CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT

Kenneth C. Bilchick; Daniel A. Auger; Mohammad Abdishektaei; Roshin Mathew; Min Woong Sohn; Xiaoying Cai; Changyu Sun; Aditya Narayan; Rohit Malhotra; Andrew Darby; J. Michael Mangrum; Nishaki Mehta; John Ferguson; Sula Mazimba; Pamela K. Mason; Christopher M. Kramer; Wayne C. Levy; Frederick H. Epstein

doi:10.1016/j.jcmg.2019.10.017

CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT

Kenneth C. Bilchick, Daniel A. Auger, Mohammad Abdishektaei, Roshin Mathew, Min Woong Sohn, Xiaoying Cai, Changyu Sun, Aditya Narayan, Rohit Malhotra, Andrew Darby, J. Michael Mangrum, Nishaki Mehta, John Ferguson, Sula Mazimba, Pamela K. Mason, Christopher M. Kramer, Wayne C. Levy, Frederick H. Epstein

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Objectives: This study sought to determine if combining the Seattle Heart Failure Model (SHFM-D) and cardiac magnetic resonance (CMR) provides complementary prognostic data for patients with cardiac resynchronization therapy (CRT) defibrillators. Background: The SHFM-D is among the most widely used risk stratification models for overall survival in patients with heart failure and implantable cardioverter-defibrillators (ICDs), and CMR provides highly detailed information regarding cardiac structure and function. Methods: CMR Displacement Encoding with Stimulated Echoes (DENSE) strain imaging was used to generate the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) circumferential strain dyssynchrony parameter, and the SHFM-D was determined from clinical parameters. Multivariable Cox proportional hazards regression was used to determine adjusted hazard ratios and time-dependent areas under the curve for the primary endpoint of death, heart transplantation, left ventricular assist device, or appropriate ICD therapies. Results: The cohort consisted of 100 patients (65.5 [interquartile range 57.7 to 72.7] years; 29% female), of whom 47% had the primary clinical endpoint and 18% had appropriate ICD therapies during a median follow-up of 5.3 years. CURE-SVD and the SHFM-D were independently associated with the primary endpoint (SHFM-D: hazard ratio: 1.47/SD; 95% confidence interval: 1.06 to 2.03; p = 0.02) (CURE-SVD: hazard ratio: 1.54/SD; 95% confidence interval: 1.12 to 2.11; p = 0.009). Furthermore, a favorable prognostic group (Group A, with CURE-SVD <0.60 and SHFM-D <0.70) comprising approximately one-third of the patients had a very low rate of appropriate ICD therapies (1.5% per year) and a greater (90%) 4-year survival compared with Group B (CURE-SVD ≥0.60 or SHFM-D ≥0.70) patients (p = 0.02). CURE-SVD with DENSE had a stronger correlation with CRT response (r = −0.57; p < 0.0001) than CURE-SVD with feature tracking (r = −0.28; p = 0.004). Conclusions: A combined approach to risk stratification using CMR DENSE strain imaging and a widely used clinical risk model, the SHFM-D, proved to be effective in this cohort of patients referred for CRT defibrillators. The combined use of CMR and clinical risk models represents a promising and novel paradigm to inform prognosis and device selection in the future.

Original language	English
Pages (from-to)	924-936
Number of pages	13
Journal	JACC: Cardiovascular Imaging
Volume	13
Issue number	4
DOIs	https://doi.org/10.1016/j.jcmg.2019.10.017
State	Published - Apr 2020

