Prognostic Value of Speckle-tracking Echocardiography in Assessing Outcomes of Transcatheter Edge-to-edge Repair for Mitral Regurgitation
Bibliographic record
Abstract
Mitral regurgitation (MR) is a valvular heart disease with high morbidity and mortality. Transcatheter edge-to-edge repair (TEER) presents a viable treatment option for severe MR in high-risk surgical patients. This review aims to assess the prognostic significance of speckle-tracking echocardiography (STE) in predicting outcomes after TEER and its potential role in patient management. A systematic literature review was done on PubMed for studies until November 2024. The search incorporated keywords of STE and TEER, including original research on STE's predictive value in TEER patients. Studies concerning surgical mitral valve repair or unrelated imaging techniques were excluded. Twenty-nine articles were included, indicating that STE metrics, especially global longitudinal strain (GLS), correlate with clinical outcomes like heart failure progression and mortality. Baseline GLS was determined as predictive of hospitalization and mortality, while post-TEER GLS improvements were associated with better functional capacity. Furthermore, left atrial function metrics were significant in predicting arrhythmia recurrence. This study highlights the utility of STE in predicting outcomes for TEER patients. While findings are encouraging, additional research is essential to elucidate the long-term effects of TEER on cardiac function, thereby enhancing patient selection and management approaches. This review highlights the relevance of STE in prognosticating outcomes for patients undergoing TEER. Further investigations are essential to clarify the long-term repercussions of TEER on cardiac functionality, thereby refining patient selection and management strategies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".