Novel frequency analysis of signal‐averaged electrocardiograms is predictive of adverse outcomes in implantable cardioverter defibrillator patients. Issue 3 (28th January 2019) - Chow, Ryan; Hashemi, Javad; Torbey, Sami; Siu, Johnny; Glover, Benedict; Baranchuk, Adrian M.; Abdollah, Hoshiar; Simpson, Christopher; Akl, Selim; Redfearn, Damian P.
Abstract: Background: Current noninvasive risk stratification methods offer limited prediction of arrhythmic events when selecting patients for ICD implantation. Our laboratory has recently developed a signal processing metric called Layered Symbolic Decomposition frequency (LSDf) that quantifies the percentage of hidden QRS wave frequency components in signal‐averaged ECG (SAECG) recordings. The purpose of this pilot study was to determine whether LSDf can be predictive of ventricular arrhythmia or death in an ICD patient cohort. Methods and Results: Fifty‐two ICD patients were recruited from 2008 to 2009. These were followed for a mean of 8.5 ± 0.4 years for the primary outcome of first appropriately treated ventricular arrhythmia (VT/VF) or death. Thirty‐four subjects met the primary outcome. LSDf was significantly lower, and 12‐lead QRS duration was significantly greater in patients meeting the primary outcome (12.14 ± 3.97% vs. 16.45 ± 3.73%; p  = 0.001) and (111.59 ± 14.96 ms vs. 97.69 ± 13.51 ms; p  = 0.012) respectively. A 13.25% LSDf threshold (0.74 sensitivity and 0.85 specificity) was selected based on an ROC curve. Kaplan–Meier survival analysis was conducted; patients above the 13.25% threshold demonstrated significantly better survival outcomes (log‐rank p  < 0.001). In Cox multivariate regression analysis, the LSDf threshold (13.25%) was compared to LVEF (28.5%), 12‐lead QRSd (100 ms), age, % male sex, NYHA classification, and antiarrhythmic usage. LSDf was a predictor of the primary outcome ( p  = 0.005) and an independent predictor for solely ventricular arrhythmia ( p  = 0.002). Conclusion: Layered Symbolic Decomposition frequency analysis in SAECG recordings may be a viable predictor of negative ICD survival outcomes.