A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection.
Eur Radiol
; 33(12): 8682-8692, 2023 Dec.
Article
em En
| MEDLINE
| ID: mdl-37368110
ABSTRACT
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 prediction model provides accurate risk evaluation for vessel damage, improving the safety of the intervention procedure.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Aneurisma da Aorta Torácica
/
Implante de Prótese Vascular
/
Procedimentos Endovasculares
/
Dissecção Aórtica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article