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

Xuehuan Zhang1, Dianpeng Wang2, Xuyang Zhang1

  • 1School of Life Science, Beijing Institute of Technology, Beijing, China.

European Radiology
|June 27, 2023
PubMed
Summary

This study developed a predictive model to assess the risk of distal stent-induced new entry (distal SINE) after thoracic endovascular aortic repair. The model, utilizing virtual stenting, aids in personalized planning and improves procedural safety.

Keywords:
Aortic dissectionComputer simulationPostoperative complicationsPredictive medicineThoracic endovascular aortic repair

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Area of Science:

  • Cardiovascular Surgery
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Distal stent-induced new entry (distal SINE) is a serious complication of thoracic endovascular aortic repair (TEVAR).
  • Predictive models for distal SINE are currently lacking, hindering personalized risk assessment.
  • Understanding preoperative risk factors is crucial for improving TEVAR outcomes.

Purpose of the Study:

  • To establish a predictive model for distal SINE using preoperative data.
  • To evaluate the efficacy of a virtual stenting algorithm (VSA) in predicting distal SINE.
  • To provide a tool for personalized intervention planning in TEVAR.

Main Methods:

  • A cohort of 206 patients with Stanford type B aortic dissection undergoing TEVAR was analyzed.
  • Pre-TEVAR morphological parameters were measured from CT scans.
  • Virtual post-TEVAR parameters were computed using VSA to develop two predictive models (PM-1 and PM-2).

Main Results:

  • PM-2, incorporating virtual post-TEVAR parameters, demonstrated superior performance over PM-1.
  • PM-2 showed excellent discrimination in both development (AUC=0.95) and validation (AUC=0.9727) cohorts.
  • The decision curve analysis confirmed the clinical utility of PM-2.

Conclusions:

  • A CT-based predictive model incorporating VSA was developed for distal SINE risk.
  • This model can efficiently predict distal SINE risk, aiding personalized intervention planning.
  • The predictive model enhances the safety of endovascular repair procedures.