Bibliographical note

Publisher Copyright:
© 2020 American College of Cardiology Foundation

Funding

This work was supported by the National Institutes of Health (R56 HL135556, Dr. Bilchick is Principal Investigator). Dr. Bilchick has received research grant support from Medtronic and Siemens Healthineers. Dr. Malhotra has received research grant support from Biosense Webster and Medtronic. Dr. Darby has received grant support from Medtronic and Biosense Webster. Dr. Mangrum has received research grant support from Boston Scientific, CardioFocus, and St. Jude Medical. Dr. Ferguson has received support from Biosense Webster. Dr. Mason is on the steering committee for Medtronic; and has received grant support from Medtronic, Boston Scientific, and Cook Medical. Drs. Kramer and Epstein have received research grant support from Siemens Healthineers. Dr. Levy is a consultant to Medtronic and Impulse Dynamics; a steering Committee member for ADMIRE ICD (GE Healthcare) and Respircardia Inc.; and is a clinical endpoint committee member for the CHAMPION Post Approval Study (CardioMEMS, Abbott, Baim Institute), SOLVE CRT (EBR Systems, Inc.). The University of Washington CoMotion holds the copyright to the Seattle Heart Failure Model. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Victor Ferrari, MD, served as Guest Editor for this paper. This work was supported by the National Institutes of Health (R56 HL135556, Dr. Bilchick is Principal Investigator). Dr. Bilchick has received research grant support from Medtronic and Siemens Healthineers. Dr. Malhotra has received research grant support from Biosense Webster and Medtronic. Dr. Darby has received grant support from Medtronic and Biosense Webster. Dr. Mangrum has received research grant support from Boston Scientific, CardioFocus, and St. Jude Medical. Dr. Ferguson has received support from Biosense Webster. Dr. Mason is on the steering committee for Medtronic; and has received grant support from Medtronic, Boston Scientific, and Cook Medical. Drs. Kramer and Epstein have received research grant support from Siemens Healthineers. Dr. Levy is a consultant to Medtronic and Impulse Dynamics; a steering Committee member for ADMIRE ICD (GE Healthcare) and Respircardia Inc.; and is a clinical endpoint committee member for the CHAMPION Post Approval Study (CardioMEMS, Abbott, Baim Institute), SOLVE CRT (EBR Systems, Inc.). The University of Washington CoMotion holds the copyright to the Seattle Heart Failure Model. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Victor Ferrari, MD, served as Guest Editor for this paper.

Funders	Funder number
EBR Systems, Inc.
Respircardia Inc.
National Institutes of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)	R56HL135556
Medtronic
St. Jude Medical Center
Biosense Webster
Boston Scientific Neuromodulation Corporation
CoMotion, University of Washington
Siemens Medical Solutions USA

Keywords

cardiac magnetic resonance
cardiac resynchronization therapy
heart failure
implantable cardioverter-defibrillator
risk models

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging
Cardiology and Cardiovascular Medicine

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10.1016/j.jcmg.2019.10.017

Cite this

Bilchick, K. C., Auger, D. A., Abdishektaei, M., Mathew, R., Sohn, M. W., Cai, X., Sun, C., Narayan, A., Malhotra, R., Darby, A., Mangrum, J. M., Mehta, N., Ferguson, J., Mazimba, S., Mason, P. K., Kramer, C. M., Levy, W. C., & Epstein, F. H. (2020). CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT. JACC: Cardiovascular Imaging, 13(4), 924-936. https://doi.org/10.1016/j.jcmg.2019.10.017

