Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest.
Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest.
复制标题
院外心脏骤停复苏期间电击难治性心室颤动的预测。
DOI：
10.1161/circulationaha.122.063651
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发表时间：
2023
期刊：
影响因子：
37.8
通讯作者：
Kudenchuk,PeterJ
中科院分区：
文献类型：
--
作者：
Coult,Jason;Yang,BettyY;Kwok,Heemun;Kutz,JNathan;Boyle,PatrickM;Blackwood,Jennifer;Rea,ThomasD;Kudenchuk,PeterJ
休克难治性室颤（VF）引起的院外心脏骤停与相对较差的生存率相关。预测难治性VF的能力（需要≥3次电击）可以进行旨在改善结局的先发制人的靶向干预，如早期给予抗肾上腺素，重新考虑肾上腺素的使用或剂量，改变电击策略，方法：我们进行了一项VF院外心脏骤停的队列研究，以制定一项ECG-的算法来预测难治性VF患者。将具有可用除颤器记录的患者以80%/20%的比例随机分为训练/测试组。应用随机森林分类器对心肺复苏过程中初始电击前即刻和电击后1分钟的3 s ECG段进行分类，以基于ECG小波变换的奇异值分解来预测是否需要≥3次电击。根据受试者工作特征曲线下面积量化性能。在1376例VF院外心脏骤停患者中，311例（23%）为女性，864例（63%）经历了难治性VF，591例（43%）实现了功能性神经存活。总的电击次数与神经功能存活的可能性降低相关，每次连续电击的相对危险度为0.95（95%CI，0.93-0.97）（P<0.001）。在275例受试者中，预测难治性室颤的受试者工作特征曲线下面积为0.85（95%CI，0.79-0.89），特异性为91%，敏感性为63%，阳性似然比为6.7。结论使用初始休克周围ECG的机器学习算法预测可能发生难治性VF的患者，并且可以使救援人员能够预先瞄准干预以潜在地改善复苏结果。
BACKGROUNDOut-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation (VF) is associated with relatively poor survival. The ability to predict refractory VF (requiring ≥3 shocks) in advance of repeated shock failure could enable preemptive targeted interventions aimed at improving outcome, such as earlier administration of antiarrhythmics, reconsideration of epinephrine use or dosage, changes in shock delivery strategy, or expedited invasive treatments.METHODSWe conducted a cohort study of VF out-of-hospital cardiac arrest to develop an ECG-based algorithm to predict patients with refractory VF. Patients with available defibrillator recordings were randomized 80%/20% into training/test groups. A random forest classifier applied to 3-s ECG segments immediately before and 1 minute after the initial shock during cardiopulmonary resuscitation was used to predict the need for ≥3 shocks based on singular value decompositions of ECG wavelet transforms. Performance was quantified by area under the receiver operating characteristic curve.RESULTSOf 1376 patients with VF out-of-hospital cardiac arrest, 311 (23%) were female, 864 (63%) experienced refractory VF, and 591 (43%) achieved functional neurological survival. Total shock count was associated with decreasing likelihood of functional neurological survival, with a relative risk of 0.95 (95% CI, 0.93–0.97) for each successive shock (P<0.001). In the 275 test patients, the area under the receiver operating characteristic curve for predicting refractory VF was 0.85 (95% CI, 0.79–0.89), with specificity of 91%, sensitivity of 63%, and a positive likelihood ratio of 6.7.CONCLUSIONSA machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.