Efficacy and safety of radiofrequency ablation for hypertrophic obstructive cardiomyopathy: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Although radiofrequency ablation is widely used in the treatment of arrhythmias, its role in septal reduction therapy of hypertrophic obstructive cardiomyopathy (HOCM) is unclear. This meta-analysis aimed to assess the efficacy and safety of radiofrequency septal ablation for HOCM. HYPOTHESIS: Radiofrequency septal ablation is effective and safe for relieving obstruction and improving exercise capacity in patients with HOCM. METHODS: A systematic review of eligible studies that reported outcomes of patients with HOCM who underwent radiofrequency septal ablation was performed using PubMed, Embase, Cochrane, ProQuest, Scopus, ScienceDirect, and Web of Science database. Pooled estimates were calculated using random-effects meta-analysis. Methodological quality was assessed using the Newcastle-Ottawa scale. Publication bias and sensitivity analyses were also performed. RESULTS: Eight studies with 91 patients (mean follow-up 11.6 months) were included. The left ventricular outflow tract (LVOT) gradient at rest decreased significantly after radiofrequency septal ablation (pooled reduction: -58.8 mmHg; 95% confidence interval [CI] -64.3 to -53.5). A reduction was also found in the provoked LVOT gradient with a pooled reduction of -97.6 mmHg (95% CI: -124.4 to -87.1). An improvement of the New York Heart Association classification (mean: -1.4; 95% CI: -1.6 to -1.2) was found during follow-up. The change in septal thickness was minimal and not statistically significant. Two procedure-related deaths were documented, and complete heart block occurred in eight patients. CONCLUSIONS: Radiofrequency septal ablation is effective and safe for relieving LVOT obstruction and improving exercise capacity in patients with HOCM.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".