A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection

• Published in: European radiology 2023-12, Vol.33 (12), p.8682-8692
• Main Authors: Zhang, Xuehuan, Wang, Dianpeng, Zhang, Xuyang, Liang, Shichao, Wu, Ziheng, Wen, Zipeng, Ventikos, Yiannis, Xiong, Jiang, Chen, Duanduan
• Format: Article
• Language: English
• Subjects: Algorithms; Aorta; Aortic dissection; Blood vessels; Cardiovascular system; Damage; Datasets; Diagnostic Radiology; Dissection; Imaging; Implants; Internal Medicine; Interventional; Interventional Radiology; Mathematical models; Mechanical properties; Medicine; Medicine & Public Health; Morphology; Neuroradiology; Nomograms; Parameters; Prediction models; Predictions; Radiology; Risk assessment; Risk factors; Safety; Stents; Surgical implants; Thorax; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-09773-z

Objectives The distal stent-induced new entry (distal SINE) is a life-threatening device-related complication after thoracic endovascular aortic repair (TEVAR). However, risk factors for distal SINE are not fully determined, and prediction models are lacking. This study aimed to establish a predictive model for distal SINE based on the preoperative dataset. Methods Two hundred and six patients with Stanford type B aortic dissection (TBAD) that experienced TEVAR were involved in this study. Among them, thirty patients developed distal SINE. Pre-TEVAR morphological parameters were measured based on the CT-reconstructed configurations. Virtual post-TEVAR morphological and mechanical parameters were computed via the virtual stenting algorithm (VSA). Two predictive models (PM-1 and PM-2) were developed and presented as nomograms to help risk evaluation of distal SINE. The performance of the proposed predictive models was evaluated and internal validation was conducted. Results Machine-selected variables for PM-1 included key pre-TEVAR parameters, and those for PM-2 included key virtual post-TEVAR parameters. Both models showed good calibration in both development and validation subsamples, while PM-2 outperformed PM-1. The discrimination of PM-2 was better than PM-1 in the development subsample, with an optimism-corrected area under the curve (AUC) of 0.95 and 0.77, respectively. Application of PM-2 in the validation subsample presented good discrimination with an AUC of 0.9727. The decision curve demonstrated that PM-2 was clinically useful. Conclusion This study proposed a predictive model for distal SINE incorporating the CT-based VSA. This predictive model could efficiently predict the risk of distal SINE and thus might contribute to personalized intervention planning. Clinical relevance statement This study established a predictive model to evaluate the risk of distal SINE based on the pre-stenting CT dataset and planned device information. With an accurate VSA tool, the predictive model could help to improve the safety of the endovascular repair procedure. Key Points • Clinically useful prediction models for distal stent-induced new entry are still lacking, and the safety of the stent implantation is hard to guarantee. • Our proposed predictive tool based on a virtual stenting algorithm supports different stenting planning rehearsals and real-time risk evaluation, guiding clinicians to optimize the presurgical plan when necessary. • The established predic