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).
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.
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.
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.
• 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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Inferior vena cava area
Left pulmonary artery area
Area of pulmonary artery below anastomosis
Right pulmonary artery area
Superior vena cava area
Area under the curve
Diameter of anastomosis major axis
Diameter of anastomosis minor axis
Diameter of inferior vena cava major axis
Diameter of inferior vena cava minor axis
Diameter of pulmonary artery major axis below anastomosis
Diameter of superior vena cava major axis
Diameter of superior vena cava minor axis
Major aortopulmonary collateral arteries
Mean pulmonary arterial pressure
Inferior vena cava perimeter
Superior vena cava perimeter
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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).
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.
Written informed consent was waived from all subjects (patients) in this study.
Institutional Review Board approval was obtained.
• 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
- Heart diseases
- Multi-detector computed tomography
- Machine learning