Lyu, Mengzhe;
Torii, Ryo;
Liang, Ce;
Zhang, Xuehuan;
Wang, Xifu;
Li, Qiaoqiao;
Ventikos, Yiannis;
(2025)
Predictive computational framework to provide a digital twin for personalized cardiovascular medicine.
Communications Medicine
, 5
(1)
, Article 370. 10.1038/s43856-025-01055-7.
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Abstract
Background: 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. Methods: 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. Results: 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). Conclusions: 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.
| Type: | Article |
|---|---|
| Title: | Predictive computational framework to provide a digital twin for personalized cardiovascular medicine |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s43856-025-01055-7 |
| Publisher version: | https://doi.org/10.1038/s43856-025-01055-7 |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/bync-nd/4.0/. © The Author(s) 2025 |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Medicine, Research & Experimental, Research & Experimental Medicine, FRACTIONAL FLOW RESERVE, PERCUTANEOUS CORONARY INTERVENTION, ARTERY-DISEASE, STENT DESIGN, CONTACT, ANGIOGRAPHY, VALIDATION, SIMULATION, OUTCOMES, MESH |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215453 |
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