Transcatheter Left Ventricular Restoration in Ischemic Heart Failure and Dilated Cardiomyopathy
Bibliographic record
Abstract
Ischemic heart disease is the leading global cause of death and frequently progresses to ischemic heart failure (IHF) after myocardial infarction through adverse left ventricular (LV)remodeling. Surgical LV restoration improves ventricular geometry and function but is limited by invasiveness and inconsistent outcomes. Transcatheter Left Ventricular Restoration (TLVR) has emerged as a minimally invasive alternative,employing devices such as Revivent TC, AccuCinch, and Parachute to reduce LV volume, wall stress, and improve LV ejection fraction (LVEF). While several studies have reported promising results, no prior meta-analysis has synthesized the evidence across devices. This meta-analysis followed PRISMA 2020 guidelines. PubMed, Embase, Scopus, and Cochrane Library were searched through July 2025. Eligible studies included adult patients with IHF or dilated cardiomyopathy undergoing TLVR and reporting outcomes including LVEF, LV end-diastolic volume (LVEDV), New York Heart Association (NYHA) class, or Kansas City Cardiomyopathy Questionnaire (KCCQ). Single-arm interventional and observational cohort studies were included. Data extraction was performed by four reviewers, with bias assessed using ROBINS-I and the Newcastle-Ottawa Scale. Pooled mean differences (MD) were calculated using a random-effects model (Hartung-Knapp-Sidik-Jonkman). Seventeen studies were included, with baseline LVEF 22.8%-38% and LVEDV 75-235 mL. TLVR significantly reduced LVEDV (MD -25.94 mL; p < 0.00001), increased LVEF (+6.69%; p < 0.00001), and improved the NYHA class (MD -0.73; p = 0.02). KCCQ scores improved in some studies but were not significant overall. Revivent TC and AccuCinch showed consistent benefits, whereas Parachute outcomes were more variable. This meta-analysis of TLVR demonstrates consistent improvements in LV remodeling, function, and symptoms. Nonetheless, high heterogeneity, small cohorts, and limited follow-up highlight the need for large randomized trials to establish durability, survival benefit, and refine patient selection.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".