Impact of coronary artery disease on left ventricular ejection fraction recovery following transcatheter aortic valve implantation
Bibliographic record
Abstract
OBJECTIVES: The objective of the present study was to assess if the presence and severity of CAD is associated with decreased LVEF recovery after TAVI. BACKGROUND: Coronary artery disease (CAD) and low left ventricular ejection fraction (LVEF) are common findings in patients undergoing transcatheter aortic valve implantation (TAVI). The impact of CAD on LVEF recovery after TAVI has not been specifically evaluated. METHODS: All patients with LVEF≤50% who underwent TAVI between March 2006 and May 2012 were included in the study. The presence and severity of coronary artery disease was measured using the Duke Myocardial Jeopardy Score (DMJS). A DMJS = 0 corresponds to patients without CAD or complete revascularization and a DMJS > 0 to those with incomplete revascularization. LVEF recovery was assessed by transthoracic echocardiography, measuring the change in LVEF from baseline to 3-months post-TAVI. Myocardial viability was evaluated in a subgroup of patients using cardiac magnetic resonance (CMR) imaging pre-TAVI. RESULTS: Fifty-six patients were included in the study. Twenty-eight patients (50%) had a DMJS > 0. At 3 months, patients with incomplete revascularization (DMJS > 0) demonstrated less LVEF recovery post-TAVI (2.0 ± 9.2% versus 11.7 ± 8.9% if DMJS = 0; P = 0.001). On multivariate analysis, DMJS and presence of significant delayed-enhancement were found to be independent predictors of LVEF recovery. Patients with incomplete revascularization exhibited a worse prognosis with higher mortality at 30-days (22.2% versus 0% if DMJS = 0; P = 0.010) and 1-year (25.9% versus 3.5% if DMJS = 0; P = 0.019). CONCLUSIONS: The present study demonstrates an independent association between incomplete revascularization and decreased LVEF recovery in patients with left ventricular dysfunction undergoing TAVI for severe aortic stenosis.
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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) | 0.000 | 0.019 |
| 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 | 0.000 | 0.000 |
| 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 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".