A systematic review and meta‐analysis of the use of renin‐angiotensin system drugs and COVID‐19 clinical outcomes: What is the evidence so far?
Bibliographic record
Abstract
Abstract Conflicting evidence exists about the effect of angiotensin‐converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) on COVID‐19 clinical outcomes. We aimed to provide a comprehensive/updated evaluation of the effect of ACEIs/ARBs on COVID‐19‐related clinical outcomes, including exploration of interclass differences between ACEIs and ARBs, using a systematic review/meta‐analysis approach conducted in Medline (OVID), Embase, Scopus, Cochrane library, and medRxiv from inception to 22 May 2020. English studies that evaluated the effect of ACEIs/ARBs among patients with COVID‐19 were included. Studies’ quality was appraised using the Newcastle‐Ottawa Scale. Data were analyzed using the random‐effects modeling stratified by exposure (ACEIs/ARBs, ACEIs, and ARBs). Heterogeneiity was assessed using I2 statistic. Several subgroup analyses were conducted to explore the impact of potential confounders. Overall, 27 studies were eligible. The pooled analyses showed nonsignificant associations between ACEIs/ARBs and death (OR:0.97, 95%CI:0.75,1.27), ICU admission (OR:1.09;95%CI:0.65,1.81), death/ICU admission (OR:0.67; 95%CI:0.52,0.86), risk of COVID‐19 infection (OR:1.01; 95%CI:0.93,1.10), severe infection (OR:0.78; 95%CI:0.53,1.15), and hospitalization (OR:1.15; 95%CI:0.81,1.65). However, the subgroup analyses indicated significant association between ACEIs/ARBs and hospitalization among USA studies (OR:1.59; 95%CI:1.03,2.44), peer‐reviewed (OR:1.93, 95%CI:1.38,2.71), good quality and studies which reported adjusted measure of effect (OR:1.30, 95%CI:1.10,1.50). Significant differences were found between ACEIs and ARBs with the latter being significantly associated with lower risk of acquiring COVID‐19 infection (OR:0.24; 95%CI: 0.17,0.34). In conclusion, high‐quality evidence exists for the effect of ACEIs/ARBs on some COVID‐19 clinical outcomes. For the first time, we provided evidence, albeit of low quality, on interclass differences between ACEIs and ARBs for some of the reported clinical outcomes.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".