Association of Renin-Angiotensin-Aldosterone Inhibitors with COVID-19 Infection and Disease Severity among Individuals with Hypertension.

BACKGROUND: The association between use of renin-angiotensin-aldosterone (RAAS) inhibitors and both SARS-CoV-2 infection and the development of severe COVID-19 has been presented in the recent medical literature with inconsistent results. OBJECTIVES: To assess the association between RAAS inhibitor use and two outcomes: infection with SARS-CoV-2 (Model 1) and severe COVID-19 among those infected (Model 2). METHODS: We accessed used electronic health records of individuals from Israel who were receiving anti-hypertensive medications for this retrospective study. For Model 1 we used a case-control design. For Model 2 we used a cohort design. In both models, inverse probability weighting adjusted for identified confounders as part of doubly robust outcome regression. RESULTS: We tested 38,554 individuals for SARS-CoV-2 who had hypertension and were being treated with medication; 691 had a positive test result. Among those with a positive test, 119 developed severe illness. There was no association between RAAS inhibitor use and a positive test. Use of RAAS inhibitors was associated with a decreased risk for severe COVID-19 (adjusted odds ratio [OR] 0.47, 95% confidence interval [95%CI] 0.29-0.77) compared with users of non-RAAS anti-hypertensive medication. The association remained significant when use of angiotensin-converting-enzyme inhibitors (adjusted OR 0.46, 95%CI 0.27-0.77) and angiotensin II receptor blockers (adjusted OR 0.39, 95%CI 0.16-0.95) were analyzed separately. CONCLUSIONS: Among individuals with hypertension using RAAS inhibitors, we found a lower risk of severe disease compared to those using non-RAAS anti-hypertensive medications. This finding suggests that RAAS inhibitors may have a protective effect on COVID-19 severity among individuals with medically treated hypertension.

Evidence for increased breakthrough rates of SARS-CoV-2 variants of concern in BNT162b2-mRNA-vaccinated individuals.

The BNT162b2 mRNA vaccine is highly effective against SARS-CoV-2. However, apprehension exists that variants of concern (VOCs) may evade vaccine protection, due to evidence of reduced neutralization of the VOCs B.1.1.7 and B.1.351 by vaccine sera in laboratory assays. We performed a matched cohort study to examine the distribution of VOCs in infections of BNT162b2 mRNA vaccinees from Clalit Health Services (Israel) using viral genomic sequencing, and hypothesized that if vaccine effectiveness against a VOC is reduced, its proportion among breakthrough cases would be higher than in unvaccinated controls. Analyzing 813 viral genome sequences from nasopharyngeal swabs, we showed that vaccinees who tested positive at least 7 days after the second dose were disproportionally infected with B.1.351, compared with controls. Those who tested positive between 2 weeks after the first dose and 6 days after the second dose were disproportionally infected by B.1.1.7. These findings suggest reduced vaccine effectiveness against both VOCs within particular time windows. Our results emphasize the importance of rigorously tracking viral variants, and of increasing vaccination to prevent the spread of VOCs.

Developing a COVID-19 mortality risk prediction model when individual-level data are not available.

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.