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  • Open Access

Prediction of appropriate ICD-therapy using infarct heterogeneity from CMR in patients with coronary artery disease

  • 1,
  • 3,
  • 1,
  • 1,
  • 1, 2,
  • 3 and
  • 1
Journal of Cardiovascular Magnetic Resonance201517 (Suppl 1) :P165

https://doi.org/10.1186/1532-429X-17-S1-P165

  • Published:

Keywords

  • Cardiac Magnetic Resonance
  • Late Gadolinium Enhancement
  • Cardiac Magnetic Resonance Imaging
  • Improve Patient Selection
  • Threshold Algorithm

Background

The heterogeneous peri-infarction zone surrounding the core infarct with cardiac magnetic resonance imaging (CMR) late gadolinium enhancement (LGE) has been linked to all-cause mortality in patients with coronary artery disease. Previously, the heterogeneity of fibrotic areas has been analyzed by threshold algorithms. We hypothesized that the heterogeneous peri-infarction zone is related to appropriate ICD-therapy in ischemic cardiomyopathy patients. Therefore, the purpose of this study was to investigate if 1) infarct heterogeneity can predict appropriate ICD-therapy and 2) evaluate which analysis method best depicts and quantifies the peri-infarction zone.

Methods

Ischemic cardiomyopathy patients with a primary prophylactic ICD who underwent CMR on a 1.5T scanner prior to ICD implantation were retrospectively included and divided into two groups (i) patients with appropriate ICD-therapy (anti-tachy pacing, shock or both) and (ii) patients with no ICD-therapy. A newly developed semi-automatic quantitative algorithm was used to evaluate the peri-infarction zone. This method was compared against a previously used threshold method with the total scar area defined as signal intensity (SI)>2SD from remote myocardium, infarct core as SI>3SD from remote and the peri-infarction zone defined as SI between 2 and 3SD from remote (Figure 1). Differences with a p<0.05 were considered statistically significant.
Figure 1
Figure 1

Representative short axis LGE-CMR images from one patient evaluated for peri-infarction zone with the semi-automatic algorithm (left panel) and threshold algorithm (right panel). The peri-infarction zone is defined as the area between the pink (infarct core) and yellow line. Red line=endocardium, green line=epicardium.

Results

A total of 14 patients were included in the analysis, six patients with appropriate ICD-therapy (age 53±11years, 100% male, LV-EF 29±9%) and eight patients with no ICD-therapy (age 55±14years, 100% male, LV-EF 26±4%). The total scar burden was similar between both groups with and without ICD-therapy (49±13g vs 45±8g, p=0.1).

The mean peri-infarction zone normalized to the total scar using the semi-quantitative algorithm was larger in the group with appropriate therapy (34±1%) compared to the group with no ICD-therapy (30±1%, p=0.03), Figure 2. There was no difference between groups using the threshold algorithm for peri-infarction zone analysis (11±2% with appropriate ICD-therapy vs 10±2% with no therapy, p=0.4). There was a significant difference in peri-infarction zone normalized for total scar between the semi-automatic and threshold algorithm for patients with appropriate therapy (p=0.002) and no therapy (p=0.0003).
Figure 2
Figure 2

Quantification of peri-infarction zone normalized to total scar in patients with and without appropriate ICD-therapy using a semi-automatic algorithm (white columns) and a threshold algorithm based on standard deviations (black columns). The peri-infarction zone is significantly larger in patients with appropriate therapy compared to patients without therapy using the semi-automatic algorithm.

Conclusions

The peri-infarction zone quantified on CMR using a semi-automatic algorithm was larger in patients with appropriate ICD-therapy compared to patients with no ICD-therapy. The use of a threshold algorithm did, however, not separate the groups. Accurate quantification and characterization of the peri-infarction zone could aid in the identification of patients with infarction and at risk of ventricular arrhythmias and help to improve patient selection for primary prevention with ICD-therapy.

Funding

Lund University, Region of Scania.

Authors’ Affiliations

(1)
Cardiac MR group Lund, Dept of Clinical Physiology, Lund University, Lund, Sweden
(2)
Dept. of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden
(3)
Dept. of Cardiology, Arrhytmia Clinic, Skane University Hospital, Lund University, Lund, Sweden

Copyright

© Jablonowski et al; licensee BioMed Central Ltd. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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