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Magnetocardiography in the Evaluation of Sudden Cardiac Death Risk: A Systematic Review
ABSTRACT Sudden cardiac death (SCD) is responsible for 15%–20% of deaths globally/year, predominantly due to ventricular arrhythmias (VA) caused by vulnerable cardiac substrate. Identifying those at risk has proved difficult with several limitations of current methods. We evaluated the evidence for...
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Published in: | Annals of noninvasive electrocardiology 2024-11, Vol.29 (6), p.e70028-n/a |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | ABSTRACT
Sudden cardiac death (SCD) is responsible for 15%–20% of deaths globally/year, predominantly due to ventricular arrhythmias (VA) caused by vulnerable cardiac substrate. Identifying those at risk has proved difficult with several limitations of current methods. We evaluated the evidence for magnetocardiography (MCG) in predicting SCD events. We searched Embase/Medline databases for English language papers evaluating MCG in patients at risk of VA. A total of 119 papers were screened with 27 papers included for analysis (23 case–controlled, four cohort studies); study sizes varied (n = 12 to 158). Etiology was ischemic cardiomyopathy (ICM) in 22, dilated cardiomyopathy in 2, arrhythmogenic cardiomyopathy in 1 and mixed in 2. In patients with ICM there were consistent discriminatory features seen using time‐based and signal‐complexity measures that persisted when evaluating the independence of these parameters. Current flow analysis demonstrated promising discriminatory results in other etiologies. The features studied support the role of MCG in identifying substrate for VA, particularly in ICM.
Sudden cardiac death is responsible for 15%–20% of deaths globally/year, predominantly due to VA caused by vulnerable cardiac substrate. Current risk predictors of SCD based on ECG and LV function parameters have significant limitations. Magnetocardiography offers hope for better risk‐prediction, and we evaluated the evidence base for this technology in this review. |
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ISSN: | 1082-720X 1542-474X 1542-474X |
DOI: | 10.1111/anec.70028 |