@article{c3ff5b16597041ddb319c2f70d671e3b,

title = "CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT",

abstract = "Objectives: This study sought to determine if combining the Seattle Heart Failure Model (SHFM-D) and cardiac magnetic resonance (CMR) provides complementary prognostic data for patients with cardiac resynchronization therapy (CRT) defibrillators. Background: The SHFM-D is among the most widely used risk stratification models for overall survival in patients with heart failure and implantable cardioverter-defibrillators (ICDs), and CMR provides highly detailed information regarding cardiac structure and function. Methods: CMR Displacement Encoding with Stimulated Echoes (DENSE) strain imaging was used to generate the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) circumferential strain dyssynchrony parameter, and the SHFM-D was determined from clinical parameters. Multivariable Cox proportional hazards regression was used to determine adjusted hazard ratios and time-dependent areas under the curve for the primary endpoint of death, heart transplantation, left ventricular assist device, or appropriate ICD therapies. Results: The cohort consisted of 100 patients (65.5 [interquartile range 57.7 to 72.7] years; 29\% female), of whom 47\% had the primary clinical endpoint and 18\% had appropriate ICD therapies during a median follow-up of 5.3 years. CURE-SVD and the SHFM-D were independently associated with the primary endpoint (SHFM-D: hazard ratio: 1.47/SD; 95\% confidence interval: 1.06 to 2.03; p = 0.02) (CURE-SVD: hazard ratio: 1.54/SD; 95\% confidence interval: 1.12 to 2.11; p = 0.009). Furthermore, a favorable prognostic group (Group A, with CURE-SVD <0.60 and SHFM-D <0.70) comprising approximately one-third of the patients had a very low rate of appropriate ICD therapies (1.5\% per year) and a greater (90\%) 4-year survival compared with Group B (CURE-SVD ≥0.60 or SHFM-D ≥0.70) patients (p = 0.02). CURE-SVD with DENSE had a stronger correlation with CRT response (r = −0.57; p < 0.0001) than CURE-SVD with feature tracking (r = −0.28; p = 0.004). Conclusions: A combined approach to risk stratification using CMR DENSE strain imaging and a widely used clinical risk model, the SHFM-D, proved to be effective in this cohort of patients referred for CRT defibrillators. The combined use of CMR and clinical risk models represents a promising and novel paradigm to inform prognosis and device selection in the future.",

keywords = "cardiac magnetic resonance, cardiac resynchronization therapy, heart failure, implantable cardioverter-defibrillator, risk models",

author = "Bilchick, \{Kenneth C.\} and Auger, \{Daniel A.\} and Mohammad Abdishektaei and Roshin Mathew and Sohn, \{Min Woong\} and Xiaoying Cai and Changyu Sun and Aditya Narayan and Rohit Malhotra and Andrew Darby and Mangrum, \{J. Michael\} and Nishaki Mehta and John Ferguson and Sula Mazimba and Mason, \{Pamela K.\} and Kramer, \{Christopher M.\} and Levy, \{Wayne C.\} and Epstein, \{Frederick H.\}",

note = "Publisher Copyright: {\textcopyright} 2020 American College of Cardiology Foundation",

year = "2020",

month = apr,

doi = "10.1016/j.jcmg.2019.10.017",

language = "English",

volume = "13",

pages = "924--936",

number = "4",

}

Bilchick, KC, Auger, DA, Abdishektaei, M, Mathew, R, Sohn, MW, Cai, X, Sun, C, Narayan, A, Malhotra, R, Darby, A, Mangrum, JM, Mehta, N, Ferguson, J, Mazimba, S, Mason, PK, Kramer, CM, Levy, WC & Epstein, FH 2020, 'CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT', JACC: Cardiovascular Imaging, vol. 13, no. 4, pp. 924-936. https://doi.org/10.1016/j.jcmg.2019.10.017

TY - JOUR

T1 - CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT

AU - Bilchick, Kenneth C.

AU - Auger, Daniel A.

AU - Abdishektaei, Mohammad

AU - Mathew, Roshin

AU - Sohn, Min Woong

AU - Cai, Xiaoying

AU - Sun, Changyu

AU - Narayan, Aditya

AU - Malhotra, Rohit

AU - Darby, Andrew

AU - Mangrum, J. Michael

AU - Mehta, Nishaki

AU - Ferguson, John

AU - Mazimba, Sula

AU - Mason, Pamela K.

AU - Kramer, Christopher M.

AU - Levy, Wayne C.

AU - Epstein, Frederick H.

