Current cardiology reports · 2024
A Comprehensive Review on the Electrocardiographic Manifestations of Cardiac Sarcoidosis: Patterns and Prognosis.
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Jain Hritvik, Marsool Marsool Mohammed Dheyaa, Verma Amogh, Irfan Hamza, Nadeem Abdullah, Jain Jyoti, Goyal Aman, Passey Siddhant, Gole Shrey, Khatib Mahalaqua Nazli, Zahiruddin Quazi Syed, Gaidhane Abhay M, Rustagi Sarvesh, Satapathy Prakasini
Аннотация
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.
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.