PhD Scientific Days 2025

Poster Session I. - T: Cardiovascular Medicine and Research

CT-derived Radiomic Phenotype of Left Ventricle Myocardium Predicts Long-term Outcome in TAVR Patients

Előadó neve

Dr. Benyó Franciska

Neptun code

YB2GJE

Előadó munkahelye

Gottsegen György National Cardiovascular Institute

Előadó telefonszáma

+36307286687

Előadó e-mail címe

franciska.benyo@gokvi.hu

Az előadás címe

CT-derived Radiomic Phenotype of Left Ventricle Myocardium Predicts Long-term Outcome in TAVR Patients

Szerző(k) neve és munkahelye

Franciska Benyó MD¹, Shan-yu Lin¹, Fanni Mohácsi MD¹, Zsolt Szedlacsek MD¹, Anita Káposzta MD¹, Mónika Dénes MD, PhD¹, Márton Kolossváry MD, PhD¹

1: Gottsegen National Cardiovascular Institute

Bemutatás módja

Poszter

Szekció

Poster Session I. - T: Cardiovascular Medicine and Research

Language of the presentation

English

Preferred session

Cardiovascular Medicine and Research

Összefoglaló szövege

Introduction: Survival rates and quality of life after transcatheter aortic valve replacement (TAVR) procedure vary widely, yet factors predicting favorable outcomes remain unclear. Severe aortic stenosis can cause left ventricular hypertrophy and potential myocardial fibrosis due to the increased afterload. While TAVR reduces afterload and promotes myocardial remodeling, some maladaptive changes resulting from aortic stenosis may be irreversible, leading to poor outcomes. Currently, there are no established methods to asses the remodeling capacity of the left ventricle myocardium (LVM) and its correlation with long-term outcomes.

Aims: Radiomic analysis enables quantitative assessment of morphological and textural features within CT images. Our aim was to investigate the associations between the radiomic phenotype of the LVM on pre-TAVR CT scans and adverse outcomes following TAVR in a matched case-control study.

Methods: We identified 89 TAVR patients who died more than 30 days post-procedure and matched them to controls by age, sex, valve gradient, ejection fraction, diabetes and prior coronary intervention. Pre-TAVR LVM was segmented into 17 AHA regions using syngo.via software, then radiomic analysis was performed on the data. The association between radiomic features and all-cause mortality was evaluated using conditional logistic regression models correcting for LVM volume index. Multiple comparisons were adjusted for using the false discovery rate (FDR) method.

Results: We evaluated 178 individuals (mean age 81.3 ± 3.3 years; 44% female; 71% with high-gradient AS) over a median follow-up of 2.57 years. From 3,026 LVM segments, 258 radiomic features were analyzed. Conditional logistic regression, adjusted for LVM volume index, identified 58 features (22.5%) significantly associated with all-cause mortality—7 histogram-based, 50 texture-based, and 1 volume-based. The odds ratios for protective features ranged from 0.84 to 0.95, whereas those for adverse features spanned from 1.05 to 1.18 per population standard deviation.

Conclusions: Pre-TAVR CT morphological and textural LVM patterns may predict all-cause mortality. Precision LVM phenotyping may help identify high-risk patients and guide therapies post-TAVR.

University

Semmelweis University

Supervisor

Dr. Kolossváry Márton

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies before complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

9367

Start

17:48

End

17:54