Abstract 18268: When is Left Ventricular Function Too Poor for Successful Aortic Valve Replacement? Analysis of 3,075 Cases
Bibliographic record
Abstract
Introduction: Preoperative left ventricular (LV) dysfunction often improves after aortic valve replacement (AVR) due to relief of pressure or volume overload; however, LV function sometimes remains unimproved. We sought to determine the predictors of unimproved LV function after AVR, and its effects on clinical outcomes. Methods: A total of 3,075 patients who underwent surgical AVR (without concomitant mitral surgery) were longitudinally assessed in a follow-up clinic and with echocardiography (median follow-up 6.5yrs). At operation, mean age was 67.8±13.5yrs, 32% were female, and 29% had preoperative LV dysfunction (defined as: grade II, ejection fraction 35-49%; grade III, 20-34%; grade IV, Results: Actuarial survival at 15yrs was 62.0±2.4, 47.8±4.6, 40.7±5.7, and 34.9±8.0% ( P P =0.005) (Figure). Unimproved LV dysfunction was predicted by higher preoperative LV diastolic diameter (OR 1.9 per cm; P P =0.003), and older age (OR 1.3 per 10yrs; P =0.026). Notably, in patients with LV diastolic diameter≥55mm, the incidence of death or unimproved LV dysfunction at 2yrs was 55.1% ( P Conclusions: Unimproved LV dysfunction after AVR is a strong risk factor for decreased long-term survival. Older age, LV dilatation, and a low preoperative aortic valve gradient are significant risk factors for unimproved LV dysfunction and death, especially in those with LV diastolic diameter≥55mm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".