Background

Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. Quantitative analysis of the VF electrocardiogram (ECG) can predict patient outcomes and could potentially enable a patient-specific, guided approach to resuscitation. However, VF analysis during resuscitation is confounded by cardiopulmonary resuscitation (CPR) artifact in the ECG, challenging continuous application to guide therapy throughout resuscitation. We therefore sought to design a method to predict VF shock outcomes during CPR.

Methods

Study data included 4577 5-s VF segments collected during and without CPR prior to defibrillation attempts in N = 1151 arrest patients. Using training data (460 patients), an algorithm was designed to predict the VF shock outcomes of defibrillation success (return of organized ventricular rhythm) and functional survival (Cerebral Performance Category 1–2). The algorithm was designed with variable-frequency notch filters to reduce CPR artifact in the ECG based on real-time chest compression rate. Ten ECG features and three dichotomous patient characteristics were developed to predict outcomes. These variables were combined using support vector machines and logistic regression. Algorithm performance was evaluated by area under the receiver operating characteristic curve (AUC) to predict outcomes in validation data (691 patients).

Results

AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71–0.77) during CPR and 0.77 (0.74–0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72–0.78) during CPR and 0.76 (0.74–0.79) without CPR.

Conclusion

A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy.

中文翻译：

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一种预测胸外按压期间心室颤动休克结果的方法

背景

院外室颤 (VF) 心脏骤停是死亡的主要原因。VF 心电图 (ECG) 的定量分析可以预测患者的治疗结果，并有可能实现针对患者的具体复苏指导方法。然而，复苏过程中的心室颤动分析会受到心电图心肺复苏 (CPR) 伪影的影响，这对在整个复苏过程中持续应用指导治疗提出了挑战。因此，我们试图设计一种方法来预测心肺复苏期间室颤休克的结果。

方法

研究数据包括在 N = 1151 名逮捕患者中尝试除颤之前在心肺复苏期间和未进行心肺复苏期间收集的 4577 个 5 秒 VF 片段。使用训练数据（460 名患者），设计了一种算法来预测除颤成功（有组织的心室节律恢复）和功能生存（大脑性能类别 1-2）的室颤休克结果。该算法采用变频陷波滤波器设计，可根据实时胸外按压率减少心电图中的心肺复苏伪影。开发了十种心电图特征和三种二分患者特征来预测结果。使用支持向量机和逻辑回归将这些变量组合起来。算法性能通过受试者工作特征曲线 (AUC) 下的面积进行评估，以预测验证数据（691 名患者）的结果。

结果

在 CPR 期间预测除颤成功的 AUC（95% 置信区间）为 0.74 (0.71–0.77)，在不进行 CPR 时为 0.77 (0.74–0.79)。CPR 期间预测功能生存的 AUC 为 0.75 (0.72–0.78)，不进行 CPR 时预测功能生存的 AUC 为 0.76 (0.74–0.79)。

结论

一种新的算法可以预测室颤骤停后持续心肺复苏期间除颤的成功率和功能存活率，为复苏治疗的实时指导提供了潜在的概念验证。