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基于带有合成少数过采样技术(SMOTE)的决策树的左心室辅助装置患者右心室衰竭的预后

Prognosis of right ventricular failure in patients with left ventricular assist device based on decision tree with SMOTE.

作者信息

Wang Yajuan, Simon Marc, Bonde Pramod, Harris Bronwyn U, Teuteberg Jeffrey J, Kormos Robert L, Antaki James F

机构信息

Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

出版信息

IEEE Trans Inf Technol Biomed. 2012 May;16(3):383-90. doi: 10.1109/TITB.2012.2187458. Epub 2012 Feb 10.

Abstract

Right ventricular failure is a significant complication following implantation of a left ventricular assist device (LVAD), which increases morbidity and mortality. Consequently, researchers have sought predictors that may identify patients at risk. However, they have lacked sensitivity and/or specificity. This study investigated the use of a decision tree technology to explore the preoperative data space for combinatorial relationships that may be more accurate and precise. We retrospectively analyzed the records of 183 patients with initial LVAD implantation at the Artificial Heart Program, University of Pittsburgh Medical Center, between May 1996 and October 2009. Among those patients, 27 later required a right ventricular assist device (RVAD+) and 156 remained on LVAD (RVAD-) until the time of transplantation or death. A synthetic minority oversampling technique (SMOTE) was applied to the RVAD+ group to compensate for the disparity of sample size. Twenty-one resampling levels were evaluated, with decision tree model built for each. Among these models, the top six predictors of the need for an RVAD were transpulmonary gradient (TPG), age, international normalized ratio (INR), heart rate (HR), aspartate aminotransferase (AST), prothrombin time, and right ventricular systolic pressure. TPG was identified to be the most predictive variable in 15 out of 21 models, and constituted the first splitting node with 7 mmHg as the breakpoint. Oversampling was shown to improve the senstivity of the models monotonically, although asymptotically, while the specificity was diminished to a lesser degree. The model built upon 5X synthetic RVAD+ oversampling was found to provide the best compromise between sensitivity and specificity, included TPG (layer 1), age (layer 2), right atrial pressure (layer 3), HR (layer 4,7), INR (layer 4, 9), alanine aminotransferase (layer 5), white blood cell count (layer 5,6, &7), the number of inotrope agents (layer 6), creatinine (layer 8), AST (layer 9, 10), and cardiac output (layer 9). It exhibited 85% sensitivity, 83% specificity, and 0.87 area under the receiver operating characteristic curve (RoC), which was found to be greatly improved compared to previously published studies.

摘要

右心室衰竭是植入左心室辅助装置(LVAD)后的一个严重并发症,会增加发病率和死亡率。因此,研究人员一直在寻找可能识别有风险患者的预测指标。然而,这些指标缺乏敏感性和/或特异性。本研究调查了使用决策树技术来探索术前数据空间中可能更准确和精确的组合关系。我们回顾性分析了1996年5月至2009年10月期间在匹兹堡大学医学中心人工心脏项目中首次植入LVAD的183例患者的记录。在这些患者中,27例后来需要右心室辅助装置(RVAD+),156例在移植或死亡前一直使用LVAD(RVAD-)。对RVAD+组应用了合成少数过采样技术(SMOTE)以补偿样本量的差异。评估了21个重采样水平,并为每个水平构建了决策树模型。在这些模型中,需要RVAD的前六个预测指标是跨肺压梯度(TPG)、年龄、国际标准化比值(INR)、心率(HR)、天冬氨酸转氨酶(AST)、凝血酶原时间和右心室收缩压。在21个模型中的15个中,TPG被确定为最具预测性的变量,并以7 mmHg作为断点构成第一个分裂节点。过采样显示出单调提高模型的敏感性,尽管是渐近的,而特异性降低的程度较小。发现基于5倍合成RVAD+过采样构建的模型在敏感性和特异性之间提供了最佳折衷,包括TPG(第1层)、年龄(第2层)、右心房压(第3层)、HR(第4、7层)、INR(第4、9层)、丙氨酸转氨酶(第5层)、白细胞计数(第5、6、7层)、血管活性药物数量(第6层)、肌酐(第8层)、AST(第9、10层)和心输出量(第9层)。它表现出85%的敏感性、83%的特异性和0.87的受试者工作特征曲线(RoC)下面积,与先前发表的研究相比有很大改善。

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