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预测除颤成功率。

Predicting defibrillation success.

作者信息

Strohmenger Hans-Ulrich

机构信息

Department of Anesthesiology and Critical Care Medicine, Medical University Innsbruck, Innsbruck, Austria.

出版信息

Curr Opin Crit Care. 2008 Jun;14(3):311-6. doi: 10.1097/MCC.0b013e3282fc9a9c.

DOI:10.1097/MCC.0b013e3282fc9a9c
PMID:18467892
Abstract

PURPOSE OF REVIEW

Ventricular fibrillation is the primary rhythm in many cardiac arrest patients. Since the late 1980s, the surface electrocardiogram of ventricular fibrillation has been subjected to analysis to obtain reliable information about the likelihood of successful countershock and to estimate the duration of cardiac arrest. Considerable efforts were made in the past 2 years to further improve the predictive power of rescue shock measures.

RECENT FINDINGS

In a retrospective clinical study, ventricular fibrillation single feature analysis was not able to reliably estimate duration between cardiac arrest onset and initial electrocardiogram. Combining ventricular fibrillation features in the time and frequency domain by employing neural networks did not further improve the best single feature prediction power taken from higher ventricular fibrillation frequency bands. Cardioversion outcome prediction based on the wavelet technique increased the specificity up to 66% at the 95% sensitivity level.

SUMMARY

Recent results question the ventricular fibrillation feature analysis as a reliable tool to estimate the duration of human cardiac arrest. Animal and clinical studies confirmed that ventricular fibrillation waveform analysis contains information to reliably predict the countershock success rate and further improved countershock outcome prediction. Prospective clinical studies are highly warranted to demonstrate that ventricular fibrillation waveform analysis definitely improves survival after cardiac arrest.

摘要

综述目的

室颤是许多心脏骤停患者的主要心律。自20世纪80年代末以来,室颤的体表心电图一直受到分析,以获取有关成功除颤可能性的可靠信息,并估计心脏骤停的持续时间。在过去两年中,人们付出了相当大的努力来进一步提高抢救性电击措施的预测能力。

最新发现

在一项回顾性临床研究中,室颤单一特征分析无法可靠地估计心脏骤停发作与初始心电图之间的持续时间。通过使用神经网络在时域和频域中结合室颤特征,并没有进一步提高从较高室颤频带获取的最佳单一特征预测能力。基于小波技术的心脏复律结果预测在95%敏感性水平下将特异性提高到了66%。

总结

最近的结果对室颤特征分析作为估计人类心脏骤停持续时间的可靠工具提出了质疑。动物和临床研究证实,室颤波形分析包含可靠预测除颤成功率和进一步改善除颤结果预测的信息。非常有必要进行前瞻性临床研究,以证明室颤波形分析确实能提高心脏骤停后的生存率。

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引用本文的文献

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Advanced ECG feature extraction and SVM classification for predicting defibrillation success in OHCA.用于预测院外心脏骤停除颤成功率的高级心电图特征提取与支持向量机分类
Front Cardiovasc Med. 2025 Jul 16;12:1550422. doi: 10.3389/fcvm.2025.1550422. eCollection 2025.
2
A method to predict ventricular fibrillation shock outcome during chest compressions.一种预测胸外按压期间室颤电击转归的方法。
Comput Biol Med. 2021 Feb;129:104136. doi: 10.1016/j.compbiomed.2020.104136. Epub 2020 Nov 21.
3
Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation.
心室颤动波形测量结合先前的电击结果可预测心肺复苏期间的除颤成功率。
J Electrocardiol. 2018 Jan-Feb;51(1):99-106. doi: 10.1016/j.jelectrocard.2017.07.016. Epub 2017 Aug 1.
4
Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning.通过机器学习对除颤成功进行预测的非线性动力信号特征描述。
BMC Med Inform Decis Mak. 2012 Oct 15;12:116. doi: 10.1186/1472-6947-12-116.