Back to Search
Start Over
Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.
- Source :
-
European radiology [Eur Radiol] 2020 Mar; Vol. 30 (3), pp. 1369-1377. Date of Electronic Publication: 2019 Nov 08. - Publication Year :
- 2020
-
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).<br />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.<br />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.<br />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.<br />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.
- Subjects :
- Adolescent
Algorithms
Bayes Theorem
Cardiac Catheterization
Child
Child, Preschool
Discriminant Analysis
Double Outlet Right Ventricle diagnostic imaging
Double Outlet Right Ventricle surgery
Female
Heart Septal Defects diagnostic imaging
Heart Septal Defects surgery
Humans
Infant
Logistic Models
Lung
Machine Learning
Male
Prognosis
Pulmonary Atresia diagnostic imaging
Pulmonary Atresia surgery
Retrospective Studies
Tomography, X-Ray Computed methods
Transposition of Great Vessels diagnostic imaging
Transposition of Great Vessels surgery
Tricuspid Atresia diagnostic imaging
Tricuspid Atresia surgery
Univentricular Heart diagnostic imaging
Univentricular Heart surgery
Young Adult
Blood Pressure
Fontan Procedure
Heart Defects, Congenital diagnostic imaging
Heart Defects, Congenital surgery
Pulmonary Artery diagnostic imaging
Support Vector Machine
Subjects
Details
- Language :
- English
- ISSN :
- 1432-1084
- Volume :
- 30
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- European radiology
- Publication Type :
- Academic Journal
- Accession number :
- 31705256
- Full Text :
- https://doi.org/10.1007/s00330-019-06502-3