Mitral transcatheter edge-to-edge repair and outcomes according to baseline health status: the RESHAPE-HF2 trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Mitral transcatheter edge-to-edge repair (M-TEER) using the MitraClip device improves clinical outcomes in patients with moderate-to-severe ventricular secondary mitral regurgitation (vSMR) and heart failure (HF). This study evaluated whether the effects of M-TEER on clinical outcomes vary by baseline health status, as measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ), and assessed the impact of M-TEER on health status post-randomization. METHODS: The RESHAPE-HF2 trial included patients with symptomatic HF and moderate-to-severe vSMR (mean effective regurgitant orifice area .25 cm2; 14% >.40 cm2, 23% <.20 cm2). The impact of baseline KCCQ-clinical summary score (CSS) on the effect of M-TEER on clinical outcomes was assessed using Cox proportional hazards models. Changes post-randomization in health status and responder analyses were performed to assess the odds ratio (OR) of improvement and deterioration in KCCQ scores. RESULTS: Among 505 patients, M-TEER reduced cardiovascular death or HF hospitalization risk [hazard ratio (HR): .71 (.48-1.05), .50 (.29-.85), and .73 (.38-1.41)] across KCCQ-CSS tertiles of <38.9, 38.9-66.1, and >66.1, respectively (P-trend = .53). Similar results were seen for total HF hospitalization (P-trend = .48). M-TEER improved KCCQ-CSS, total symptom score, and overall summary score at 1, 6, 12, and 24 months compared to medical therapy alone (P < .05 at all time points). More patients in the M-TEER arm experienced a ≥5-point [OR 3.38 (2.09-5.45)], ≥10-point [OR 3.12 (1.93-5.02)], and ≥15-point [OR 3.25 (1.94-5.45)] improvement, and less patients had a ≥5-point deterioration [OR .34 (.19-.57)] in KCCQ-CSS at 6 months. Similar results were seen across other KCCQ domains and all time points. CONCLUSIONS: In patients with HF and moderate-to-severe vSMR, M-TEER showed a consistent trend towards a lower risk of HF hospitalization, with or without cardiovascular death, across all KCCQ-CSS tertiles and improved health status over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".