Data mining to improve detection of arrhythmogenic T-wave alternans.

摘要

One of the major unresolved challenges in our field is to develop and apply clinical paradigms that allow better prediction and prevention of sudden cardiac death (SCD).1 Microvolt-level T-wave alternans (MTWA) continues to have considerable appeal as an SCD risk marker because it is a reflection of arrhythmia substrate rather than overall progression of disease.2'3 This is key to the clinical application of risk markers because treatment directed against ventricular fibrillation, as with implantable defibrillators, will only mitigate SCD risks, and can actually amplify risk for non-SCD risk such as progression of heart failure.4 Moreover, MTWA was shown to possess greater predictive accuracy when compared head-to-head with other risk markers. Although several methodological approaches have been proposed for measuring MTWA, the so-called spectral method applied during controlled heart-rate-based exercise testing is the only method that has been rigorously standardized with consistent exercise protocols, standard rules for electrocardiographic (ECG) lead sets, and cut-points for test positivity applied prospectively and repeatedly in numerous clinical trials.