Guideline-Directed Medical Therapy Assessment in Heart Failure Patients Undergoing Percutaneous Mitral Valve Repair
Bibliographic record
Abstract
AIMS: Achieving optimized guideline-directed medical therapy (GDMT) is recommended prior to transcatheter mitral valve edge-to-edge repair (M-TEER) for secondary mitral regurgitation (SMR). We aimed to propose and validate an easy-to-use score for assessing the quality of GDMT in patients with heart failure with reduced ejection fraction (HFrEF) undergoing M-TEER. METHODS AND RESULTS: Among the 1641 EuroSMR patients enrolled in the EuroSMR Registry who underwent M-TEER, a total of 1072 patients [median age 74, interquartile range (IQR) 67-79 years, 29% female] had complete data on GDMT and a left ventricular ejection fraction ≤ 40% and were included in the current study. We proposed a GDMT score that considers the dosage levels of three medication classes (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors, beta-blockers, and mineralocorticoid receptor antagonists), with a maximum score of 12 points indicating optimal GDMT. The primary outcome was all-cause mortality. The median GDMT score was 4 points (IQR 3-6). All three domains of the scoring system were associated with all-cause mortality (P < 0.05 for all). The overall GDMT score was associated with all-cause mortality (hazard ratio 0.90, 95% confidence interval 0.86-0.95 for each 1-point increase in the GDMT score). This association remained significant after adjusting for renal function and co-morbidities. CONCLUSIONS: This study demonstrates the utility of a simple GDMT scoring system for assessing the adequacy of GDMT in HFrEF patients with relevant SMR undergoing M-TEER. The GDMT score has potential applications in guiding the design of future clinical trials and aiding clinical decision-making processes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".