Blood pressure reduction and clinical outcomes with angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers: protocol for a systematic review and meta-regression analysis
Bibliographic record
Abstract
BACKGROUND: Angiotensin-converting enzyme inhibitors (ACEis) and angiotensin II receptor blockers (ARBs) efficaciously reduce systolic blood pressure (BP), a well-established risk factor for myocardial infarction (MI). Both inhibit the renin-angiotensin system, albeit through different mechanisms, and produce similar reductions in BP. However, in parallel meta-analyses of ACEi and ARB trials, ACEis reduce risk of MI whereas ARBs do not-a phenomenon described as the 'ARB-MI paradox'. In addition, ACEis reduce all-cause mortality, whereas ARBs do not, which appears to be independent of BP lowering. The divergent cardiovascular effects of ACE inhibitors and ARBs, despite similar BP reductions, are counter-intuitive. This systematic review aims to ascertain the extent to which clinical outcomes in randomised trials of ACEi and ARBs are attributable to reductions in systolic BP. METHODS: A comprehensive search of bibliographic databases will be performed to identify all randomised studies of agents of the ACEi and ARB class. Placebo and active comparator-controlled studies that report clinical outcomes, with greater than 500 person-years of follow-up in each study arm, will be included. Two independent reviewers will screen study records against a priori-defined eligibility criteria and perform data extraction. The Cochrane Risk of Bias Tool will be applied to all included studies. Studies retracted subsequent to initial publication will be excluded. Primary outcomes of interest include MI and all-cause mortality; secondary outcomes include stroke, heart failure, revascularisation and cardiovascular mortality. Meta-regression will be performed, evaluating the relationship between attained reduction in systolic BP and relative risk of each outcome, stratified by drug class. Where a BP-dependent effect exists (two-tailed p value < 0.05), relative risks, standardised per 10 mmHg difference in BP, will be reported for each study outcome. Publication bias will be examined using Funnel plots, and calculation of Egger's statistic. DISCUSSION: This systematic review will provide a detailed synthesis of evidence regarding the relationship between BP reduction and clinical outcomes with ACEi and ARBs. Greater understanding of the dependency of the effect of each class on BP reduction will advance insight into the nature of the ARB-MI paradox and guide the future usage of these agents. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017072988.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.037 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".