A Comprehensive Review on the Electrocardiographic Manifestations of Cardiac Sarcoidosis: Patterns and Prognosis.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
ACS Applied Electronic Materials Pub Date : 2024-09-01 Epub Date: 2024-07-02 DOI:10.1007/s11886-024-02088-5
Hritvik Jain, Mohammed Dheyaa Marsool Marsool, Amogh Verma, Hamza Irfan, Abdullah Nadeem, Jyoti Jain, Aman Goyal, Siddhant Passey, Shrey Gole, Mahalaqua Nazli Khatib, Quazi Syed Zahiruddin, Abhay M Gaidhane, Sarvesh Rustagi, Prakasini Satapathy
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Abstract

Purpose of review: Cardiac sarcoidosis (CS) refers to cardiac involvement in sarcoidosis and is usually associated with worse outcomes. This comprehensive review aims to elucidate the electrocardiographic (ECG) signs and features associated with CS, as well as examine modern techniques and their importance in CS evaluation.

Recent findings: The exact pathogenesis of CS is still unclear, but it stems from an abnormal immunological response triggered by environmental factors in individuals with genetic predisposition. CS presents with non-cardiac symptoms; however, conduction system abnormalities are common in patients with CS. The most common electrocardiographic (ECG) signs include atrioventricular blocks and ventricular tachyarrhythmia. Distinct patterns, such as fragmented QRS complexes, T-wave alternans, and bundle branch blocks, are critical indicators of myocardial involvement. The application of advanced ECG techniques such as signal-averaged ECG, Holter monitoring, wavelet-transformed ECG, microvolt T-wave alternans, and artificial intelligence-supported analysis holds promising outcomes for opportune detection and monitoring of CS. Timely utilisation of inexpensive and readily available ECG possesses the potential to allow early detection and intervention for CS. The integration of artificial intelligence models into ECG analysis is a promising approach for improving the ECG diagnostic accuracy and further risk stratification of patients with CS.

Abstract Image

心脏肉样瘤病心电图表现综述:模式与预后
审查目的:心脏肉样瘤病(CS)是指肉样瘤病的心脏受累,通常与较差的预后有关。本综述旨在阐明与 CS 相关的心电图(ECG)征象和特征,并探讨现代技术及其在 CS 评估中的重要性:CS 的确切发病机制尚不清楚,但它源于具有遗传倾向的个体在环境因素诱发下产生的异常免疫反应。CS 表现为非心脏症状,但传导系统异常在 CS 患者中很常见。最常见的心电图(ECG)表现包括房室传导阻滞和室性心动过速。QRS 波群分裂、T 波交替和束支传导阻滞等独特模式是心肌受累的关键指标。应用先进的心电图技术,如信号平均心电图、Holter 监测、小波变换心电图、微伏 T 波交替和人工智能支持的分析,有望及时发现和监测 CS。及时利用廉价且易于获得的心电图有可能对 CS 进行早期检测和干预。将人工智能模型整合到心电图分析中是提高心电图诊断准确性和进一步对 CS 患者进行风险分层的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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