Prospective meta-analysis protocol on randomised trials of renin–angiotensin system inhibitors in patients with COVID-19: an initiative of the International Society of Hypertension
Bibliographic record
Abstract
INTRODUCTION: Whether ACE inhibitors (ACEi) or angiotensin II receptor blocker (ARB) therapy should be continued, initiated or ceased in patients with COVID-19 is uncertain. Given the widespread use of ACEi/ARBs worldwide, guidance on the use of these drugs is urgently needed. This prospective meta-analysis aims to pool data from randomised controlled trials (RCTs) to assess the safety and efficacy of ACEi/ARB therapy in adults infected with SARS-CoV-2. METHODS AND ANALYSIS: RCTs will be eligible if they compare patients with COVID-19 randomised to ACEi/ARB continuation or commencement versuss no ACEi/ARB therapy; study duration ≥14 days; recruitment completed between March 2020 and May 2021. The primary outcome will be all-cause mortality at ≤30 days. Secondary outcomes will include mechanical ventilation, admission to intensive care or cardiovascular events at short-term follow-up (≤30 days) and all-cause mortality at longer-term follow-up (>1 month). Prespecified subgroup analyses will assess the effect of sex; age; comorbidities; smoking status; ethnicity; country of origin on all-cause mortality. A search of ClinicalTrials.gov has been performed, which will be followed by a formal search of trial registers, preprint servers, MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials to identify RCTs that meet inclusion criteria. To date, a search of ClinicalTrials.gov identified 21 potentially eligible trials for this meta-analysis. We will request trial investigators/sponsors to contribute standardised grouped tabular outcome data. ETHICS AND DISSEMINATION: Ethics approval and informed consent will be the responsibility of the individual RCTs. Dissemination of results will occur by peer-reviewed publication. The results of our analysis can inform public health policy and clinical decision making regarding ACEi/ARB use in patients with COVID-19 on a global scale.
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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 | 0.082 | 0.134 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.026 | 0.024 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.083 | 0.013 |
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".