV-2 variants pose a threat to human immunity induced by natural infection and vaccination. We assessed the recognition of three variants of concern (B.1.1.7, B.1.351 and P.1) in cohorts of COVID-19 patients ranging in disease severity (n = 69) and recipients of the Pfizer/BioNTech vaccine (n = 50). Spike binding and neutralization against all three VOC was substantially reduced in the majority of samples, with the largest 4-7-fold reduction in neutralization being observed against B.1.351. While hospitalized COVID-19 patients and vaccinees maintained sufficient neutralizing titers against all three VOC, 39% of non-hospitalized patients did not neutralize B.1.351. Moreover, monoclonal neutralizing antibodies (NAbs) show sharp reductions in their binding kinetics and neutralizing potential to B.1.351 and P.1, but not to B.1.1.7. These data have implications for the degree to which pre-existing immunity can protect against subsequent infection with VOC and informs policy makers of susceptibility to globally circulating SARS-CoV-2 VOC.
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BACKGROUNDIt is unclear how, when and where health care workers (HCW) working in hospitals are infected with SARS-CoV-2. METHODSProspective cohort study comprising 4-weekly measurement of SARS-CoV-2 specific antibodies and questionnaires from March to June 2020. We compared SARS-CoV-2 incidence between HCW working in Covid-19 patient care, HCW working in non-Covid-19 patient care and HCW not in patient care. Phylogenetic analyses of SARS-CoV-2 samples from patients and HCW were performed to identify potential transmission clusters. RESULTSWe included 801 HCW: 439 in the Covid-19 patient care group, 164 in the non-Covid-19 patient care group and 198 in the no patient care group. SARS-CoV-2 incidence was highest in HCW working in Covid-19 patient care (13.2%), as compared with HCW in non-Covid-19 patient care (6.7%, hazard ratio [HR] 2.2, 95% confidence interval [CI] 1.2 to 4.3) and in HCW not working in patient care (3.6%, HR 3.9, 95% CI 1.8 to 8.6). Within the group of HCW caring for Covid-19 patients, SARS-CoV-2 cumulative incidence was highest in HCW working on Covid-19 wards (25.7%), as compared with HCW working on intensive care units (7.1%, HR 3.6, 95% CI 1.9 to 6.9), and HCW working in the emergency room (8.0%, HR 3.3, 95% CI 1.5 to 7.1). Phylogenetic analyses on Covid-19 wards identified multiple potential HCW-to-HCW transmission clusters while no patient-to-HCW transmission clusters were identified. CONCLUSIONSHCW working on Covid-19 wards are at increased risk for nosocomial SARS-CoV-2 infection, with an important role for HCW-to-HCW transmission. (Funded by the Netherlands Organization for Health Research and Development ZonMw & the Corona Research Fund Amsterdam UMC; Netherlands Trial Register number NL8645)
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ObjectiveDevelop and validate models that predict mortality of SARS-CoV-2 infected patients admitted to the hospital. DesignRetrospective cohort study SettingA multicenter cohort across ten Dutch hospitals including patients from February 27 to June 8 2020. ParticipantsSARS-CoV-2 positive patients (age [≥] 18) admitted to the hospital. Main Outcome Measures21-day mortality evaluated by the area under the receiver operatory curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from analysis. Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory & radiology values, were derived from 80 features. Additionally, an ANOVA-based data-driven feature selection selected the ten features with the highest F-values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression (LR) and non-linear tree-based gradient boosting (XGB) algorithm fitted the data with an AUC of 0.81 (95% confidence interval 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the ten selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age > 70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81) ConclusionBoth models showed excellent performance and had better test characteristics than age-based decision rules, using ten admission features readily available in Dutch hospitals. The models hold promise to aid decision making during a hospital bed shortage.