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Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning

  • Vascular-Interventional
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To construct models for predicting reintervention after thoracic endovascular aortic repair (TEVAR) of Stanford type B aortic dissection (TBAD).

Methods

A total of 192 TBAD patients who underwent TEVAR were included; 68 (35.4%) had indications for reintervention. Clinical characteristics, aorta characteristics on pre- and postoperative computed tomography angiography, and aorta characteristics on immediate postoperative aortic digital subtraction angiography were collected. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify the risk factors for reintervention. Eight classifiers were used for modeling. The models were trained on 100 train-validation random splits with a ratio of 2:1. The performance was evaluated by the receiver operating characteristic curve.

Results

Seven predictors of reintervention were identified, including maximum false lumen diameter, aortic diameter measured at the level of approximately 15 mm distal to the left subclavian artery, aortic diameter measured at the level of the diaphragm, false lumen diameter measured at the level of the celiac artery, number of bare-metal and covered stents, number of bare-metal stents, and residual perfusion of the false lumen. Logistic regression (LR) yielded the highest performance, with an area under the curve of 0.802. A nomogram built for clinical use showed good calibration. The cutoff value for dividing patients into low- and high-risk subgroups was 0.413. Kaplan-Meier curves showed that the overall survival of high-risk patients was significantly shorter than that of low-risk patients (both p < 0.05).

Conclusion

Our nomogram could predict the reintervention after TEVAR in patients with TBAD, which may facilitate patient selection and surveillance strategies.

Key Points

• Seven risk factors of reintervention after TEVAR of TBAD were identified for modeling.

• Logistic regression performed best in predicting reintervention with an AUC of 0.802.

• Patients with a high risk of reintervention had shorter OS than those with a low risk.

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Abbreviations

AdaBoost:

Adaptive boosting

AUC:

Area under the curve

BPNN:

Back propagation neural networks

CI:

Confidence interval

CTA:

Computed tomography angiography

DSA:

Digital subtraction angiography

IQR:

Interquartile range

KNN:

K-nearest neighbor

k-SVM:

Kernel support vector machine

LASSO:

Least absolute shrinkage and selection operator

LR:

Linear regression

NB:

Naive Bayes

NPV:

Negative predictive value

OS:

Overall survival

PPV:

Positive predictive value

RF:

Random forest

RTAD:

Retrograde type A aortic dissection

TBAD:

Type B aortic dissection

TEVAR:

Thoracic endovascular aortic repair

XGBoost:

Extreme gradient boosting

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Funding

This work was supported by the National Key Research and Development Program of China (2018YFC1002600); the Science and Technology Planning Project of Guangdong Province, China (No. 2017A070701013, 2017B090904034, 2018B090944002, 2019B020230003); and the Guangdong Peak Project (DFJH201802).

Author information

Author notes

  1. Yuhao Dong and Lifeng Que contributed equally to this work.

Authors and Affiliations

  1. Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, Guangzhou, 510080, People’s Republic of China

    Yuhao Dong, Qianjun Jia & Meiping Huang

  2. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Lifeng Que, Yue Xi, Jinglei Li & Hui Liu

  3. Guangdong Provincial People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China

    Yue Xi

  4. Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Jian Zhuang

  5. Big Data Decision Institute, Jinan University, Guangzhou, Guangdong, China

    Weiqi Chen

Authors
  1. Yuhao Dong
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Corresponding authors

Correspondence to Weiqi Chen or Meiping Huang.

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Guarantor

The scientific guarantor of this publication is Meiping Huang.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Weiqi Chen performed the statistical analyses of this paper.

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Written informed consent was waived from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at single institution

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Dong, Y., Que, L., Jia, Q. et al. Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning. Eur Radiol 32, 355–367 (2022). https://doi.org/10.1007/s00330-021-07849-2

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  • DOI: https://doi.org/10.1007/s00330-021-07849-2

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