Myocardial viability evaluation using magnetocardiography in patients with coronary artery disease

OBJECTIVEMagnetocardiography (MCG) has been used to risk stratify patients in terms of sudden death or to detect ischemia. We evaluated the potential of this technique to assess myocardial viability in coronary artery disease. METHODSFifteen patients aged 36–75 (median, 59) years with stable single-...

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Veröffentlicht in:Coronary artery disease 2004-05, Vol.15 (3), p.155-162
Hauptverfasser: Morguet, Andreas J, Behrens, Steffen, Kosch, Olaf, Lange, Christine, Zabel, Markus, Selbig, Daniela, Munz, Dieter L, Schultheiss, Heinz-Peter, Koch, Hans
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Sprache:eng
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Zusammenfassung:OBJECTIVEMagnetocardiography (MCG) has been used to risk stratify patients in terms of sudden death or to detect ischemia. We evaluated the potential of this technique to assess myocardial viability in coronary artery disease. METHODSFifteen patients aged 36–75 (median, 59) years with stable single-vessel disease (≥70% diameter stenosis) and corresponding regional wall-motion abnormality underwent (1) echocardiography to evaluate wall motion, (2) Tl dipyridamole single-photon emission computed tomography to document perfusion and (3) quantitative F-fluorodeoxyglucose positron emission tomography to assess viability in 16 left-ventricular wall segments. MCG was performed in each patient using a shielded prototype 49-channel low-temperature superconducting quantum interference device (SQUID) system. Multiple time and area parameters were extracted automatically from each baseline-corrected data set. RESULTSEleven patients had prior myocardial infarction. In each patient, four to 12 (median, seven) segments were lesion dependent, totalling up to 117 out of 240 segments. A total of 88 segments (75%) were viable and 29 segments (25%) represented scar. Patients were divided into three categories(a) no scar segments (five patients), (b) scar in one to three segments (six patients) and (c) scar in ≥ four segments (four patients). The three MCG parameters with the best selectivity were identified using linear discriminant analysis with forward inclusion (P
ISSN:0954-6928
1473-5830
DOI:10.1097/00019501-200405000-00004