IMPACT OF ANGIOTENSIN-CONVERTING ENZYME INHIBITORS AND ANGIOTENSIN RECEPTOR BLOCKERS ON CORONARY HEART DISEASE EVENTS: A SYSTEMATIC REVIEW AND META-ANALYSIS OF 299,871 PATIENTS
Bibliographic record
Abstract
Objective: To evaluate the effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) on risk of myocardial infarction (MI), angina & heart failure (HF) in patients with or at high-risk of cardiovascular disease (CVD). Design and method: A meta-analysis of randomized-controlled trials was performed. Bibliographic databases were searched until 31 July 2019 to identify all trials of ACEIs & ARBs versus control (placebo or active) & supported with head-to-head trials. Trials with at least 100 participants & at least one year's follow-up were eligible. Studies were excluded if they were redacted or combined ACEIs with ARBs. Outcomes were MI, angina pectoris & HF. Dichotomous data was analysed using risk ratio (RR) measure and its 95% confidence interval (CI) with random-effects model. A random-effects meta-regression analysis was performed to explore role systolic blood pressure (SBP) reduction achieved. Results: We identified 32 trials of ACEIs, 38 of ARBs compared with control & 8 direct comparison trials. Altogether, trials enrolled 299,871 patient-years of follow-up. Compared with control, ACEIs had a 16% lower MI risk (RR, 0.84; 95% CI, 0.79–0.90; p < 0.00001) & 17% lower HF (RR, 0.83; 95% CI, 0.76–0.92; p = 0.0003); while no such benefit was seen for angina (RR;1.02; 95% CI, 0.94–1.11; p = 0.63). ARBs was reduced risk of HF by 14% (RR, 0.86; 95% CI 0.81–0.91; p < 0.00001). While, no benefit was appeared with MI risk (RR,0.97; 95% CI 0.89–1.06; p = 0.55) & angina (RR, 0.99; 95% CI 0.88–1.11; p = 0.87). Trials comparing ARBs with ACEIs revealed no difference in outcomes. The meta-regression suggested that independently of BP reduction, ACEIs had a 11% lower MI (RR,0.89; 95% CI 0.81–0.98; p = 0.02) & ARBs provide a 15% reduction in HF (RR, 0.85; 95% CI 0.77–0.93; p = 0.001). Whereas, prevention of HF by ACEIs was explained mainly by SBP reduction achieved (p = 0.01). Conclusions: In patients with or at high-risk of CVD, ARBs and ACEIs reduced risk of HF. However, they did not appear to be case for angina. Moreover, ACEIs result in a further reduction of MI whereas ARBs had no such benefit. However, evidence from direct comparison trials suggests similar effects on all 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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".