- Poster presentation
- Open Access
Detection and prevalence of myocardial infarction early and late after heart transplantation detected by late gadolinium enhanced MRI
© Steen et al; licensee BioMed Central Ltd. 2012
- Published: 1 February 2012
- Myocardial Infarction
- Philips Medical System
- Basal Segment
- Tissue Characterisation
- Gadolinium Contrast
As recently shown, non-invasive late gadolinium contrast enhanced MRI (LGE-CMR) is able to detect myocardial infarction (MI) typical patterns in patients after heart transplant (HTX). Patho-physiologically, the underlying reason is an accelerated vascular immuno-atherosclerosis, the so-called transplant coronary artery disease (TCAD) which limits long-term survival of HTX pts. To date, there is only scarce data on the detection of early infarctions after HTX, the time course and the effects on myocardial function.
We hypothesized that LGE-CMR could detect TCAD-related MI in pts already early after the HTX procedure.
123 patients (pts) were divided into group I (62 pts; HTX operation<2ys) and group II (61 pts; HTX operation>2ys). LGE-CMR (Gadolinium:0.2mmol/kg bw) was performed on a 1.5T Whole Body MRI scanner (Philips Medical Systems) and analysed blindly by two experienced observers. For anatomic LGE description, hearts were divided according to the 17-segment model. Areas of infarct-typical LGE patterns were defined as sub-endocardial LGE patterns of various transmurality and were quantified by delineation of hyper-enhanced areas related to the myocardial mass on LGE images (relative infarct size). Groups were compared using ANOVA. P-values ≤ 0.05 were considered statistically significant.
LGE-CMR is a novel and sensitive imaging technique to detect infarct-typical MI in TCAD patients early and late after HTX. Unexpectedly, even in patients less than two years after the operation, there is already a noticeable prevalence of MI (21% of patients). These findings could have potential impact on a modified HTX patient risk stratification including LGE-CMR for tissue characterisation and detection of unknown MI.
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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.