Angiotensin Receptor Blockers and Angiotensin-Converting Enzyme Inhibitors in COVID-19: Meta-analysis/Meta-regression Adjusted for Confounding Factors
Bibliographic record
Abstract
BACKGROUND: Angiotensin receptor blockers (ARBs) and/or angiotensin-converting enzyme (ACE) inhibitors could alter mortality from coronavirus disease 2019 (COVID-19), but existing meta-analyses that combined crude and adjusted results may be confounded by the fact that comorbidities are more common in ARB/ACE inhibitor users. METHODS: We searched PubMed/MEDLINE/Embase for cohort studies and meta-analyses reporting mortality by preexisting ARB/ACE inhibitor treatment in hospitalized COVID-19 patients. Random effects meta-regression was used to compute pooled odds ratios for mortality adjusted for imbalance in age, sex, and prevalence of cardiovascular disease, hypertension, diabetes mellitus, and chronic kidney disease between users and nonusers of ARBs/ACE inhibitors at the study level during data synthesis. RESULTS: In 30 included studies of 17,281 patients, 22%, 68%, 25%, and 11% had cardiovascular disease, hypertension, diabetes mellitus, and chronic kidney disease. ARB/ACE inhibitor use was associated with significantly lower mortality after controlling for potential confounding factors (odds ratio 0.77 [95% confidence interval: 0.62, 0.96]). In contrast, meta-analysis of ARB/ACE inhibitor use was not significantly associated with mortality when all studies were combined with no adjustment made for confounders (0.87 [95% confidence interval: 0.71, 1.08]). CONCLUSIONS: ARB/ACE inhibitor use was associated with decreased mortality in cohorts of COVID-19 patients after adjusting for age, sex, cardiovascular disease, hypertension, diabetes, and chronic kidney disease. Unadjusted meta-analyses may not be appropriate for determining whether ARBs/ACE inhibitors are associated with mortality from COVID-19 because of indication bias.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.080 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".