PDF(Pubmed)
Abstract：
Background Congenital heart disease (CHD) is a structural deformity of the heart present at birth. Pulmonary hypertension (PH) may arise from increased blood flow to the lungs, persistent pulmonary arterial pressure elevation, or the use of cardiopulmonary bypass (CPB) during surgical repair. Inhaled nitric oxide (iNO) selectively reduces high blood pressure in the pulmonary vessels without lowering systemic blood pressure, making it useful for treating children with postoperative PH due to heart disease. However, reducing or stopping iNO can exacerbate postoperative PH and hypoxemia, necessitating long-term administration and careful tapering. This study aimed to evaluate, using machine learning (ML), factors that predict the need for long-term iNO administration after open heart surgery in CHD patients in the postoperative ICU, primarily for PH management. Methods We used an ML approach to establish an algorithm to predict \'patients with long-term use of iNO\' and validate its accuracy in 34 pediatric postoperative open heart surgery patients who survived and were discharged from the ICU at Kagoshima University Hospital between April 2016 and March 2019. All patients were started on iNO therapy upon ICU admission. Overall, 16 features reflecting patient and surgical characteristics were utilized to predict the patients who needed iNO for over 168 hours using ML analysis with AutoGluon. The dataset was randomly classified into training and test cohorts, comprising 80% and 20% of the data, respectively. In the training cohort, the ML model was constructed using the important features selected by the decrease in Gini impurity and a synthetic oversampling technique. In the testing cohort, the prediction performance of the ML model was evaluated by calculating the area under the receiver operating characteristics curve (AUC) and accuracy. Results Among 28 patients in the training cohort, five needed iNO for over 168 hours; among six patients in the testing cohort, one needed iNO for over 168 hours. CPB, aortic clamp time, in-out balance, and lactate were the four most important features for predicting the need for iNO for over 168 hours. In the training cohorts, the ML model achieved perfect classification with an AUC of 1.00. In the testing cohort, the ML model also achieved perfect classification with an AUC of 1.00 and an accuracy of 1.00. Conclusion The ML approach identified that four factors (CPB, in-out balance, aortic cross-clamp time, and lactate) are strongly associated with the need for long-term iNO administration after open heart surgery in CHD patients. By understanding the outcomes of this study, we can more effectively manage iNO administration in postoperative open heart surgery in CHD patients with PH, potentially preventing the recurrence of postoperative PH and hypoxemia, thereby contributing to safer patient management.
摘要：
背景先天性心脏病（CHD）是出生时心脏的结构性畸形。肺动脉高压（PH）可能是由于肺部血流量增加引起的，持续肺动脉压升高,或在手术修复期间使用体外循环（CPB）。吸入一氧化氮（iNO）选择性地降低肺血管中的高血压，而不降低全身血压，使其适用于治疗因心脏病导致的术后PH的儿童。然而,减少或停止iNO可加剧术后PH和低氧血症，需要长期管理和谨慎缩减。本研究旨在评估，使用机器学习(ML)，预测冠心病患者术后ICU心脏直视手术后需要长期使用iNO的因素，主要用于PH管理。方法我们使用ML方法建立一种算法来预测长期使用iNO的患者，并在2016年4月至2019年3月在鹿儿岛大学医院ICU存活并出院的34例小儿术后心脏直视手术患者中验证其准确性。所有患者在入住ICU后开始接受iNO治疗。总的来说,使用具有AutoGluon的ML分析,利用反映患者和手术特征的16个特征来预测需要iNO超过168小时的患者。数据集随机分为训练和测试队列，包括80%和20%的数据，分别。在训练组中，ML模型是使用Gini杂质减少和合成过采样技术选择的重要特征构建的。在测试队列中，通过计算受试者工作特征曲线下面积(AUC)和准确度来评估ML模型的预测性能.结果在训练队列中的28例患者中，5人需要iNO超过168小时；在测试队列中的6名患者中，一个人需要超过168小时的iNO。CPB,主动脉钳夹时间,内外平衡，和乳酸是预测超过168小时的iNO需求的四个最重要的特征。在培训队列中，ML模型实现了完美的分类，AUC为1.00。在测试队列中，ML模型也实现了完美的分类,AUC为1.00,准确率为1.00.结论ML方法确定了四个因素(CPB，内外平衡，主动脉交叉钳夹时间,和乳酸）与冠心病患者心脏直视手术后长期使用iNO的需求密切相关。通过了解这项研究的结果，我们可以更有效地管理iNO在CHD患者术后心脏直视手术中的给药，可能预防术后PH和低氧血症的复发，从而有助于更安全的患者管理。
