Abstract 10325: Feasibility and Predictive Utility of Three-Dimensional Myocardial Deformation Analysis From Multi-Phase Computed Tomography Angiography in Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Introduction : Multi-phase computed tomography angiography (CTA) for the pre-procedural planning of TAVR presents a unique opportunity to assess 3D myocardial biomechanics. Using a novel approach, we assessed the feasibility and predictive utility of 3D myocardial deformation analysis (3D-MDA) to deliver principal strain (PS) based markers of left ventricular (LV) health for the prediction of time to heart failure hospitalization or death following TAVR. Methods: 205 patients undergoing a pre-TAVR CTA and followed clinically for >6 months were identified. Whole-heart segmentation with 3D-mesh modelling was followed by 3D-MDA of the LV. 3D global LV minimum PS (minPS) was calculated for endocardial, epicardial and transmural layers. Cox regression models were performed to evaluate associations between 3D minPS and the composite outcome of all-cause mortality or heart failure hospitalization. Results: Of 205 patients, 196 (96%) had analyzable CTA data for 3D-MDA. Median (IQR) age was 85 (79.5-88) years (55% male) with median STS-PROM score 3.10 (2.10-4.55)% and median echocardiography LVEF 60 (55.9-65.0)%. Over 25 (11-36) months 55 patients (28%) experienced all-cause death or HF hospitalization. Patients with lower 3D minPS, below -23.7%, experienced a 3-fold increased risk of the primary outcome (p<0.001). Following adjustment for baseline characteristics, inclusive of STS and LVEF, 3D minPS remained independently associated with the primary outcome: endocardial 3D minPS providing highest prognostic value [HR (95% CI) of 1.09 per 1% change (1.04-1.15), p<0.001]. Conclusions: 3D-MDA of pre-TAVR multi-phase CTA is feasible and delivers principal-strain based markers strongly predictive of future clinical outcomes. The potential of this approach to optimize patient selection and post-procedural management requires future evaluation in a multi-centre setting.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".