QF3-08 - Displacement Encoding with Stimulated Echoes enables the identification of myocardial infarction transmurality early post myocardial infarction

Friday, February 14, 2020

3:11 PM – 3:14 PM

Location: Sabal/Sago (Meeting Room in Exhibit Hall)

Background: Segmental extent of infarction assessed by late gadolinium enhancement (LGE) imaging early post ST-elevation myocardial infarction (STEMI) has utility in predicting left ventricular functional recovery. Recently, extracellular volume (ECV) fraction has been reported as a superior biomarker of functional recovery than transmural extent of infarction. We hypothesized that segmental strain with Displacement Encoding with Stimulated Echoes (DENSE) would be a stronger predictor of infarct transmurality than feature-tracking strain, and non-inferior to extracellular volume fraction (ECV).

Methods: We prospectively enrolled participants with acute STEMI and an occluded infarct-related artery (Thrombolysis in Myocardial Infarction (TIMI) flow grade 0-1) on coronary angiography at a single centre. The study was publicly registered and had UK ethics approval. Participants gave informed consent. Cardiac magnetic resonance (CMR) imaging was undertaken at 1.5 Tesla. The protocol included cine imaging, 2D Spiral (DENSE), pre- and post-contrast T1 mapping (Modified Look-Locker Inversion recovery [MOLLI] sequence) and LGE imaging. Blood samples were obtained for blood hematocrit. The left ventricle was segmented using a 16-segment American Heart Association model. Segmental percentage LGE extent, presence of microvascular obstruction (MVO), circumferential and radial strain with DENSE and feature-tracking and T1 relaxation times and ECV fraction were derived. Statistical analysis was performed using R version 2.15 or higher.

Results: Fifty participants out of 62 who underwent CMR on day 1 post-STEMI were included in this analysis (mean ± SD, 58 ± 9 years, 39 (78%) male) (table 1, figure 1). Segmental circumferential strain with DENSE was identified as a good predictor of segmental extent of infarction >50% (AUC (95% CI), cut-off value = 0.87 (0.83, 0.91), -10.0%) similar to ECV (0.83 (0.78, 0.87), 36.5%)(p=0.117). DENSE circumferential strain was superior to feature-tracking circumferential strain (0.70(0.65, 0.76) (p< 0.001) (Table 2). For the detection of segmental infarction >75%, circumferential strain with DENSE (0.88 (0.83, 0.92), -9.9%) was non-inferior to ECV (0.81 (0.74, 0.88), 41.8%)(p=0.132) and superior to feature-tracking (0.74(0.67, 0.81), -13.1%)(p< 0.001). For the detection of MVO, DENSE (AUC = 0.84 (0.79, 0.88), -11.7%) was superior to ECV (AUC=0.76 (0.70,0.82,34.1%)(p=0.018) and to feature-trackingFT (AUC=0.66 (0.60, 0.73) -20.9%)(p< 0.001). Segmental radial strain with both techniques was a poor predictor of infarct characteristics (AUC < 0.5).

Conclusion: Circumferential strain with DENSE is a good predictor of infarct extent and may obviate the need for gadolinium contrast agents.

Presenting Author(s)

KM

Kenneth Mangion, MD, PhD

Lecturer in Cardiology and Honorary Cardiology Registrar
University of Glasgow

Disclosure: Disclosure information not submitted.

Primary Author(s)

KM

Kenneth Mangion, MD, PhD

Lecturer in Cardiology and Honorary Cardiology Registrar
University of Glasgow

Disclosure: Disclosure information not submitted.

Co-Author(s)

CL

Christopher M. Loughrey, PhD

University of Glasgow

Disclosure: Disclosure information not submitted.
Daniel Auger, PhD

Client Services Manager / Research Scientist
Medical Metrics, Inc. / University of Virginia

Disclosure: Disclosure information not submitted.
CM

Christie McComb, PhD, MSc

NHS Greater Glasgow and Clyde

Disclosure: Disclosure information not submitted.
ML

Matthew M. Lee

University of Glasgow

Disclosure: Disclosure information not submitted.
DC

David Corcoran, MD, PhD

Honorary Clinical Lecturer
University of Glasgow

Disclosure: Disclosure information not submitted.
MM

Margaret McEntegart, MD, PhD

Consultant Cardiologist
Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
AD

Andrew Davie, MD

Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
RG

Richard Good, MD, MA

Consultant Interventional Cardiologist
Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
ML

Mitchell Lindsay, MD

Consultant Interventional Cardiologist
Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
HE

Hany Eteiba, MD

Consultant Interventional Cardiologist
University of Glasgow/Golden Jubilee national hospital

Disclosure: Disclosure information not submitted.
JR

John P. Rocchiccioli, MD

University of Glasgow/Golden Jubilee national hospital

Disclosure: Disclosure information not submitted.
SW

Stuart Watkins, MD

Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
SH

Stuart Hood, MD

Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
AS

Aadil Shaukat, MD

Consultant Interventional Cardiologist
Golden Jubilee National Hospital

Disclosure: Disclosure information not submitted.
CH

Caroline E. Haig, PhD

University of Glasgow

Disclosure: Disclosure information not submitted.
Frederick Epstein, PhD

Mac Wade Professor
University of Virginia

Disclosure: Disclosure information not submitted.
Colin Berry, MD, PhD

Professor of Cardiology and Imaging
University of Glasgow

Disclosure: Abbott Vascular (Individual(s) Involved: Self, Products/Services: Diagnostic guidewires): Grant/Research Support, Received grants or funds in support of research of related company products., Speaker/Honoraria; AstraZeneca: Consulting Fees (e.g., advisory boards), Speaker/Honoraria; Heartflow: Consulting Fees (e.g., advisory boards); Siemens Healthcare (Individual(s) Involved: Self, Products/Services: Research imaging Pulse Sequences): Other Financial or Material Support