Effect of opioids for breathlessness in heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Background For the treatment of breathlessness in heart failure (HF), most textbooks advocate the use of opioids. Yet, meta-analyses are lacking. Methods A systematic review was performed for randomised controlled trials (RCTs) assessing effects of opioids on breathlessness (primary outcome) in patients with HF. Key secondary outcomes were quality of life (QoL), mortality and adverse effects. Cochrane Central Register of Controlled Trials, MEDLINE and Embase were searched in July 2021. Risk of bias (RoB) and certainty of evidence were assessed by the Cochrane RoB 2 Tool and Grading of Recommendations Assessment, Development and Evaluation criteria, respectively. The random-effects model was used as primary analysis in all meta-analyses. Results After removal of duplicates, 1180 records were screened. We identified eight RCTs with 271 randomised patients. Seven RCTs could be included in the meta-analysis for the primary endpoint breathlessness with a standardised mean difference of 0.03 (95% CI −0.21 to 0.28). No study found statistically significant differences between the intervention and placebo. Several key secondary outcomes favoured placebo: risk ratio of 3.13 (95% CI 0.70 to 14.07) for nausea, 4.29 (95% CI 1.15 to 16.01) for vomiting, 4.77 (95% CI 1.98 to 11.53) for constipation and 4.42 (95% CI 0.79 to 24.87) for study withdrawal. All meta-analyses revealed low heterogeneity (I 2 in all these meta-analyses was <8%). Conclusion Opioids for treating breathlessness in HF are questionable and may only be the very last option if other options have failed or in case of an emergency. PROSPERO registration number CRD42021252201.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.044 |
| Bibliometrics | 0.010 | 0.008 |
| 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) | 0.004 | 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".