He Mi, Gong Yushun, Li Yongqin, Mauri Tommaso, Fumagalli Francesca, Bozzola Marcella, Cesana Giancarlo, Latini Roberto, Pesenti Antonio, Ristagno Giuseppe
School of Biomedical Engineering, Third Military Medical University and Chongqing University, 30 Gaotanyan Main Street, Chongqing, 400038, China.
Department of Anesthesia, Critical Care and Emergency, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via Francesco Sforza, 35, 20122, Milan, Italy.
Crit Care. 2015 Dec 10;19:425. doi: 10.1186/s13054-015-1142-z.
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.
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.
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.
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.
定量心电图（ECG）波形分析可在复苏期间对心肌代谢环境进行无创反映，是优化除颤策略的潜在有用工具。然而，与单特征分析相比，组合多个心电图特征是否能提高除颤结果预测能力仍不确定。
分析了1617例院外心脏骤停患者的3828次除颤情况。每次除颤前无胸外按压时2.048秒的心电图记录用于分析。通过包含1050例患者2447次电击的训练数据集优化了16个预测特征。使用逻辑回归、神经网络和支持向量机组合多个特征来预测除颤结果。在由567例患者的1381次电击组成的验证数据集上，通过受试者操作特征曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和预测准确性（PA）比较单预测特征和组合预测特征之间的性能。
在单特征中，平均斜率（MS）表现优于其他方法，AUC为0.876。在基于多特征的方法中，使用神经网络组合互补特征的AUC最高，为0.874。与MS相比，考虑多个特征时，在AUC、敏感性、特异性、PPV、NPV和PA方面未观察到统计学差异。
在这个大型数据集中，与其他特征相比，与振幅相关的特征具有更好的除颤结果预测能力。多个电特征的组合并未进一步提高预测性能。