PY - 2020/4

Y1 - 2020/4

N2 - Objectives: This study sought to determine if combining the Seattle Heart Failure Model (SHFM-D) and cardiac magnetic resonance (CMR) provides complementary prognostic data for patients with cardiac resynchronization therapy (CRT) defibrillators. Background: The SHFM-D is among the most widely used risk stratification models for overall survival in patients with heart failure and implantable cardioverter-defibrillators (ICDs), and CMR provides highly detailed information regarding cardiac structure and function. Methods: CMR Displacement Encoding with Stimulated Echoes (DENSE) strain imaging was used to generate the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) circumferential strain dyssynchrony parameter, and the SHFM-D was determined from clinical parameters. Multivariable Cox proportional hazards regression was used to determine adjusted hazard ratios and time-dependent areas under the curve for the primary endpoint of death, heart transplantation, left ventricular assist device, or appropriate ICD therapies. Results: The cohort consisted of 100 patients (65.5 [interquartile range 57.7 to 72.7] years; 29% female), of whom 47% had the primary clinical endpoint and 18% had appropriate ICD therapies during a median follow-up of 5.3 years. CURE-SVD and the SHFM-D were independently associated with the primary endpoint (SHFM-D: hazard ratio: 1.47/SD; 95% confidence interval: 1.06 to 2.03; p = 0.02) (CURE-SVD: hazard ratio: 1.54/SD; 95% confidence interval: 1.12 to 2.11; p = 0.009). Furthermore, a favorable prognostic group (Group A, with CURE-SVD <0.60 and SHFM-D <0.70) comprising approximately one-third of the patients had a very low rate of appropriate ICD therapies (1.5% per year) and a greater (90%) 4-year survival compared with Group B (CURE-SVD ≥0.60 or SHFM-D ≥0.70) patients (p = 0.02). CURE-SVD with DENSE had a stronger correlation with CRT response (r = −0.57; p < 0.0001) than CURE-SVD with feature tracking (r = −0.28; p = 0.004). Conclusions: A combined approach to risk stratification using CMR DENSE strain imaging and a widely used clinical risk model, the SHFM-D, proved to be effective in this cohort of patients referred for CRT defibrillators. The combined use of CMR and clinical risk models represents a promising and novel paradigm to inform prognosis and device selection in the future.

AB - Objectives: This study sought to determine if combining the Seattle Heart Failure Model (SHFM-D) and cardiac magnetic resonance (CMR) provides complementary prognostic data for patients with cardiac resynchronization therapy (CRT) defibrillators. Background: The SHFM-D is among the most widely used risk stratification models for overall survival in patients with heart failure and implantable cardioverter-defibrillators (ICDs), and CMR provides highly detailed information regarding cardiac structure and function. Methods: CMR Displacement Encoding with Stimulated Echoes (DENSE) strain imaging was used to generate the circumferential uniformity ratio estimate with singular value decomposition (CURE-SVD) circumferential strain dyssynchrony parameter, and the SHFM-D was determined from clinical parameters. Multivariable Cox proportional hazards regression was used to determine adjusted hazard ratios and time-dependent areas under the curve for the primary endpoint of death, heart transplantation, left ventricular assist device, or appropriate ICD therapies. Results: The cohort consisted of 100 patients (65.5 [interquartile range 57.7 to 72.7] years; 29% female), of whom 47% had the primary clinical endpoint and 18% had appropriate ICD therapies during a median follow-up of 5.3 years. CURE-SVD and the SHFM-D were independently associated with the primary endpoint (SHFM-D: hazard ratio: 1.47/SD; 95% confidence interval: 1.06 to 2.03; p = 0.02) (CURE-SVD: hazard ratio: 1.54/SD; 95% confidence interval: 1.12 to 2.11; p = 0.009). Furthermore, a favorable prognostic group (Group A, with CURE-SVD <0.60 and SHFM-D <0.70) comprising approximately one-third of the patients had a very low rate of appropriate ICD therapies (1.5% per year) and a greater (90%) 4-year survival compared with Group B (CURE-SVD ≥0.60 or SHFM-D ≥0.70) patients (p = 0.02). CURE-SVD with DENSE had a stronger correlation with CRT response (r = −0.57; p < 0.0001) than CURE-SVD with feature tracking (r = −0.28; p = 0.004). Conclusions: A combined approach to risk stratification using CMR DENSE strain imaging and a widely used clinical risk model, the SHFM-D, proved to be effective in this cohort of patients referred for CRT defibrillators. The combined use of CMR and clinical risk models represents a promising and novel paradigm to inform prognosis and device selection in the future.

KW - cardiac magnetic resonance

KW - cardiac resynchronization therapy

KW - heart failure

KW - implantable cardioverter-defibrillator

KW - risk models

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CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT

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