BACKGROUND: Current risk stratification for sudden cardiac death (SCD) in nonischemic dilated cardiomyopathy (NIDC) relies on left ventricular (LV) dysfunction, a poor marker of ventricular electrical instability. Contrast-enhanced cardiac magnetic resonance has the ability to accurately identify and quantify ventricular myocardial fibrosis (late gadolinium enhancement [LGE]). OBJECTIVE: To evaluate the impact of the presence and amount of myocardial fibrosis on arrhythmogenic risk prediction in NIDC. METHODS: One hundred thirty-seven consecutive patients with angiographically proven NIDC were enrolled for this study. All patients were followed up for a combined arrhythmic end point including sustained ventricular tachycardia (VT), appropriate implantable cardioverter-defibrillator (ICD) intervention, ventricular fibrillation (VF), and SCD. RESULTS: LV-LGE was identified in 76 (55.5%) patients. During a median follow-up of 3 years, the combined arrhythmic end point occurred in 22 (16.1%) patients: 8 (5.8%) sustained VT, 9 (6.6%) appropriate ICD intervention, either against VF (n = 5; 3.6%) or VT (n = 4; 2.9%), 3 (2.2%) aborted SCD, and 2 (1.5%) died suddenly. Kaplan-Meier analysis revealed a significant correlation between the LV-LGE presence (not the amount and distribution) and malignant arrhythmic events (P < .001). In univariate Cox regression analysis, LV-LGE (hazard ratio [HR] 4.17; 95% confidence interval [CI] 1.56-11.2; P = .005) and left bundle branch block (HR 2.43; 95% CI 1.01-5.41; P = .048) were found to be associated with arrhythmias. In multivariable analysis, the presence of LGE was the only independent predictor of arrhythmias (HR 3.8; 95% CI 1.3-10.4; P = .01). CONCLUSIONS: LV-LGE is a powerful and independent predictor of malignant arrhythmic prognosis, while its amount and distribution do not provide additional prognostic value. Contrast-enhanced cardiac magnetic resonance may contribute to identify candidates for ICD therapy not fulfilling the current criteria based on left ventricular ejection fraction.
BACKGROUND: Current risk stratification for sudden cardiac death (SCD) in nonischemic dilated cardiomyopathy (NIDC) relies on left ventricular (LV) dysfunction, a poor marker of ventricular electrical instability. Contrast-enhanced cardiac magnetic resonance has the ability to accurately identify and quantify ventricular myocardial fibrosis (late gadolinium enhancement [LGE]). OBJECTIVE: To evaluate the impact of the presence and amount of myocardial fibrosis on arrhythmogenic risk prediction in NIDC. METHODS: One hundred thirty-seven consecutive patients with angiographically proven NIDC were enrolled for this study. All patients were followed up for a combined arrhythmic end point including sustained ventricular tachycardia (VT), appropriate implantable cardioverter-defibrillator (ICD) intervention, ventricular fibrillation (VF), and SCD. RESULTS: LV-LGE was identified in 76 (55.5%) patients. During a median follow-up of 3 years, the combined arrhythmic end point occurred in 22 (16.1%) patients: 8 (5.8%) sustained VT, 9 (6.6%) appropriate ICD intervention, either against VF (n = 5; 3.6%) or VT (n = 4; 2.9%), 3 (2.2%) aborted SCD, and 2 (1.5%) died suddenly. Kaplan-Meier analysis revealed a significant correlation between the LV-LGE presence (not the amount and distribution) and malignant arrhythmic events (P < .001). In univariate Cox regression analysis, LV-LGE (hazard ratio [HR] 4.17; 95% confidence interval [CI] 1.56-11.2; P = .005) and left bundle branch block (HR 2.43; 95% CI 1.01-5.41; P = .048) were found to be associated with arrhythmias. In multivariable analysis, the presence of LGE was the only independent predictor of arrhythmias (HR 3.8; 95% CI 1.3-10.4; P = .01). CONCLUSIONS: LV-LGE is a powerful and independent predictor of malignant arrhythmic prognosis, while its amount and distribution do not provide additional prognostic value. Contrast-enhanced cardiac magnetic resonance may contribute to identify candidates for ICD therapy not fulfilling the current criteria based on left ventricular ejection fraction.
Authors: Fabian Sanchis-Gomar; Laura M Pérez; Michael J Joyner; Herbert Löllgen; Alejandro Lucia Journal: Sports Med Date: 2016-04 Impact factor: 11.136
Authors: Edmond M Cronin; Frank M Bogun; Philippe Maury; Petr Peichl; Minglong Chen; Narayanan Namboodiri; Luis Aguinaga; Luiz Roberto Leite; Sana M Al-Khatib; Elad Anter; Antonio Berruezo; David J Callans; Mina K Chung; Phillip Cuculich; Andre d'Avila; Barbara J Deal; Paolo Della Bella; Thomas Deneke; Timm-Michael Dickfeld; Claudio Hadid; Haris M Haqqani; G Neal Kay; Rakesh Latchamsetty; Francis Marchlinski; John M Miller; Akihiko Nogami; Akash R Patel; Rajeev Kumar Pathak; Luis C Saenz Morales; Pasquale Santangeli; John L Sapp; Andrea Sarkozy; Kyoko Soejima; William G Stevenson; Usha B Tedrow; Wendy S Tzou; Niraj Varma; Katja Zeppenfeld Journal: J Interv Card Electrophysiol Date: 2020-10 Impact factor: 1.900
Authors: Kuo Zhang; Wenyao Wang; Shihua Zhao; Stuart D Katz; Giorgio Iervasi; A Martin Gerdes; Yi-Da Tang Journal: Clin Cardiol Date: 2018-01-23 Impact factor: 2.882