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Identification of high-risk patients for development of type B aortic dissection based on novel morphological

Da Li1,2, Jiarong Wang3, Jichun Zhao3

  • 1Department of Applied Mechanics, Sichuan University, Chengdu, China.

Frontiers in Physiology
|February 23, 2023
PubMed

Insights

Predicting sporadic type B aortic dissection (TBAD) is challenging. New morphological parameters identified via CT angiography can help identify high-risk patients for TBAD, improving early detection and management.

Area of Science:

  • Cardiovascular Imaging
  • Thoracic Surgery
  • Radiology

Background:

  • Sporadic type B aortic dissection (TBAD) prediction remains a clinical challenge.
  • Accurate risk stratification is crucial for timely intervention and improved patient outcomes.
  • Morphological assessment via computed tomography angiography (CTA) offers potential for risk identification.

Purpose of the Study:

  • To identify high-risk patients for sporadic type B aortic dissection (TBAD) development.
  • To establish a novel prediction model based on specific morphological parameters.
  • To compare the predictive performance of the new model against existing methods.

Main Methods:

  • A propensity-score-matched case-control study utilizing CTA data from acute TBAD patients and controls.
  • Multivariate regression analysis to determine odds ratios (aOR) and confidence intervals (CI) for morphological parameters.
  • Evaluation of discriminant and reclassification abilities of the developed prediction model.

Main Results:

  • Key morphological predictors identified: degree of question mark, brachiocephalic trunk diameter and angle, aortic root diameter, and aortic width.
  • A novel prediction model demonstrated superior discriminant ability (c-index 0.78 vs. 0.67, p = .03).
  • The new model significantly improved reclassification of TBAD patients (NRI 0.16, p = .02).

Conclusions:

  • Morphological parameters from CTA, including aortic root and brachiocephalic trunk dimensions and angles, are significant predictors of TBAD.
  • A new prediction model incorporating these features enhances the identification of high-risk individuals for TBAD.
  • These findings support the use of detailed morphological analysis for proactive TBAD risk assessment.

Keywords:
aortic archaortic dissectionmorphologypredictorsstatistics

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