Angiotensin-Converting Enzyme Inhibitor Induced Cough in Chinese Patients: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
PURPOSE: To determine the risk of angiotensin converting enzyme inhibitor (ACEI)-induced cough compared to non-ACEI cough among Chinese patients. METHODS: A comprehensive search was conducted including randomized controlled trials, case-control studies and observational studies that compared ACEI treatment with control treatment in MEDLINE, EMBASE, CINAHL, Scopus, Google Scholar and ProQuest Dissertations & Theses Global. The studies which contained: Chinese population, ACEI, non-ACEI, and indications for the treatment of ACEI were included. The pooled risk ratios (RRs) and 95% confidence intervals (CIs) were calculated to compare the relative risk of cough between ACEIs and non-ACEI drugs based on the events of reported cough in each study. RESULTS: Eleven randomized controlled trials were included with a total of 1815 patients. The total number of cough events in ACEI treatment was 101 in 930 patients (11%) and 20 in 885 patients (2%) in the Non-ACEI treatment. The pooled RR was 5.16 (95% CI: 3.39-7.85) under fixed model. The discontinuation number of single ACEI treatment due to coughing side effect was 21 and the withdrawal rate was 4.13%. Only two patients discontinued non-ACEIs treatment due to the intolerable cough and the withdrawal rate was 0.34%. The overall RR of withdrawal related to cough was 7.06 (95% CI: 2.49-20.04). CONCLUSIONS: The pooled risk of the incidence of ACEI-induced cough was about five times higher than that of non-ACEI-induced cough in Chinese population. The risk of withdrawal events related to cough in the single ACEI treatment was seven times of that in the non-ACEI treatment.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| 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".