Abstract 14773: Automated Cardiovascular Calcium Burden: Unlocking the Future of TAVR Prognosis With Artificial Intelligence
Bibliographic record
Abstract
Background: Calcific valve disease is now understood as a dynamic inflammatory process that alters the tissue’s adaptive response to stress stimuli. Transcatheter aortic valve replacement (TAVR) is a minimally invasive treatment for severe aortic stenosis. Manual analysis of calcifications in pre-TAVR CT scans is time-consuming and subjective. Hypothesis: Deep learning (DL) models can accurately quantify cardiovascular calcification and predict post-procedural outcomes in TAVR patients. We aimed to develop such a model and investigate its correlation with long-term all-cause mortality in TAVR patients. Methods: This post-hoc analysis examined patients from the FRAILTY-AVR trial and the Royal Victoria Hospital TAVR registry. A 3D UNet-based DL pipeline was developed and trained to detect and quantify coronary artery calcification (CAC), aortic valve calcification (AVC), mitral annular calcification (MAC), and thoracic aorta calcification (TAC) based on pre-procedural CT images. Quantification involved segmentation and CT image analysis techniques. Cox regression analysis, adjusting for various covariates, evaluated the primary endpoint of all-cause mortality one year after TAVR. Results: The study included 585 TAVR patients (mean age: 82.5 years; 44% females) with a median follow-up of 506 days. At one year, 27.5%(161) reached the primary endpoint of all-cause mortality. Hazard ratios for MAC, AVC, TAC, and CAC were 1.26 (95% CI: 1.06-1.51, P=0.010), 0.99 (95% CI: 0.83-1.18, P=0.91), 1.07 (95% CI: 0.89-1.29, P=0.46), and 0.89 (95% CI: 0.72-1.09, P=0.26), respectively. Adjustments for covariates did not significantly change these hazard ratios. Mean calcification volumes were 770 mm3 (AVC), 737 mm3 (CAC), 3076 mm3 (TAC), and 629 mm3 (MAC). No substantial correlations were observed between the four calcifications or cardiovascular co-morbidities however, each score may yield meaningful information. Conclusions: High mitral annular calcification, quantified by our AI pipeline, was independently associated with increased one-year post-TAVR mortality. AI enables efficiency, objectivity, and improved insights, enhancing clinical decision-making and personalized care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".