Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models.
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...
| Published in: | European Radiology Vol. 30; no. 3; pp. 1369 - 1378 |
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| Main Authors: | , , , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148390817&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148390817 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: 2020 vid: 30 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148390817 148390817 NLM31705256 148390817 10.1007/s00330-019-06502-3 NLM31705256 148390817 ppf: 1369 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models. aug: au: Huang, Lei Li, Jiahua Huang, Meiping Zhuang, Jian Yuan, Haiyun Jia, Qianjun Zeng, Dewen Que, Lifeng Xi, Yue Lin, Jijin Dong, Yuhao affil: Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 510080, Guangzhou, People's Republic of China sug: subj: Pulmonary Artery Cardiopulmonary Bypass Blood Pressure Heart Defects, Congenital Surgery Heart Defects, Congenital Heart Septal Defects Surgery Male Child Heart Catheterization Transposition of Great Arteries Discriminant Analysis Pulmonary Atresia Transposition of Great Arteries Surgery Pulmonary Atresia Surgery Retrospective Design Infant Young Adult Probability Child, Preschool Heart Septal Defects Lung Logistic Regression Prognosis Female Tricuspid Atresia Tricuspid Atresia Surgery Tomography, X-Ray Computed Methods Algorithms Adolescence Human Child: 6-12 years Infant: 1-23 months Child, Preschool: 2-5 years Adolescent: 13-18 years Male Female ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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