目的:本研究旨在开发基于术前心脏计算机断层扫描(CT)的非侵入性机器学习分类器,用于预测平均肺动脉压(mPAP)>15mmHg的低风险和高风险Glenn分流后患者。
方法:这项回顾性研究纳入了96例功能性单心室患者,这些患者在2009年11月1日至7月期间接受了双向Glenn手术,31,2017.所有患者均接受术后CT检查,然后是心脏导管插入术.总的来说,从每位患者的心脏CT图像中手动提取23个形态学参数。采用Mann-WhitneyU或卡方检验选择最显著的预测因子。六种机器学习算法,包括逻辑回归,天真的贝叶斯,随机森林(RF),线性判别分析,支持向量机,和K最近邻用于建模。这些算法在100个训练验证随机分裂上以3:1的比例独立训练。通过曲线下面积(AUC)评估其平均性能,准确度,灵敏度,和特异性。
结果:选择7个CT形态学参数进行建模。RF获得最佳性能,平均AUC为0.840(置信区间[CI]0.832-0.850)和0.787(95%CI0.780-0.794);灵敏度为0.815(95%CI0.797-0.833)和0.778(95%CI0.767-0.788),在训练和验证队列中,特异性为0.766(95%CI0.748-0.785)和0.746(95%CI0.735-0.757);准确性为0.782(95%CI0.771-0.793)和0.756(95%CI0.748-0.764),分别。
结论:基于CT的RF模型在mPAP的预测中显示出良好的性能,对于怀疑mPAP>15mmHg的Glenn分流后患者,这可能会减少右心导管插入的需要.
结论:•从心脏计算机断层扫描图像中手动提取23个候选描述符,并选择其中的七个进行后续建模。•随机森林模型在所有方法中呈现肺压的最佳预测性能。•基于计算机断层扫描的机器学习模型可以无创地预测Glenn分流术后肺压。
OBJECTIVE: 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.
CONCLUSIONS: • 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.