ArticlePediatric cardiology2026
Predictive Performance and Rater Reliability of CT-derived Virtual Reality Models for PDA Stent Planning in Patients with Ductal-Dependent Cyanotic Congenital Heart Disease.
Article in Pediatric cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Transcatheter patent ductus arteriosus (PDA) stenting is a key palliative strategy for ductal-dependent cyanotic congenital heart disease (CHD). This study assessed the feasibility of generating 3-dimensional (3D) virtual reality (VR) models, evaluated their predictive performance for predicting vascular access and stent size, and examined inter- and intra-rater reliability of VR ductal measurements. This retrospective study included 42 infants (January 2021-December 2024) with pre-procedural cardiac CT (CCT) who underwent PDA stenting for ductal-dependent pulmonary circulation. 3D VR PDA and PDA stent models in 4 mm length increments were generated using commercial software. A blinded interventional cardiologist predicted optimal vascular access and stent size. VR-based recommendations were compared to procedural outcomes. Two blinded operators independently measured total length (TL), straight length (SL), and ductal curvature index (CI) in VR; reliability was assessed by intraclass correlation coefficients (ICCs). VR models were successfully generated for all patients. The mean age and weight at procedure were 8.1 ± 4.7 days and 3.2 ± 0.6 kg, respectively. VR-predicted vascular access matched actual access in 36 (85.7%) cases. Stent size prediction matched deployed stents in 15 (35.7%) cases and was ± one size category in an additional 22 (52.4%) cases (88.1% total cases within one size category of deployed stent). Intra-rater ICCs were > 0.926; inter-rater ICCs were > 0.977. In conclusion, high-fidelity VR models are feasible for PDA stent planning, demonstrate high predictive performance for vascular access, and have excellent rater reliability for ductal measurements. This supports integrating VR modeling into pre-procedural workflows to improve planning.
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