Angiotensin Converting Enzyme Inhibitors and Angiotensin Receptor Blockers and The Risk of SARS-CoV-2 Infection: A Systematic Review and Meta-analysis 
Bibliographic record
Abstract
Abstract Importance:SARS-CoV-2 virus gains access and infects target cells via angiotensin converting enzyme 2 (ACE2) receptor. Because angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) could increase the expression of ACE2, there are growing concerns that their use could increase the risk of SARS-CoV-2 infection. Cardiac societies have called for epidemiological research about this emerging controversy. Objective:We sought to systematically review the literature and perform a meta-analysis about prior use of ACEI/ARBs and risk of SARS-CoV-2 infection.Data source:We searched multiple data sources including PubMed , ClinicalTrial.org , and medrxiv.org from November 2019 through May 16, 2020. Study selection:Any study that reported on the adjusted association of prior use of ACEIs / ARBs and risk of acquiring SARS-CoV-2 infection was eligible. Two authors independently reviewed eligible studies and extracted data into a prespecified data collection form. Data synthesis:An inverse variance meta-analytic approach was used to pool adjusted odds ratios using a random effect model meta-analysis. I2 test was used to assess in-between studies heterogeneity. The Newcastle–Ottawa quality assessment scale (NOS) was used to assess the quality of included studies. Main outcome and Measures:The association between the prior use of ACEIs or ARBs and risk of SARS-CoV-2 infection was assessed using pooled OR and 95% confidence interval. Results:Six case control studies that enrolled a total of 5657 patients (2536 patients in ACEIs arm and 3121 patients in ARBs arm ) and 721,859 controls were included in our meta-analysis. Two of the included studies were from the USA, one from Italy, one from China, one from Spain, and one from South Korea. All included studies scored high based on NOS scale. Prior use of ACEIs was not significantly associated with an increased risk of SARS-CoV-2 infection, OR 0.93, CI (0.85,1.02), I2=20%. Similarly, prior use of ARBs was not significantly associated with an increased risk of SARS-CoV-2 infection, OR 0.86, CI (0.67,1.10), I2=93%. Sensitivity analysis was performed by removing a study that could have been affected by residual confounding; OR for ARB 1.04, CI (0.96,1.12), I2=32%.Conclusion:Findings from this systematic review and meta-analysis suggest that prior use of ACEIs or ARBs is not associated with a higher risk of COVID-19. Our results are in support of the recent recommendations of cardiac societies and provide a reassurance to the public not to discontinue prescribed ACEIs/ARBs due to fear of COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".