Robert Jablonowski1, Uzma Chaudhry1, Jesper van der Pals1, Henrik Engblom1, Håkan Arheden1, Einar Heiberg1, Katherine C Wu1, Rasmus Borgquist1, Marcus Carlsson2. 1. From the Clinical Physiology (R.J., H.E., H.A., E.H., M.C.) and Cardiology (U.C., J.v.d.P., R.B.), Department of Clinical Sciences, Lund University, Lund University Hospital, Sweden; Department of Biomedical Engineering and Centre for Mathematical Sciences, Faculty of Engineering, Lund University, Sweden (E.H.); and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W.). 2. From the Clinical Physiology (R.J., H.E., H.A., E.H., M.C.) and Cardiology (U.C., J.v.d.P., R.B.), Department of Clinical Sciences, Lund University, Lund University Hospital, Sweden; Department of Biomedical Engineering and Centre for Mathematical Sciences, Faculty of Engineering, Lund University, Sweden (E.H.); and Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institutions, Baltimore, MD (K.C.W.). Marcus.Carlsson@med.lu.se.
Abstract
BACKGROUND: Late gadolinium enhancement (LGE) border zone on cardiac magnetic resonance imaging has been proposed as an independent predictor of ventricular arrhythmias. The purpose was to determine whether size and heterogeneity of LGE predict appropriate implantable cardioverter defibrillator (ICD) therapy in ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM) patients and to evaluate 4 LGE border-zone algorithms. METHODS AND RESULTS: ICM and NICM patients who underwent LGE cardiac magnetic resonance imaging prior to ICD implantation were retrospectively included. Two semiautomatic algorithms, expectation maximization, weighted intensity, a priori information and a weighted border zone algorithm, were compared with a modified full-width half-maximum and a 2-3SD threshold-based algorithm (2-3SD). Hazard ratios were calculated per 1% increase in LGE. A total of 74 ICM and 34 NICM were followed for 63 months (1-140) and 52 months (0-133), respectively. ICM patients had 27 appropriate ICD events, and NICM patients had 7 ICD events. In ICM patients with primary prophylactic ICD, LGE border zone predicted ICD therapy in univariable and multivariable analysis measured by the expectation maximization, weighted intensity, a priori information, weighted border zone, and modified full-width half-maximum algorithms (hazard ratios 1.23, 1.22, and 1.05, respectively; P<0.05; negative predictive value 92%). For NICM, total LGE by all 4 methods was the strongest predictor (hazard ratios, 1.03-1.04; P<0.05), though the number of events was small. CONCLUSIONS: Appropriate ICD therapy can be predicted in ICM patients with primary prevention ICD by quantifying the LGE border zone. In NICM patients, total LGE but not LGE border zone had predictive value for ICD therapy. However, the algorithms used affects the predictive value of these measures.
BACKGROUND: Late gadolinium enhancement (LGE) border zone on cardiac magnetic resonance imaging has been proposed as an independent predictor of ventricular arrhythmias. The purpose was to determine whether size and heterogeneity of LGE predict appropriate implantable cardioverter defibrillator (ICD) therapy in ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM) patients and to evaluate 4 LGE border-zone algorithms. METHODS AND RESULTS: ICM and NICM patients who underwent LGE cardiac magnetic resonance imaging prior to ICD implantation were retrospectively included. Two semiautomatic algorithms, expectation maximization, weighted intensity, a priori information and a weighted border zone algorithm, were compared with a modified full-width half-maximum and a 2-3SD threshold-based algorithm (2-3SD). Hazard ratios were calculated per 1% increase in LGE. A total of 74 ICM and 34 NICM were followed for 63 months (1-140) and 52 months (0-133), respectively. ICM patients had 27 appropriate ICD events, and NICM patients had 7 ICD events. In ICM patients with primary prophylactic ICD, LGE border zone predicted ICD therapy in univariable and multivariable analysis measured by the expectation maximization, weighted intensity, a priori information, weighted border zone, and modified full-width half-maximum algorithms (hazard ratios 1.23, 1.22, and 1.05, respectively; P<0.05; negative predictive value 92%). For NICM, total LGE by all 4 methods was the strongest predictor (hazard ratios, 1.03-1.04; P<0.05), though the number of events was small. CONCLUSIONS: Appropriate ICD therapy can be predicted in ICM patients with primary prevention ICD by quantifying the LGE border zone. In NICM patients, total LGE but not LGE border zone had predictive value for ICD therapy. However, the algorithms used affects the predictive value of these measures.
