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

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.

Effect of Vessel Tortuosity on Stress Concentration at the Distal Stent-Vessel Interface: Possible Link With New Entry Formation Through Biomechanical Simulation

A computational approach is used to investigate potential risk factors for distal stent graft-induced new entry (dSINE) in aortic dissection (AD) patients. Patient-specific simulations were performed based on computed tomography images acquired from six AD patients (three dSINE and three non-dSINE) to analyze the correlation between anatomical characteristics and stress/strain distributions. Sensitivity analysis was carried out using idealized models to independently assess the effect of stent graft length, stent tortuosity and wedge apposition angle at the landing zone on key biomechanical variables. Mismatch of biomechanical properties between the stented and nonstented regions led to high stress at the distal stent graft-vessel interface in all patients, as well as shear strain in the neighboring region, which coincides with the location of tear formation. Stress was observed to increase with the increase of stent tortuosity (from 263 kPa at a tortuosity angle of 50 deg to 313 kPa at 30 deg). It was further amplified by stent graft landing at the inflection point of a curve. Malapposition of the stent graft led to an asymmetrical segment within the aorta, therefore changing the location and magnitude of the maximum von Mises stress substantially (up to +25.9% with a +25 deg change in the distal wedge apposition angle). In conclusion, stent tortuosity and wedge apposition angle serve as important risk predictors for dSINE formation in AD patients.