Angiotensin‐converting enzyme inhibitor induced cough compared with placebo, and other antihypertensives: A systematic review, and network meta‐analysis
Bibliographic record
Abstract
Studies have shown that angiotensin converting enzyme inhibitors (ACEIs) are superior in primary and secondary prevention for cardiac mortality and morbidity to angiotensin receptor blocker (ARBs). One of the common side effects from ACEI is dry cough. The aims of this systematic review, and network meta-analysis are to rank the risk of cough induced by different ACEIs and between ACEI and placebo, ARB or calcium channel blockers (CCB). We performed a systematic review, and network meta-analysis of randomized controlled trials to rank the risk of cough induced by each ACEI and between ACEI and placebo, ARB or CCB. A total of 135 RCTs with 45,420 patients treated with eleven ACEIs were included in the analyses. The pooled estimated relative risk (RR) between ACEI and placebo was 2.21 (95% CI: 2.05-2.39). ACEI had more incidences of cough than ARB (RR 3.2; 95% CI: 2.91, 3.51), and pooled estimated of RR between ACEI and CCB was 5.30 (95% CI: 4.32-6.50) Moexipril ranked as number one for inducing cough (SUCRA 80.4%) and spirapril ranked the least (SUCRA 12.3%). The order for the rest of the ACEIs are as follows: ramipril (SUCRA 76.4%), fosinopril (SUCRA 72.5%), lisinopril (SUCRA 64.7%), benazepril (SUCRA 58.6%), quinapril (SUCRA 56.5%), perindopril (SUCRA 54.1%), enalapril (SUCRA 49.7%), trandolapril (SUCRA 44.6%) and, captopril (SUCRA 13.7%). All ACEI has the similar risk of developing a cough. ACEI should be avoided in patients who have risk of developing cough, and an ARB or CCB is an alternative based on the patient's comorbidity.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 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 | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".