Angiotensin Converting Enzyme Inhibitors and Angiotensin Receptor Blockers and Outcome of COVID-19 : A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Importance There is conflicting evidence about the role of angiotensin converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) in the pathogenesis and outcome of patients infected with acute severe respiratory syndrome coronavirus 2 (SARS-CoV-2) and growing public concerns about their use during this pandemic. Objective We sought to systematically review the literature and perform a meta-analysis about prior use of ACEI/ARBs and outcome of COVID-19 patients. Data source We searched multiple data sources including PubMed, ClinicalTrial.org, and medrxiv.org from November 2019 through May 3, 2020. Study selection Any study that reported on the role of ACEIs / ARBs and outcome of COIVD-19 is 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. I 2 test was used to assess in between studies heterogeneity. The Newcastle-Ottawa quality assessment scale was used to assess the quality of included studies. Main outcome and Measures The association between the prior use of ACEIs / ARBs and the mortality among SARS-CoV-2 infected patients was assessed using pooled OR and 95% confidence interval. For studies that did not report adjusted effect estimates for mortality, we used their adjusted effect estimate of critical outcome to estimate another pooled OR for critical or fatal outcome and its 95% confidence interval. Results Nine studies were included in this systematic review. The studies included a total of 58,615 patients infected with SARS-CoV-2. Prior use of ACEIs and/or ARBs was associated with a significant reduction of inpatient mortality among SARS-CoV-2 infected patients, with a pooled adjusted OR from 4 studies of 0.33, 95% CI [0.22, 0.49] with no between studies heterogeneity (12=0%) and with a significant reduction of critical or fatal outcome, with a pooled adjusted OR from 5 studies of 0.32, 95% CI [0.22, 0.46] (12 =32%). Conclusion Our meta-analysis suggests that use of ACEIs/ARBs is associated with a decreased risk of death or critical outcome among SARS-CoV-2 infected patients. This finding is limited by the observational nature of the included studies. However, it provides a reassurance to the public not to stop prescribed ACEIs/ARBs due to fear of severe COVID-19. It also calls upon investigators and ethics committees to reconsider the ongoing randomized trials of discontinuation of these drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".