OBJECTIVES: The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of myocardial heterogeneity, for predicting cardiovascular events in patients with dilated cardiomyopathy (DCM). BACKGROUND: Current risk stratification for ventricular arrhythmia in patients with DCM is imprecise. LV entropy is a measure of myocardial heterogeneity derived from cardiac magnetic resonance imaging that assesses the probability distribution of pixel signal intensities in the LV myocardium. METHODS: A registry-based cohort of primary prevention implantable cardioverter-defibrillator patients with DCM had their LV entropy, late gadolinium enhancement (LGE) presence, and LGE mass measured on cardiac magnetic resonance imaging. Patients were followed from implantable cardioverter-defibrillator placement for arrhythmic events (appropriate implantable cardioverter-defibrillator therapy, ventricular arrhythmia, or sudden cardiac death), end-stage heart failure events (cardiac death, transplantation, or ventricular assist device placement), and all-cause mortality. RESULTS: One hundred thirty patients (mean age 55 years, 83% men, LV ejection fraction 29%, mean LV entropy 5.58 ± 0.72, LGE present in 57%) were followed for a median of 3.2 years. Eighteen (14.0%) experienced arrhythmic events, 17 (13.1%) experienced end-stage heart failure events, and 7 (5.4%) died. LV entropy provided substantial improvement of predictive ability when added to a model containing clinical variables and LGE mass (hazard ratio: 3.5; 95% confidence interval: 1.42 to 8.82; p = 0.007; net reclassification index = 0.345, p = 0.04). For end-stage heart failure events, LV entropy did not improve the model containing clinical variables and LGE mass (hazard ratio: 2.03; 95% confidence interval: 0.78 to 5.28; p = 0.14). Automated LV entropy measurement has excellent intraobserver (mean difference 0.04) and interobserver (mean difference 0.03) agreement. CONCLUSIONS: Automated LV entropy measurement is a novel marker for risk stratification toward ventricular arrhythmia in patients with DCM.
OBJECTIVES: The aim of this study was to assess the utility of left ventricular (LV) entropy, a novel measure of myocardial heterogeneity, for predicting cardiovascular events in patients with dilated cardiomyopathy (DCM). BACKGROUND: Current risk stratification for ventricular arrhythmia in patients with DCM is imprecise. LV entropy is a measure of myocardial heterogeneity derived from cardiac magnetic resonance imaging that assesses the probability distribution of pixel signal intensities in the LV myocardium. METHODS: A registry-based cohort of primary prevention implantable cardioverter-defibrillator patients with DCM had their LV entropy, late gadolinium enhancement (LGE) presence, and LGE mass measured on cardiac magnetic resonance imaging. Patients were followed from implantable cardioverter-defibrillator placement for arrhythmic events (appropriate implantable cardioverter-defibrillator therapy, ventricular arrhythmia, or sudden cardiac death), end-stage heart failure events (cardiac death, transplantation, or ventricular assist device placement), and all-cause mortality. RESULTS: One hundred thirty patients (mean age 55 years, 83% men, LV ejection fraction 29%, mean LV entropy 5.58 ± 0.72, LGE present in 57%) were followed for a median of 3.2 years. Eighteen (14.0%) experienced arrhythmic events, 17 (13.1%) experienced end-stage heart failure events, and 7 (5.4%) died. LV entropy provided substantial improvement of predictive ability when added to a model containing clinical variables and LGE mass (hazard ratio: 3.5; 95% confidence interval: 1.42 to 8.82; p = 0.007; net reclassification index = 0.345, p = 0.04). For end-stage heart failure events, LV entropy did not improve the model containing clinical variables and LGE mass (hazard ratio: 2.03; 95% confidence interval: 0.78 to 5.28; p = 0.14). Automated LV entropy measurement has excellent intraobserver (mean difference 0.04) and interobserver (mean difference 0.03) agreement. CONCLUSIONS: Automated LV entropy measurement is a novel marker for risk stratification toward ventricular arrhythmia in patients with DCM.
Authors: Konstantinos N Aronis; David R Okada; Eric Xie; Usama A Daimee; Adityo Prakosa; Nisha A Gilotra; Katherine C Wu; Natalia Trayanova; Jonathan Chrispin Journal: Pacing Clin Electrophysiol Date: 2021-11-26 Impact factor: 1.976
Authors: David R Okada; Jason Miller; Jonathan Chrispin; Adityo Prakosa; Natalia Trayanova; Steven Jones; Mauro Maggioni; Katherine C Wu Journal: Circ Arrhythm Electrophysiol Date: 2020-03-18
Authors: Gabriel Balaban; Brian P Halliday; Bradley Porter; Wenjia Bai; Ståle Nygåard; Ruth Owen; Suzan Hatipoglu; Nuno Dias Ferreira; Cemil Izgi; Upasana Tayal; Ben Corden; James Ware; Dudley J Pennell; Daniel Rueckert; Gernot Plank; Christopher A Rinaldi; Sanjay K Prasad; Martin J Bishop Journal: JACC Clin Electrophysiol Date: 2020-10-29
Authors: Gabriel Balaban; Brian P Halliday; Daniel Hammersley; Christopher A Rinaldi; Sanjay K Prasad; Martin J Bishop; Pablo Lamata Journal: Europace Date: 2022-07-21 Impact factor: 5.486
Authors: Gabriel Balaban; Brian P Halliday; Wenjia Bai; Bradley Porter; Carlotta Malvuccio; Pablo Lamata; Christopher A Rinaldi; Gernot Plank; Daniel Rueckert; Sanjay K Prasad; Martin J Bishop Journal: PLoS Comput Biol Date: 2019-10-28 Impact factor: 4.475
Authors: Jie Wang; Laura Bravo; Jinquan Zhang; Wen Liu; Ke Wan; Jiayu Sun; Yanjie Zhu; Yuchi Han; Georgios V Gkoutos; Yucheng Chen Journal: Front Cardiovasc Med Date: 2021-12-10