Thomas A Treibel1, Yaron Fridman2, Patrick Bering3, Aatif Sayeed3, Maren Maanja4, Fredrika Frojdh4, Louise Niklasson4, Eric Olausson4, Timothy C Wong2, Peter Kellman5, Christopher A Miller6, James C Moon1, Martin Ugander4, Erik B Schelbert7. 1. Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom; Institute for Cardiovascular Science, University College of London, London, United Kingdom. 2. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; UPMC Cardiovascular Magnetic Resonance Center, Pittsburgh, Pennsylvania; Heart and Vascular Institute, UPMC, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania. 3. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; UPMC Cardiovascular Magnetic Resonance Center, Pittsburgh, Pennsylvania. 4. Department of Clinical Physiology, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. 5. National Institutes of Health, Bethesda, Maryland. 6. Centre Division of Cardiovascular Sciences, University of Manchester and Manchester University National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom. 7. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; UPMC Cardiovascular Magnetic Resonance Center, Pittsburgh, Pennsylvania; Heart and Vascular Institute, UPMC, Pittsburgh, Pennsylvania; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: schelberteb@upmc.edu.
Abstract
OBJECTIVES: Because risk stratification data represents a key domain of biomarker validation, we compared associations between outcomes and various cardiovascular magnetic resonance (CMR) metrics quantifying myocardial fibrosis (MF) in noninfarcted myocardium: extracellular volume fraction (ECV), native T1, post-contrast T1, and partition coefficient. BACKGROUND: MF associates with vulnerability to adverse events (e.g., mortality and hospitalization for heart failure [HHF]), but investigators still debate its optimal measurement; most histological validation data show strongest ECV correlations with MF. METHODS: We enrolled 1,714 consecutive patients without amyloidosis or hypertrophic cardiomyopathy from a single CMR referral center serving an integrated healthcare network. We measured T1 (MOdified Look-Locker Inversion recovery [MOLLI]) in nonenhanced myocardium, averaged from 2 short-axis slices (basal and mid) before and 15 to 20 min after a gadolinium contrast bolus. We compared chi-square test values from CMR MF measures in univariable and multivariable Cox regression models. We assessed "dose-response" relationships in Kaplan-Meier curves using log-rank statistics for quartile strata. We also computed net reclassification improvement (NRI) and integrated discrimination improvement (IDI for Cox models with ECV vs. native T1). RESULTS: Over a median of 5.6 years, 374 events occurred after CMR (162 HHF events and 279 deaths, 67 with both). ECV yielded the best separation of Kaplan-Meier curves and the highest log-rank statistics. In univariable and multivariable models, ECV associated most strongly with outcomes, demonstrating the highest chi-square test values. Native T1 or post-contrast T1 did not associate with outcomes in the multivariable model. ECV provided added prognostic value to models with native T1, for example, in multivariable models IDI = 0.0037 (95% confidence interval [CI]: 0.0009 to 0.0071), p = 0.02; NRI = 0.151 (95% CI: 0.022 to 0.292), p = 0.04. CONCLUSIONS: Analogous to histological previously published validation data, ECV myocardial fibrosis measures exhibited more robust associations with outcomes than other surrogate CMR MF measures. Superior risk stratification by ECV supports claims that ECV optimally measures MF in noninfarcted myocardium.
OBJECTIVES: Because risk stratification data represents a key domain of biomarker validation, we compared associations between outcomes and various cardiovascular magnetic resonance (CMR) metrics quantifying myocardial fibrosis (MF) in noninfarcted myocardium: extracellular volume fraction (ECV), native T1, post-contrast T1, and partition coefficient. BACKGROUND: MF associates with vulnerability to adverse events (e.g., mortality and hospitalization for heart failure [HHF]), but investigators still debate its optimal measurement; most histological validation data show strongest ECV correlations with MF. METHODS: We enrolled 1,714 consecutive patients without amyloidosis or hypertrophic cardiomyopathy from a single CMR referral center serving an integrated healthcare network. We measured T1 (MOdified Look-Locker Inversion recovery [MOLLI]) in nonenhanced myocardium, averaged from 2 short-axis slices (basal and mid) before and 15 to 20 min after a gadolinium contrast bolus. We compared chi-square test values from CMR MF measures in univariable and multivariable Cox regression models. We assessed "dose-response" relationships in Kaplan-Meier curves using log-rank statistics for quartile strata. We also computed net reclassification improvement (NRI) and integrated discrimination improvement (IDI for Cox models with ECV vs. native T1). RESULTS: Over a median of 5.6 years, 374 events occurred after CMR (162 HHF events and 279 deaths, 67 with both). ECV yielded the best separation of Kaplan-Meier curves and the highest log-rank statistics. In univariable and multivariable models, ECV associated most strongly with outcomes, demonstrating the highest chi-square test values. Native T1 or post-contrast T1 did not associate with outcomes in the multivariable model. ECV provided added prognostic value to models with native T1, for example, in multivariable models IDI = 0.0037 (95% confidence interval [CI]: 0.0009 to 0.0071), p = 0.02; NRI = 0.151 (95% CI: 0.022 to 0.292), p = 0.04. CONCLUSIONS: Analogous to histological previously published validation data, ECV myocardial fibrosis measures exhibited more robust associations with outcomes than other surrogate CMR MF measures. Superior risk stratification by ECV supports claims that ECV optimally measures MF in noninfarcted myocardium.
Authors: Mateus D Marques; Raquel Weinberg; Shrey Kapoor; Mohammad R Ostovaneh; Yoko Kato; Chia Ying Liu; Steven Shea; Robyn L McClelland; Wendy S Post; David A Bluemke; João A C Lima; Bharath Ambale-Venkatesh Journal: Eur Heart J Cardiovasc Imaging Date: 2022-09-10 Impact factor: 9.130
Authors: Anke Busse; Rengarajan Rajagopal; Seyrani Yücel; Ebba Beller; Alper Öner; Felix Streckenbach; Daniel Cantré; Hüseyin Ince; Marc-André Weber; Felix G Meinel Journal: Radiologe Date: 2020-11 Impact factor: 0.635
Authors: Ayako Seno; Panagiotis Antiochos; Helena Lichtenfeld; Eva Rickers; Iqra Qamar; Yin Ge; Ron Blankstein; Michael Steigner; Ayaz Aghayev; Michael Jerosch-Herold; Raymond Y Kwong Journal: J Am Heart Assoc Date: 2022-01-13 Impact factor: 6.106
Authors: Katherine C Wu; Sabina A Haberlen; Michael W Plankey; Frank J Palella; Damani A Piggott; Gregory D Kirk; Joseph B Margolick; Wendy S Post Journal: Eur Heart J Cardiovasc Imaging Date: 2021-07-20 Impact factor: 6.875