Arrhythmias in Cardiac Sarcoidosis: Pathophysiology, Diagnostic Strategies, Risk Stratification for Sudden Cardiac Death, and Contemporary Management—A Narrative Review

Vollständiger Abstract

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Background: Cardiac sarcoidosis (CS) is a potentially life-threatening manifestation of systemic granulomatous disease, characterized by a heterogeneous spectrum of arrhythmic and conduction disorders that represent the leading cause of CS-related sudden cardiac death (SCD). Clinical cardiac involvement is estimated at 5% of sarcoidosis patients, while silent myocardial infiltration is detected in 20–25% of autopsy series. Diagnosis is difficult because the disease frequently runs a subclinical course and no single test is pathognomonic. Methods: PubMed and Embase were searched to 5 August 2026 for this narrative review, incorporating peer-reviewed articles, international guidelines (HRS 2014, JCS 2016, AHA/ACC/HRS 2017, ESC 2022, AHA 2024, the 2024 European clinical consensus statement and the 2025 ESC guidelines on myocarditis and pericarditis), and registry data. Results: Atrioventricular (AV) block occurs in 23–30% of CS patients and frequently requires permanent pacing. Fatal ventricular arrhythmia rates reach 20.7% at 5 years and 31.9% at 10 years (ILLUMINATE-CS registry, n = 512). Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) performs well diagnostically (pooled sensitivity 95%, specificity 85%) and is associated with an odds ratio of 10.74 for arrhythmic events. 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) provides complementary value for guiding immunosuppressive therapy and pre-ablation risk assessment. Implantable loop recorders enable early arrhythmia detection in patients not yet meeting implantable cardioverter-defibrillator (ICD) criteria. Conclusion: Optimal management of arrhythmias in CS requires a multimodal diagnostic approach integrating electrocardiography, advanced cardiac imaging, and continuous rhythm monitoring. Risk stratification for SCD remains the central challenge, requiring individualized decision-making that integrates left ventricular ejection fraction (LVEF), imaging biomarkers, and electrophysiological data. Future prospective studies should aim to refine predictive risk algorithms, assess the role of emerging immunomodulatory agents such as JAK/STAT inhibitors, and validate artificial intelligence (AI)-assisted tools intended to identify cardiac involvement and arrhythmic risk earlier in the disease course.