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- Title
Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.
- Authors
Huang, Lei; Li, Jiahua; Huang, Meiping; Zhuang, Jian; Yuan, Haiyun; Jia, Qianjun; Zeng, Dewen; Que, Lifeng; Xi, Yue; Lin, Jijin; Dong, Yuhao
- Abstract
<bold>Objectives: </bold>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).<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.<bold>Key Points: </bold>• 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.
- Subjects
BLOOD pressure; CARDIAC catheterization; HEART abnormalities; HEART septum abnormalities; TRANSPOSITION of great vessels; PULMONARY atresia; LUNGS; PULMONARY artery; CONGENITAL heart disease; DISCRIMINANT analysis; RETROSPECTIVE studies; PROGNOSIS; CARDIOPULMONARY bypass; LOGISTIC regression analysis; COMPUTED tomography; PROBABILITY theory; ALGORITHMS
- Publication
European Radiology, 2020, Vol 30, Issue 3, p1369
- ISSN
0938-7994
- Publication type
Journal Article
- DOI
10.1007/s00330-019-06502-3