Authors: Stijntje D Roes; C Jan Willem Borleffs; Rob J van der Geest; Jos J M Westenberg; Nina Ajmone Marsan; Theodorus A M Kaandorp; Johan H C Reiber; Katja Zeppenfeld; Hildo J Lamb; Albert de Roos; Martin J Schalij; Jeroen J Bax Journal: Circ Cardiovasc Imaging Date: 2009-03-23 Impact factor: 7.792
Authors: Sebastiaan R D Piers; Kimberly Everaerts; Rob J van der Geest; Mark R Hazebroek; Hans-Marc Siebelink; Laurent A F G Pison; Martin J Schalij; Sebastiaan C A M Bekkers; Stephane Heymans; Katja Zeppenfeld Journal: Heart Rhythm Date: 2015-05-22 Impact factor: 6.343
Authors: Leah Iles; Heinz Pfluger; Lisa Lefkovits; Michelle J Butler; Peter M Kistler; David M Kaye; Andrew J Taylor Journal: J Am Coll Cardiol Date: 2011-02-15 Impact factor: 24.094
Authors: Arthur J Moss; Henry Greenberg; Robert B Case; Wojciech Zareba; W Jackson Hall; Mary W Brown; James P Daubert; Scott McNitt; Mark L Andrews; Adam D Elkin Journal: Circulation Date: 2004-12-06 Impact factor: 29.690
Authors: Alan Kadish; Alan Dyer; James P Daubert; Rebecca Quigg; N A Mark Estes; Kelley P Anderson; Hugh Calkins; David Hoch; Jeffrey Goldberger; Alaa Shalaby; William E Sanders; Andi Schaechter; Joseph H Levine Journal: N Engl J Med Date: 2004-05-20 Impact factor: 91.245
Authors: Eri Watanabe; Siddique A Abbasi; Bobak Heydari; Otavio R Coelho-Filho; Ravi Shah; Tomas G Neilan; Venkatesh L Murthy; François-Pierre Mongeon; Chirag Barbhaiya; Michael Jerosch-Herold; Ron Blankstein; Hiroto Hatabu; Robert J van der Geest; William G Stevenson; Raymond Y Kwong Journal: Circ Cardiovasc Imaging Date: 2014-10-06 Impact factor: 7.792
Authors: Einar Heiberg; Martin Ugander; Henrik Engblom; Matthias Götberg; Göran K Olivecrona; David Erlinge; Håkan Arheden Journal: Radiology Date: 2007-11-30 Impact factor: 11.105
Authors: Hussein Rayatzadeh; Alex Tan; Raymond H Chan; Shalin J Patel; Thomas H Hauser; Long Ngo; Jaime L Shaw; Susie N Hong; Peter Zimetbaum; Alfred E Buxton; Mark E Josephson; Warren J Manning; Reza Nezafat Journal: J Cardiovasc Magn Reson Date: 2013-04-10 Impact factor: 5.364
Authors: Henrik Engblom; Jane Tufvesson; Robert Jablonowski; Marcus Carlsson; Anthony H Aletras; Pavel Hoffmann; Alexis Jacquier; Frank Kober; Bernhard Metzler; David Erlinge; Dan Atar; Håkan Arheden; Einar Heiberg Journal: J Cardiovasc Magn Reson Date: 2016-05-04 Impact factor: 5.364
Authors: Giorgio Faganello; Aldostefano Porcari; Federico Biondi; Marco Merlo; Antonio De Luca; Giancarlo Vitrella; Manuel Belgrano; Lorenzo Pagnan; Andrea Di Lenarda; Gianfranco Sinagra Journal: J Cardiovasc Echogr Date: 2019 Jul-Sep
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: Dan M Popescu; Haley G Abramson; Rebecca Yu; Changxin Lai; Julie K Shade; Katherine C Wu; Mauro Maggioni; Natalia A Trayanova Journal: Cardiovasc Digit Health J Date: 2021-11-26
Authors: Susana Merino-Caviedes; Lilian K Gutierrez; José Manuel Alfonso-Almazán; Santiago Sanz-Estébanez; Lucilio Cordero-Grande; Jorge G Quintanilla; Javier Sánchez-González; Manuel Marina-Breysse; Carlos Galán-Arriola; Daniel Enríquez-Vázquez; Carlos Torres; Gonzalo Pizarro; Borja Ibáñez; Rafael Peinado; Jose Luis Merino; Julián Pérez-Villacastín; José Jalife; Mariña López-Yunta; Mariano Vázquez; Jazmín Aguado-Sierra; Juan José González-Ferrer; Nicasio Pérez-Castellano; Marcos Martín-Fernández; Carlos Alberola-López; David Filgueiras-Rama Journal: Sci Rep Date: 2021-09-28 Impact factor: 4.379