Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.
Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.
复制标题
使用神经网络将幅度谱区域与先前的电击信息相结合，提高了院外心脏骤停患者后续电击除颤结果的预测性能
DOI：
10.1371/journal.pone.0149115
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Li Y
中科院分区：
文献类型：
--
作者：
He M;Lu Y;Zhang L;Zhang H;Gong Y;Li Y
客观定量心室颤动 (VF) 波形分析是优化除颤的潜在强大工具。然而，将 VF 特征与与先前冲击相关的附加属性相结合是否可以增强对后续冲击的预测性能仍不确定。方法 本研究对 199 名院外心脏骤停患者的 528 次除颤电击进行了分析。使用每次电击前除颤器 ECG 记录中的振幅谱面积 (AMSA) 对 VF 波形进行量化。 AMSA 与先前电击指数 (PSI) 或/和连续电击之间 AMSA 变化 (ΔAMSA) 的组合通过训练数据集进行训练，该数据集包括来自 99 名患者的 255 次电击和神经网络。通过由 100 名患者的 273 次电击组成的验证数据集，通过受试者工作特征曲线下面积 (AUC)、灵敏度、阳性预测值 (PPV)、阴性预测值 (NPV) 和预测准确性 (PA)，将组合方法的性能与基于 AMSA 的单一特征预测进行比较。结果 验证数据集中共有 61 名 (61.0%) 患者需要后续电击 (N = 173)。在后续电击的不同组合方法中，将 AMSA 与 PSI 和 ΔAMSA 组合获得了最高的 AUC（0.904 vs. 0.819，p<0.001）。与特异性阈值为 90% 的基于 AMSA 的单一特征预测相比，灵敏度（76.5% vs. 35.3%，p<0.001）、NPV（90.2% vs. 76.9%，p = 0.007）和 PA（86.1% vs. 74.0%，p = 0.005）得到了极大的提高。结论 在这项回顾性研究中，使用神经网络将 AMSA 与先前的电击信息相结合，大大提高了后续电击除颤结果的预测性能。
Objective Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain. Methods A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients. Results A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001) among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, p<0.001), NPV (90.2% vs. 76.9%, p = 0.007) and PA (86.1% vs. 74.0%, p = 0.005)were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity. Conclusion In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks.