与单一特点相比,多种ECG特点联合并不能改进心脏纤颤的预测:一项大规模院外心跳骤停人群的研究结果

Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests
2016-01-07 14:01发表评论
作者:He, M. , Gong, Y. , Li, Y. , Mauri, T. , Fumagalli, F. , Bozzola, M. , Cesana, G. , Latini, R. , Pesenti, A. , Ristagno, G.
机构: 第三军医大学 重庆大学 生物医学工程学院
期刊: Crit Care2015年12月1期19卷

Introduction: Quantitative electrocardiographic (ECG) waveform analysis provides a noninvasive reflection of the metabolic milieu of the myocardium during resuscitation and is a potentially useful tool to optimize the defibrillation strategy. However, whether combining multiple ECG features can improve the capability of defibrillation outcome prediction in comparison to single feature analysis is still uncertain. Methods: A total of 3828 defibrillations from 1617 patients who experienced out-of-hospital cardiac arrest were analyzed. A 2.048-s ECG trace prior to each defibrillation without chest compressions was used for the analysis. Sixteen predictive features were optimized through the training dataset that included 2447 shocks from 1050 patients. Logistic regression, neural network and support vector machine were used to combine multiple features for the prediction of defibrillation outcome. Performance between single and combined predictive features were compared by area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and prediction accuracy (PA) on a validation dataset that consisted of 1381 shocks from 567 patients. Results: Among the single features, mean slope (MS) outperformed other methods with an AUC of 0.876. Combination of complementary features using neural network resulted in the highest AUC of 0.874 among the multifeature-based methods. Compared to MS, no statistical difference was observed in AUC, sensitivity, specificity, PPV, NPV and PA when multiple features were considered. Conclusions: In this large dataset, the amplitude-related features achieved better defibrillation outcome prediction capability than other features. Combinations of multiple electrical features did not further improve prediction performance. © 2015 He et al.

 

通讯机构:Third Military Medical University and Chongqing University, School of Biomedical Engineering, 30 Gaotanyan Main Street, Chongqing, China
学科代码:急诊医学 重症监护   关键词:ECG特点 心脏纤颤 院外心跳骤停 ,中国作者重要发表 爱思唯尔医学网, Elseviermed
来源: Scopus
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