Forward Left Ventricular Ejection Fraction: A Simple Risk Marker in Patients With Primary Mitral Regurgitation
Bibliographic record
Abstract
Background The timing of mitral valve surgery in asymptomatic patients with primary mitral regurgitation ( MR ) is controversial. We hypothesized that the forward left ventricular (LV) ejection fraction ( LVEF ; ie, LV outflow tract stroke volume divided by LV end‐diastolic volume) is superior to the total LVEF to predict outcomes in MR . The objective of this study was to examine the association between echocardiographic parameters of MR severity and LV function and outcomes in patients with MR . Methods and Results The clinical and Doppler‐echocardiographic data of 278 patients with ≥mild MR and no class I indication of mitral valve surgery at baseline were retrospectively analyzed. The primary study end point was the composite of mitral valve surgery or death. During a mean follow‐up of 5.4±3.2 years, there were 147 (53%) events: 96 (35%) MV surgeries and 66 (24%) deaths. Total LVEF and global longitudinal strain were not associated with the occurrence of events, whereas forward LVEF ( P <0.0001) and LV end‐systolic diameter ( P =0.0003) were. After adjustment for age, sex, MR severity, Charlson probability, coronary artery disease, and atrial fibrillation, forward LVEF remained independently associated with the occurrence of events (adjusted hazard ratio: 1.09, [95% confidence interval]: 1.02–1.17 per 5% decrease; P =0.01), whereas LV end‐systolic diameter was not ( P =0.48). Conclusions The results of this study suggest that the forward LVEF may be superior to the total LVEF and LV end‐systolic diameter to predict outcomes in patients with primary MR . This simple and easily measurable parameter may be useful to improve risk stratification and select the best timing for intervention in patients with primary MR .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".