ArticleJACC. Cardiovascular imaging2020
CMR DENSE and the Seattle Heart Failure Model Inform Survival and Arrhythmia Risk After CRT.
Article in JACC. Cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Evaluating the predictive value of late gadolinium enhancement assessed by cardiac magnetic resonance on sudden cardiac death in patients selected for implantable cardioverter defibrillator and cardiac resynchronization therapy implantation: a systematic review and meta-analysis.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Pooled it
- Measuring Cardiac Dyssynchrony with DENSE (Displacement Encoding with Stimulated Echoes)-A Systematic Review.Reviews in cardiovascular medicine · 2023Pooled it
- The role of cardiac magnetic resonance in identifying appropriate candidates for cardiac resynchronization therapy - a systematic review of the literature.Heart failure reviews · 2022Pooled it
- Prioritizing Discovery and Advancements in Arrhythmia Therapies: NIH/NHLBI Workshop.JACC. Clinical electrophysiology · 2026Review
- TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.Magnetic resonance in medicine · 2025Article
- SCAR-AWARE LATE MECHANICAL ACTIVATION DETECTION NETWORK FOR OPTIMAL CARDIAC RESYNCHRONIZATION THERAPY PLANNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- DENSE-GUIDED DEEP MOTION NETWORKS ACCOUNTED BY LARGE ROTATIONS TO IMPROVE MYOCARDIAL STRAIN ANALYSIS FROM ROUTINE CINE MRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- On the development of diagnostic support algorithms based on CPET biosignals dataPeerJ. Computer science · 2025Article
- Myocardial Strain Measured by Cardiac Magnetic Resonance Predicts Cardiovascular Morbidity and Death.Journal of the American College of Cardiology · 2024Article
- Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.Computers in biology and medicine · 2024Article
- MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Correction of phase offset errors and quantification of background noise, signal-to-noise ratio, and encoded-displacement uncertainty on DENSE MRI for kinematics of the descending thoracic and abdominal aorta.Magnetic resonance imaging · 2024Article
- Noninvasive Electrical Mapping Compared with the Paced QRS Complex for Optimizing CRT Programmed Settings and Predicting Multidimensional Response.Journal of cardiovascular translational research · 2023Article
- StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE.Radiology. Cardiothoracic imaging · 2023Article
- Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.Magnetic resonance in medicine · 2023Article
- First-in-human noninvasive left ventricular ultrasound pacing: A potential screening tool for cardiac resynchronization therapy.Heart rhythm O2 · 2023Article
- Machine learning for multidimensional response and survival after cardiac resynchronization therapy using features from cardiac magnetic resonance.Heart rhythm O2 · 2022Article
- Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data.BioData mining · 2022Article
- Reproducibility of global and segmental myocardial strain using cine DENSE at 3 T: a multicenter cardiovascular magnetic resonance study in healthy subjects and patients with heart disease.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications.Applied sciences (Basel, Switzerland) · 2022Article
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18 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesThis 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.
backgroundThe 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.
methodsCMR 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.
resultsThe 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).
conclusionsA 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.
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