A CT-based predictive model for stent-induced vessel damage: application to type B aortic dissection.
Humans
Aortic Aneurysm, Thoracic
/ diagnostic imaging
Blood Vessel Prosthesis Implantation
/ adverse effects
Treatment Outcome
Postoperative Complications
/ etiology
Incidence
Stents
/ adverse effects
Aortic Dissection
/ diagnostic imaging
Endovascular Procedures
/ adverse effects
Risk Factors
Tomography, X-Ray Computed
/ adverse effects
Retrospective Studies
Blood Vessel Prosthesis
/ adverse effects
Aortic dissection
Computer simulation
Postoperative complications
Predictive medicine
Thoracic endovascular aortic repair
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
04
10
2022
accepted:
26
03
2023
revised:
14
03
2023
medline:
27
11
2023
pubmed:
27
6
2023
entrez:
27
6
2023
Statut:
ppublish
Résumé
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. 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. 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. 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. 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. • 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.
Identifiants
pubmed: 37368110
doi: 10.1007/s00330-023-09773-z
pii: 10.1007/s00330-023-09773-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8682-8692Subventions
Organisme : National Key R&D Program of China
ID : 2018AAA0102600
Organisme : Innovative Research Group Project of the National Natural Science Foundation of China
ID : 81970404
Organisme : Innovative Research Group Project of the National Natural Science Foundation of China
ID : 81770465
Organisme : Natural Science Foundation of Beijing Municipality
ID : L192010
Organisme : Natural Science Foundation of Beijing Municipality
ID : L192045
Organisme : Natural Science Foundation of Beijing Municipality
ID : Z190014
Organisme : Key R&D Program of Zhejiang Province
ID : 2019C03013
Informations de copyright
© 2023. The Author(s), under exclusive licence to European Society of Radiology.
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