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Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning.使用机器学习预测心室颤动时心率变异性数据的最佳长度和预测时间
Comput Math Methods Med. 2021 Mar 16;2021:6663996. doi: 10.1155/2021/6663996. eCollection 2021.
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Low-energy, single-pulse surface stimulation defibrillates large mammalian ventricles.低能量、单次脉冲表面刺激可除颤大型哺乳动物心室。
Heart Rhythm. 2022 Feb;19(2):308-317. doi: 10.1016/j.hrthm.2021.10.006. Epub 2021 Oct 12.
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Do the predictors of right ventricular pacing-induced cardiomyopathy add up?右心室起搏诱导性心肌病的预测因素是否成立?
Indian Heart J. 2021 Sep-Oct;73(5):582-587. doi: 10.1016/j.ihj.2021.07.011. Epub 2021 Aug 3.
4
Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients.动态监测有望为代表性不足的患者提供公平的个性化医疗服务。
Eur Heart J Digit Health. 2021 Jun 28;2(3):494-510. doi: 10.1093/ehjdh/ztab047. eCollection 2021 Sep.
5
Microvolt T-Wave Alternans Is Modulated by Acute Low-Level Tragus Stimulation in Patients With Ischemic Cardiomyopathy and Heart Failure.缺血性心肌病和心力衰竭患者中,急性低强度耳屏刺激可调节微伏级T波交替变化。
Front Physiol. 2021 Jul 23;12:707724. doi: 10.3389/fphys.2021.707724. eCollection 2021.
6
Low-Level Tragus Stimulation Modulates Atrial Alternans and Fibrillation Burden in Patients With Paroxysmal Atrial Fibrillation.低位耳屏刺激调节阵发性心房颤动患者的心房交替和房颤负荷。
J Am Heart Assoc. 2021 Jun 15;10(12):e020865. doi: 10.1161/JAHA.120.020865. Epub 2021 Jun 2.
7
Clinical Potential of Beat-to-Beat Diastolic Interval Control in Preventing Cardiac Arrhythmias.实时舒张间期控制预防心律失常的临床潜力。
J Am Heart Assoc. 2021 Jun;10(11):e020750. doi: 10.1161/JAHA.121.020750. Epub 2021 May 22.
8
Machine Learning for Real-Time Heart Disease Prediction.机器学习实时心脏病预测。
IEEE J Biomed Health Inform. 2021 Sep;25(9):3627-3637. doi: 10.1109/JBHI.2021.3066347. Epub 2021 Sep 3.
9
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.深度神经网络可通过 12 导联心电图预测新发心房颤动,并有助于识别心房颤动相关卒中风险。
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Machine learning prediction in cardiovascular diseases: a meta-analysis.机器学习在心血管疾病中的预测:一项荟萃分析。
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心脏起搏进展:心律失常的预测、预防及控制策略

Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies.

作者信息

Patel Mehrie Harshad, Sampath Shrikanth, Kapoor Anoushka, Damani Devanshi Narendra, Chellapuram Nikitha, Challa Apurva Bhavana, Kaur Manmeet Pal, Walton Richard D, Stavrakis Stavros, Arunachalam Shivaram P, Kulkarni Kanchan

机构信息

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States.

Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States.

出版信息

Front Physiol. 2021 Dec 2;12:783241. doi: 10.3389/fphys.2021.783241. eCollection 2021.

DOI:10.3389/fphys.2021.783241
PMID:34925071
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8674736/
Abstract

Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.

摘要

心律失常给医疗保健带来了巨大负担,是全球范围内的主要死亡原因。据报道,数量惊人的人以心脏性猝死作为心律失常的首发症状,约占每年总死亡人数的20%。此外,易患房性快速性心律失常(如心房扑动和心房颤动)的患者通常伴有包括高血压、缺血性心脏病、瓣膜性心肌病等合并症,且中风风险增加。电刺激和传感方式的技术进步导致了包括起搏器和植入式除颤器在内的医疗设备的激增,旨在恢复正常心律。然而,鉴于人类心脏复杂的时空动态和非线性,预测心律失常的发作以及防止从稳态过渡到不稳定节律一直是一项极具挑战性的任务。除颤电击仍然是致死性室性心律失常的主要临床干预措施,但植入式心律转复除颤器的患者常因误判而遭受不适当电击,生活质量下降。在此,我们旨在全面综述心律失常预测、预防和控制策略的当前进展。我们概述了传统的临床心律失常管理方法,并描述了用于预测异常节律发作和有效抑制心律失常的有前景的潜在起搏技术。我们还从临床角度探讨了弥合基础科学与临床科学之间差距的问题,这将有助于采用有前景的抗心律失常起搏策略。