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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".