Clinical Impact of Changes in Left Ventricular Function After Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: Our objectives were to identify correlates of mortality and congestive heart failure after aortic valve replacement (AVR) according to preoperative left ventricular (LV) function and to describe the incidence, time course, and correlates of LV recovery and mass regression postoperatively. METHODS AND RESULTS: A total of 3112 patients with AVR were assessed in a follow-up clinic with echocardiography (median follow-up, 6.0 years). At operation, their mean age was 67.8±13.4 years, one third were female, and 29% had LV dysfunction (ejection fraction <50%). In severe patients with severe aortic stenosis and LV dysfunction, transaortic valve mean pressure gradient <40 mm Hg, longer cardiopulmonary bypass duration, and prosthesis-patient mismatch (indexed effective orifice area ≤0.85 cm(2)/m(2)) were independent correlates of the composite outcome of death or congestive heart failure after AVR. In patients with severe aortic regurgitation and LV dysfunction, older age and higher preoperative LV mass were identified. LV recovery correlated with better survival and freedom from heart failure in patients with aortic stenosis. Maximum LV mass regression took 24 months in patients with aortic stenosis and nearly 5 years with aortic regurgitation; independent correlates included smaller LV end-systolic diameter in patients with aortic stenosis and low New York Heart Association class with aortic regurgitation. CONCLUSIONS: Incomplete LV recovery, prosthesis-patient mismatch, low transaortic valve pressure gradient, and higher LV mass are associated with increased mortality or heart failure after AVR in patients with LV dysfunction. Higher LV end-systolic diameter and symptoms correlate with less LV mass regression, which takes at least 2 years. These findings help surgeons and cardiologists refine the indications, timing, prognostication, and follow-up of patients before and after AVR.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".