High-dose versus low-dose angiotensin converting enzyme inhibitors in heart failure: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically review evidence comparing the effect of low-dose versus high-dose ACE inhibitors (ACEIs) on all-cause and cardiovascular mortality and hospitalisation, functional capacity and side effects in patients with heart failure (HF). METHODS: We searched PubMed, Embase, Cochrane CENTRAL and LILACS up to January 2019. We included randomised controlled trials (RCTs) comparing low-dose versus high-dose ACEIs in adults with HF with reduced left ventricular ejection fraction (HFrEF). Study selection and data extraction were performed by two independent reviewers. Risk of bias was assessed with RoB 2.0, and quality of evidence with Grading of Recommendations Assessment, Development and Evaluation (GRADE). We conducted random effects meta-analysis and trial sequential analysis. RESULTS: We included eight RCTs (5829 patients with HF). In comparison with low-dose ACEIs, high-dose ACEIs showed a non-significant effect on all-cause mortality (8 RCTs, n=5828, relative risk (RR) 0.95, 95% CI 0.88 to 1.02; moderate quality of evidence), cardiovascular mortality (6 RCTs, n=4048, RR 0.93, 95% CI 0.85 to 1.01; moderate quality of evidence), all-cause hospitalisation (5 RCTs, n=5394, RR 0.95, 95% CI 0.82 to 1.10; moderate quality of evidence) and cardiovascular hospitalisation (4 RCTs, n=5242, RR 0.98, 95% CI 0.83 to 1.17; low quality of evidence). High-dose ACEI increased functional capacity (4 studies, n=555, standardised mean difference 0.38, 95% CI 0.20 to 0.55; low quality of evidence) and the risk of hypotension (4 RCTs, n=3783, RR 1.64, 95% CI 1.30 to 2.05; moderate quality of evidence). High-dose ACEI had no effect on dizziness (3 RCTs, n=4994, RR 1.37, 95% CI 0.97 to 1.93; low quality of evidence), but decreased the risk of cough (4 RCTs, n=5146, RR 0.85, 95% CI 0.73 to 0.98; moderate quality of evidence). CONCLUSIONS: The magnitude of benefit of using high dose versus low to intermediate doses of ACEIs might be less than traditionally suggested in clinical guidelines. These findings might help clinicians address the complex task of HF management in a more rational and timely fashion, saving efforts to implement strategies with the greatest net clinical benefit.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.028 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".