A STUDY assessing coronary proximity in patients undergoing transcatheter pulmonary valve replacement (TPVR) with self-expanding valves (SEV) has revealed dynamic movement in the area of the right ventricular outflow tract (RVOT) closest to the left coronary artery (LCA), with specific implications for patients diagnosed with pulmonary stenosis or atresia.
Transcatheter pulmonary valve replacement using self-expanding valves is a critical intervention for patients with congenital heart defects, but the risk of coronary artery compression during the procedure remains poorly understood. Coronary compression can lead to severe complications, yet current screening methods lack precision in predicting anatomical interactions between the RVOT and coronary arteries. This retrospective cohort study aimed to evaluate these spatial relationships using advanced imaging-derived modelling to improve procedural planning and patient safety.
The study analysed 42 patients evaluated for SEV-TPVR, of whom 83% (n=35) received either Harmony (n=24) or Alterra (n=11) valves. The cohort had a median age of 22.9 years (range: 12–60), with 76% diagnosed with tetralogy of Fallot (TOF). Machine learning was used to create CT-derived 3D segmentations of the RVOT and coronary arteries, generating 2D maps to measure distances between these structures during systole and diastole. While no significant difference in RVOT-to-LCA distance was observed between cardiac phases (p=0.31), the RVOT region nearest to the LCA shifted proximally by 11 mm (IQR: 5.6–19.9) during systole. Post-procedural CT scans in eight patients showed no significant changes in RVOT-LCA relationships after TPVR. Notably, pulmonary stenosis/atresia patients exhibited smaller median RVOT-to-LCA distances (1.2 mm vs. 2.1 mm in TOF patients, p=0.185), though this did not reach statistical significance.
These findings highlight the importance of accounting for dynamic anatomical changes in the RVOT-LCA interface during TPVR planning, particularly in patients with pulmonary stenosis/atresia. For clinical practice, integrating advanced imaging techniques could enhance pre-procedural risk assessment and reduce complications. Future studies should validate these results in larger cohorts and explore real-time imaging solutions to optimise valve deployment. Additionally, refining machine learning models to predict high-risk anatomical variations could further improve patient outcomes in this vulnerable population.
Katrina Thornber, EMJ
Reference
Barak‐Corren Y. Image‐derived modeling to assess coronary proximity in patients undergoing transcatheter pulmonary valve replacement with self‐expanding valves. Catheterization and Cardiovascular Interventions. 2025. DOI:10.1002/ccd.31469.
