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缺血性心脏病患者的心脏性猝死预防——超越射血分数

Sudden Cardiac Death Prevention in Patients with Ischemic Heart Disease-Beyond the Ejection Fraction.

作者信息

Steiner Hillel, Sharabi Itzhak, Goldenberg Ilan

机构信息

Department of Cardiology, The Edith Wolfson Medical Center, 5822012 Holon, Israel; Sackler School of Medicine, Tel Aviv University, 6997801 Tel Aviv, Israel.

The Adelson School of Health, Ariel University, 4076414 Ariel, Israel.

出版信息

Rev Cardiovasc Med. 2022 Dec 14;23(12):409. doi: 10.31083/j.rcm2312409. eCollection 2022 Dec.

DOI:10.31083/j.rcm2312409
PMID:39076674
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11270479/
Abstract

Sudden cardiac death (SCD) in patients with ischemic heart disease remains a leading cause of death. Prediction of who is at risk is based on the left ventricular ejection fraction (EF). However, the majority of victims of SCD have a normal EF, and the majority of patients implanted with an implantable cardioverter- defibrillator based on their EF are never treated by their device. Several parameters could allow better prediction of SCD. Several signs on the ECG and Periodic Repolarization Dynamics have been associated with increased risk. Elevated serum biomarkers such as pro-B type natriuretic peptides and serum soluble suppression of tumorigenicity 2 protein (sST2) are predictive of SCD. On the echocardiogram, global longitudinal strain, speckle tracking and relative wall thickness have been implicated. Programmed ventricular stimulation studies and cardiac magnetic resonance are promising modalities that could be further investigated. In conclusion, the EF is an imperfect tool for predicting SCD. Using the modalities reviewed, a model could be created for better prediction of patients at risk.

摘要

缺血性心脏病患者的心脏性猝死(SCD)仍然是主要的死亡原因。对高危人群的预测基于左心室射血分数(EF)。然而,大多数SCD受害者的EF正常,并且大多数基于EF植入植入式心脏复律除颤器的患者从未接受过该设备的治疗。几个参数可以更好地预测SCD。心电图上的几个征象和周期性复极动态与风险增加有关。血清生物标志物升高,如B型利钠肽原和血清可溶性肿瘤抑制因子2蛋白(sST2)可预测SCD。在超声心动图上,整体纵向应变、斑点追踪和相对室壁厚度也有一定关联。程控心室刺激研究和心脏磁共振成像都是很有前景的方法,值得进一步研究。总之,EF是预测SCD的一个不完善的工具。利用所综述的方法,可以建立一个模型来更好地预测高危患者。

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