Epicardial adipose tissue, cardiac damage, and mortality in patients undergoing TAVR for aortic stenosis
Bibliographic record
Abstract
Abstract Computed tomography (CT)-derived Epicardial Adipose Tissue (EAT) is linked to cardiovascular disease outcomes. However, its role in patients undergoing Transcatheter Aortic Valve Replacement (TAVR) and the interplay with aortic stenosis (AS) cardiac damage (CD) remains unexplored. We aim to investigate the relationship between EAT characteristics, AS CD, and all-cause mortality. We retrospectively included consecutive patients who underwent CT-TAVR followed by TAVR. EAT volume and density were estimated using a deep-learning platform and CD was assessed using echocardiography. Patients were classified according to low/high EAT volume and density. All-cause mortality at 4 years was compared using Kaplan-Meier and Cox regression analyses. A total of 666 patients (median age 81 [74–86] years; 54% female) were included. After a median follow-up of 1.28 (IQR 0.53–2.57) years, 11.7% ( n = 77) of patients died. The EAT volume ( p = 0.017) decreased, and density increased ( p < 0.001) with worsening AS CD. Patients with low EAT volume (< 49cm 3 ) and high density (≥-86 HU) had higher all-cause mortality (log-rank p = 0.02 and p = 0.01, respectively), even when adjusted for age, sex, and clinical characteristics (HR 1.71, p = 0.02 and HR 1.73, p = 0.03, respectively). When CD was added to the model, low EAT volume (HR 1.67 p = 0.03) and CD stages 3 and 4 (HR 3.14, p = 0.03) remained associated with all-cause mortality. In patients with AS undergoing TAVR, CT-derived low EAT volume, and high density were independently associated with increased 4-year mortality and worse CD stage. Only EAT volume remained associated when adjusted for CD. Graphical Abstract
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".