Renin-angiotensin system inhibitors and severity of SARS-CoV-2 infection: a meta-analysis
Bibliographic record
Abstract
Abstract Introduction The mechanism of entry of SARS-CoV-2 into the human host cell is through the ACE2 receptor. During the pandemic, a hypothesis has been proposed that Angiotensin-converting enzyme inhibitors (ACEi) and Angiotensin II receptor blockers (ARBs) could be risk factors for the development of severe SARS-CoV-2 infection. Objective To conduct a meta-analysis of the association between ACEi or ARB use and SARS-CoV-2 infection severity or mortality.Data Sources We searched PubMed, EMBASE, Google scholar and the Cochrane Database of Systematic Reviews for observational studies published between December 2019 and April 24, 2020Study Selection: Studies were included if they contained data on ACEi or ARB use and SARS-CoV-2 infection severity or mortality. Effect statistics were pooled using random-effects models. The quality of included studies was assessed with the Newcastle–Ottawa Scale (NOS). Data ExtractionData on study design, study location, year of publication, study design, number of participants, sex, age at baseline, outcome definition, exposure definition, follow-up, effect estimates and 95% Cis.Results Thirteen observational studies were identified for inclusion, combining to a total sample of 14364 participants. Mean age was 59.2 (SD 7.3) years and 53.5% were men. Mean follow-up was 28.3 (14.2) days. The mean NOS score of included studies was 7.8 (range: 7-9). Results suggested that ACEi or ARB use did not increase the risk of severe disease or mortality from SARS-CoV-2 infection (OR=0.72, 95% CI 0.47-1.11, p= 0.138).ConclusionsAt present, the limited evidence available does not support the hypothesis of increased SARS-CoV-2 risk with ACEi or ARB drugs. However, more evidence needs to accumulate before this controversy can be resolved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.060 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".