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Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

  • Cardiac
  • Published:
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Abstract

Objectives

This study aimed to develop non-invasive machine learning classifiers for predicting post–Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure (mPAP) > 15 mmHg based on preoperative cardiac computed tomography (CT).

Methods

This retrospective study included 96 patients with functional single ventricle who underwent a bidirectional Glenn procedure between November 1, 2009, and July, 31, 2017. All patients underwent post-procedure CT, followed by cardiac catheterization. Overall, 23 morphologic parameters were manually extracted from cardiac CT images for each patient. The Mann-Whitney U or chi-square test was applied to select the most significant predictors. Six machine learning algorithms including logistic regression, Naive Bayes, random forest (RF), linear discriminant analysis, support vector machine, and K-nearest neighbor were used for modeling. These algorithms were independently trained on 100 train-validation random splits with a 3:1 ratio. Their average performance was evaluated by area under the curve (AUC), accuracy, sensitivity, and specificity.

Results

Seven CT morphologic parameters were selected for modeling. RF obtained the best performance, with mean AUC of 0.840 (confidence interval [CI] 0.832–0.850) and 0.787 (95% CI 0.780–0.794); sensitivity of 0.815 (95% CI 0.797–0.833) and 0.778 (95% CI 0.767–0.788), specificity of 0.766 (95% CI 0.748–0.785) and 0.746 (95% CI 0.735–0.757); and accuracy of 0.782 (95% CI 0.771–0.793) and 0.756 (95% CI 0.748–0.764) in the training and validation cohorts, respectively.

Conclusions

The CT-based RF model demonstrates a good performance in the prediction of mPAP, which may reduce the need for right heart catheterization in post–Glenn shunt patients with suspected mPAP > 15 mmHg.

Key Points

• Twenty-three candidate descriptors were manually extracted from cardiac computed tomography images, and seven of them were selected for subsequent modeling.

• The random forest model presents the best predictive performance for pulmonary pressure among all methods.

• The computed tomography–based machine learning model could predict post–Glenn shunt pulmonary pressure non-invasively.

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Abbreviations

AAN:

Anastomosis area

AIVC:

Inferior vena cava area

ALPA:

Left pulmonary artery area

APAbAN:

Area of pulmonary artery below anastomosis

ARPA:

Right pulmonary artery area

ASVC:

Superior vena cava area

AUC:

Area under the curve

CI:

Confidence interval

CT:

Computed tomography

DANMA:

Diameter of anastomosis major axis

DANMI:

Diameter of anastomosis minor axis

DIVCMA:

Diameter of inferior vena cava major axis

DIVCMI:

Diameter of inferior vena cava minor axis

DPAMAbAN:

Diameter of pulmonary artery major axis below anastomosis

DSVCMA:

Diameter of superior vena cava major axis

DSVCMI:

Diameter of superior vena cava minor axis

MAPCAs:

Major aortopulmonary collateral arteries

mPAP:

Mean pulmonary arterial pressure

PAN:

Anastomosis perimeter

PIVC:

Inferior vena cava perimeter

PSVC:

Superior vena cava perimeter

RF:

Random forest

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Funding

This study was supported by the Key Program of Union of National Natural Science Foundation of China-Guangdong Province (U1401255), the Natural Science Foundation of Guangdong Province (2018A030313785), the Science and Technology Planning Project of Guangdong Province (2019B020230003, 2018B090944002, 2017A070701013, 2017B090904034, and 2017B030314109), the National key Research and Development Program (2018YFC1002600), and Guangdong peak project (DFJH201802).

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Author notes

  1. Lei Huang and Jiahua Li contributed equally to this work.

Authors and Affiliations

  1. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, People’s Republic of China

    Lei Huang & Jijin Lin

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China

    Lei Huang & Jijin Lin

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

    Jiahua Li, Lifeng Que & Yue Xi

  4. 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, Guangzhou, People’s Republic of China

    Meiping Huang, Haiyun Yuan, Qianjun Jia & Yuhao Dong

  5. Department of Cardiac 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, People’s Republic of China

    Jian Zhuang

  6. Huazhong University of Science and Technology, Wuhan, People’s Republic of China

    Dewen Zeng

Authors
  1. Lei Huang
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  11. Yuhao Dong
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Corresponding authors

Correspondence to Jijin Lin or Yuhao Dong.

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Guarantor

The scientific guarantor of this publication is Yuhao Dong.

Conflict of interest

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

The author Dewen Zeng did statistical analyses.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic study or prognostic

• Performed at one institution

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Huang, L., Li, J., Huang, M. et al. Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models. Eur Radiol 30, 1369–1377 (2020). https://doi.org/10.1007/s00330-019-06502-3

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  • DOI: https://doi.org/10.1007/s00330-019-06502-3

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