Predictive value of cardiac magnetic resonance imaging for fatal arrhythmias in structural and nonstructural heart diseases
Year: 2024
Type: article
Study design: Observational Study
Abstract: The risk stratification for fatal arrhythmias remains inadequate. Cardiac magnetic resonance (CMR) imaging provides a detailed evaluation of arrhythmogenic substrates. This study investigated the predictive capacity of multiparametric CMR for fatal ventricular arrhythmias (VAs) in a heterogeneous disease cohort. The study included 396 consecutive patients with structural heart disease (SHD, n = 248) and non-apparent SHD (n = 148) who underwent CMR scans between 2018 and 2022. The primary endpoint was fatal composite arrhythmias. Thirty-three patients (8.3 %) experienced fatal arrhythmias (25 with SHD, 8 with non-apparent SHD) over a median follow-up of 24 months. The independent risk factors for patients with SHD included syncope (hazard ratio [HR] = 5.347; P < 0.001), VA history (HR = 3.705; P = 0.004), right ventricular ejection fraction (RVEF) ≤ 45 % (HR = 2.587; P = 0.039), and the presence of late gadolinium enhancement (LGE) (HR = 4.767; P = 0.040). In the non-apparent SHD group, fatal arrhythmias were independently correlated with VA history (HR = 10.23; P = 0.005), RVEF ≤ 45 % (HR = 8.307; P = 0.015), and CMR myocardial abnormalities (HR = 5.203; P = 0.033). Patients at high risk of fatal arrhythmia in the SHD and non-apparent SHD groups exhibited 3-year event-free survival rates of 69.4 % and 83.5 %, respectively. CMR provides effective prognostic information for patients with and without apparent SHD. The presence of LGE, CMR myocardial abnormalities, and right ventricular dysfunction are strong risk markers for fatal arrhythmias.
Source: IJC Heart & Vasculature
Authors: Xing Xing, Xiaoqiang Liu, Yi Zhang, Lei Zhang, Gu Shen, Yulong Ge, Fang Wang
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,930
Award: G6789622245
OpenAlex ID: W4401699093
