Andrea Igoren Guaricci1, Pier Giorgio Masci2, Valentina Lorenzoni3, Jurg Schwitter2, Gianluca Pontone4. 1. Institute of Cardiovascular Disease, Department of Emergency and Organ Transplantation, University Hospital Policlinico of Bari, Bari, Italy. 2. Cardiac MR Center (CRMC), Division of Cardiology, Cardiovascular Department, Lausanne University Hospital-CHUV, Lausanne, Vaud, Switzerland. 3. Institute of Management, Scuola Superiore Sant'Anna, Pisa, Italy. 4. Centro Casrdiologico Monzino, IRCCS, Milan, Italy. Electronic address: gianluca.pontone@ccfm.it.
Abstract
BACKGROUND:Implantable cardioverter defibrillator (ICD) represents the most valuable sudden cardiac death (SCD) prophylactic strategy in patients with heart failure and severely reduced left ventricular ejection fraction (LVEF). To date, it is still unknown how to integrate the information given by cardiac magnetic resonance (CMR) into clinical and transthoracic echocardiography (TTE) work-up of non-ischemic cardiomyopathy (NICM) and ischemic cardiomyopathy (ICM) patients for accurate risk stratification. METHODS AND RESULTS: DERIVATE is a prospective, international, multicenter, observational registry of NICMand ICM patients with chronic heart failure and reduced LVEF who will undergo clinical evaluation, TTE and CMR. The registry will enrol cohorts from 34 sites. Complete risk factor, clinical presentation, TTE and CMR data will be collected and each patient will be followed-up for outcomes. Primary end point of the study is all-cause mortality. Secondary end points are: cardiovascular death, SCD, aborted SCD, sustained ventricular tachycardia (VT), and major adverse cardiac events (MACE) defined as a composite endpoint of SCD, aborted SCD, and sustained VT. Specifically, we will determine CMR findings that predict outcomes, with incremental value over LVEF and NYHA classification. Secondary aims consist in providing a comprehensive clinical and imaging score and testing the contribution of machine learning to determine prognostic CMR parameters. CONCLUSIONS: The final objective of the study consists in the identification of prognostic CMR parameters in a large prospective cohort for a better selection of patients with heart failure being worthy of primary prevention ICD therapy. (clinicaltrials.gov registration: RTT# NCT03352648).
RCT Entities:
BACKGROUND: Implantable cardioverter defibrillator (ICD) represents the most valuable sudden cardiac death (SCD) prophylactic strategy in patients with heart failure and severely reduced left ventricular ejection fraction (LVEF). To date, it is still unknown how to integrate the information given by cardiac magnetic resonance (CMR) into clinical and transthoracic echocardiography (TTE) work-up of non-ischemic cardiomyopathy (NICM) and ischemic cardiomyopathy (ICM) patients for accurate risk stratification. METHODS AND RESULTS: DERIVATE is a prospective, international, multicenter, observational registry of NICM and ICM patients with chronic heart failure and reduced LVEF who will undergo clinical evaluation, TTE and CMR. The registry will enrol cohorts from 34 sites. Complete risk factor, clinical presentation, TTE and CMR data will be collected and each patient will be followed-up for outcomes. Primary end point of the study is all-cause mortality. Secondary end points are: cardiovascular death, SCD, aborted SCD, sustained ventricular tachycardia (VT), and major adverse cardiac events (MACE) defined as a composite endpoint of SCD, aborted SCD, and sustained VT. Specifically, we will determine CMR findings that predict outcomes, with incremental value over LVEF and NYHA classification. Secondary aims consist in providing a comprehensive clinical and imaging score and testing the contribution of machine learning to determine prognostic CMR parameters. CONCLUSIONS: The final objective of the study consists in the identification of prognostic CMR parameters in a large prospective cohort for a better selection of patients with heart failure being worthy of primary prevention ICD therapy. (clinicaltrials.gov registration: RTT# NCT03352648).
Authors: Adriana Argentiero; Giuseppe Muscogiuri; Mark G Rabbat; Chiara Martini; Nicolò Soldato; Paolo Basile; Andrea Baggiano; Saima Mushtaq; Laura Fusini; Maria Elisabetta Mancini; Nicola Gaibazzi; Vincenzo Ezio Santobuono; Sandro Sironi; Gianluca Pontone; Andrea Igoren Guaricci Journal: J Clin Med Date: 2022-05-19 Impact factor: 4.964
Authors: Giuseppe Muscogiuri; Valentina Volpato; Riccardo Cau; Mattia Chiesa; Luca Saba; Marco Guglielmo; Alberto Senatieri; Gregorio Chierchia; Gianluca Pontone; Serena Dell'Aversana; U Joseph Schoepf; Mason G Andrews; Paolo Basile; Andrea Igoren Guaricci; Paolo Marra; Denisa Muraru; Luigi P Badano; Sandro Sironi Journal: Heliyon Date: 2022-10-05