Extracellular Volume Associates With Outcomes More Strongly Than Native or Post-Contrast Myocardial T1

doi:10.1016/j.jcmg.2019.03.017

JACC: Cardiovascular Imaging

Volume 13, Issue 1, Part 1, January 2020, Pages 44-54

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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.

Central Illustration

Key Words

cardiovascular magnetic resonance

extracellular matrix

extracellular volume fraction

myocardial fibrosis

T1 mapping

Abbreviations and Acronyms

CMR

cardiovascular magnetic resonance

CVF

collagen volume fraction

confidence interval

ECV

extracellular volume fraction

left ventricular ejection fraction

gadolinium

hazard ratio

HHF

hospitalization for heart failure

IDI

integrated discrimination improvement

LGE

late gadolinium enhancement

myocardial fibrosis

myocardial infarction

MOLLI

MOdified Look-Locker Inversion recovery

NRI

net reclassification improvement

PSIR

phase sensitive inversion recovery

quartile

Cited by (0)

: This work was supported by a grant from The Pittsburgh Foundation (PA), Grant M2009-0068; and an American Heart Association Scientist Development grant (09SDG2180083) including a T. Franklin Williams Scholarship Award; funding provided by the following: Atlantic Philanthropies, Inc., the John A. Hartford Foundation, the Association of Specialty Professors, and the American Heart Association (Dallas, Texas). This work was also supported by Grant Number UL1 RR024153 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. Dr. Schelbert has accepted contrast material from Bracco Diagnostics for research purposes beyond the scope of this work; and has served on the Advisory Board of Merck and Bayer. Dr. Wong was supported by a grant K12 HS19461-01 from the Agency for Healthcare Research and Quality. Dr. Treibel is supported by a clinical lecturer grant by the National Institute of Health Research (NIHR). Prof. Moon is directly and indirectly supported by the UCLH NIHR Biomedical Research Centre and Biomedical Research Unit at UCLH and Barts, respectively. Dr. Miller is funded by a Clinician Scientist Award (CS-2015-15-003) from the National Institute for Health Research; and has received research support from Roche and Guerbet. Dr. Ugander has a research agreement regarding cardiac MRI with Siemens. Dr. Maanja is affiliated with Karolinska University Hospital, which has a research and development agreement with Siemens regarding cardiovascular magnetic resonance imaging. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.