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首页> 外文期刊>Information Technology in Biomedicine, IEEE Transactions on >Prognosis of Right Ventricular Failure in Patients With Left Ventricular Assist Device Based on Decision Tree With SMOTE
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Prognosis of Right Ventricular Failure in Patients With Left Ventricular Assist Device Based on Decision Tree With SMOTE

机译:基于SMOTE决策树的左心辅助装置患者右心衰竭的预后

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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), A- T (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.
机译:植入右心室辅助装置(LVAD)后,右心室衰竭是严重的并发症,这会增加发病率和死亡率。因此,研究人员寻求了可以识别高危患者的预测因子。但是,它们缺乏敏感性和/或特异性。这项研究调查了决策树技术的使用,以探索术前数据空间以了解可能更为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月在匹兹堡大学医学中心人工心脏计划中183例初次LVAD植入患者的记录。在这些患者中,有27例后来需要右心室辅助装置(RVAD +),其余156例仍在接受LVAD(RVAD-)直到移植或死亡为止。 RVAD +组采用了合成少数样本过采样技术(SMOTE),以弥补样本量的差异。评估了21个重采样级别,并为每个级别建立了决策树模型。在这些模型中,是否需要RVAD的前六项预测指标是跨肺梯度(TPG),年龄,国际标准化比率(INR),心率(HR),天冬氨酸转氨酶(AST),凝血酶原时间和右室收缩压。在21种模型中的15种中,TPG被确定为最具预测性的变量,并且以7 mmHg作为断点构成了第一个分裂节点。研究表明,过采样可以渐进地提高模型的敏感性,尽管渐近性可以降低,但特异性降低的程度较小。发现基于5X合成RVAD +过采样的模型可以在敏感性和特异性之间提供最佳折衷,包括TPG(第1层),年龄(第2层),右心房压力(第3层),HR(第4,7层),INR (第4层,第9层),丙氨酸转氨酶(第5层),白细胞计数(第5、6,&7层),营养剂的数量(第6层),肌酐(第8层),A- T(第9层) 10)和心输出量(第9层)。它在接收器工作特性曲线(RoC)下显示85%的灵敏度,83%的特异性和0.87的面积,与以前发表的研究相比,发现它有很大的提高。

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