Predictive computational framework to provide a digital twin for personalized cardiovascular medicine

In percutaneous coronary intervention (PCI), the ability to predict post-PCI fractional flow reserve (FFR) and stented vessel informs procedural planning. However, highly precise and effective methods to quantitatively simulate coronary intervention are lacking. This study developed and validated a virtual coronary intervention (VCI) technique for non-invasive physiological and anatomical assessment of PCI.

In this study, patients with substantial lesions (pre-PCI CT-FFR of less than 0.80) were enrolled. VCI framework was used to predict vessel reshape and post-PCI CT-FFR. The accuracy of predicted post-VCI CT-FFR, luminal cross-sectional area (CSA) and centreline curvature was validated with post-PCI computed tomography (CT) angiography datasets.

Overall, 30 patients are initially screened; 21 meet the inclusion criteria, and 9 patients (9 vessels) are included in the final analysis. The average PCI-simulation time is 24.92 ± 1.00 s on a single processor. The calculated post-PCI CT-FFR is 0.92 ± 0.09, whereas the predicted post-VCI CT-FFR is 0.90 ± 0.08 (mean difference: -0.02 ± 0.05 FFR units; limits of agreement: -0.08 to 0.05). Morphologically, the predicted CSA is 16.36 ± 4.41 mm² and the post-CSA is 17.91 ± 4.84 mm² (mean difference: -1.55 ± 1.89 mm²; limits of agreement: -5.22 to 2.12). The predicted centreline curvature across the stented segment (including ~2 mm proximal and distal margins) is 0.15 ± 0.04 mm⁻¹, while the post-PCI centreline curvature is 0.17 ± 0.03 mm⁻¹ (mean difference: -0.02 ± 0.06 mm⁻¹; limits of agreement: -0.12 to 0.09).

The proposed VCI technique achieves non-invasive pre-procedural anatomical and physiological assessment of coronary intervention. The proposed model has the potential to optimize PCI pre-procedural planning and improve the safety and efficiency of PCI.