Systematic Review Examining the Association Between Angiotensin Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Prescription and Abdominal Aortic Aneurysm Growth and Events
Bibliographic record
Abstract
Objective Whether angiotensin II blockade is an effective medical treatment for abdominal aortic aneurysms (AAAs) has not been established. This systematic review and meta-analysis aimed to determine the association between angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) prescription and AAA growth and events. Data sources MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library databases were searched from their inception to 4 January 2024, with no language restrictions. Review Methods The five databases were searched for randomised controlled trials (RCTs) and observational studies reporting the association between ACEi or ARB prescription and AAA growth, repair, or rupture. The primary outcome was AAA growth, with secondary outcomes of AAA rupture, AAA repair, and AAA related events (rupture and repair combined). Risk of bias was assessed using the Risk of Bias 2 tool for RCTs and with a modified Newcastle–Ottawa scale for observational studies. Certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Random effects models were used for meta-analyses. Results Eleven studies (two RCTs, eight observational studies, and one meta-analysis of individual patient data from seven populations) involving 58 022 patients were included. ACEi prescription was not associated with a statistically significant reduction in AAA growth (standard mean difference 0.01 mm/year, 95% confidence interval [CI] –0.26 – 0.28; p = .93; I 2 = 98%) or AAA repair (odds ratio [OR] 0.73, 95% CI 0.50 – 1.09; p = .65; I 2 = 61%), but was associated with a statistically significantly lower risk of AAA rupture (OR 0.87, 95% CI 0.81 – 0.93; p < .001; I 2 = 26%) and AAA related events (OR 0.82, 95% CI 0.72 – 0.95; p = .006; I 2 = 80%). ARB prescription was not associated with significantly reduced AAA growth or a lower risk of AAA related events. The two RCTs had a low risk of bias, with one observational study having low, seven moderate, and one high risk of bias. All of the findings had a very low certainty of evidence based on the GRADE analysis. Conclusion There was no association between ACEi or ARB prescription and AAA growth, but ACEi prescription was associated with a reduced risk of AAA rupture and AAA related events with very low certainty of evidence.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".