Registries on Transcatheter Edge-To-Edge Repair in Heart Failure: Current Evidence and Future Perspectives
Bibliographic record
Abstract
AIMS: Secondary mitral regurgitation (SMR) and tricuspid regurgitation (TR) are the most common valvular heart diseases in patients with heart failure (HF). Transcatheter edge-to-edge repair (TEER) devices designed for treating MR and TR have been successfully tested in randomized controlled trials, but methodological issues have often challenged their interpretation. This manuscript aimed to provide an overview of TEER registries on SMR and TR in HF, highlighting their key features, describing clinical characteristics and outcomes of patients receiving these devices, and exploring the available data limitations. METHODS AND RESULTS: PubMed, Web of Science, and EMBASE were searched for registries reporting on TEER in SMR or TR. Registries were excluded if single-centre and with <100 patients. Twenty-six registries (46% prospective, 12% ongoing), including a total cohort of 18 925 patients, were retrieved for TEER in SMR, and six registries (50% retrospective, 33% ongoing) reported on the use of TEER for TR in a total cohort of 1412 patients. Limited geographical representativity outside North America and Europe, high number of missing values, and inconsistency in data reporting were the main existing evidence limitations. CONCLUSION: Registries on TEER represent a key data source in a setting where it is difficult to conduct randomized controlled trials. However, limitations in design, patient characterization, and outcomes reporting restrain their use. A novel conceptual framework for future prospective TEER registries, as proposed in this document, might inform current practice, address relevant clinical questions and future trial design.
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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 | 0.092 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".