Objective:
The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. The objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF.
Methods:
A multilead strategy based on periodic component analysis, to emphasize T-wave periodicity, is proposed for QT delineation. QT adaptation is modeled by a linear, time-invariant filter, which describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response.
Results:
Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes. In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49).
Conclusions:
Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged QT adaptation time lag is associated with a high risk for SCD. Significance: SCD risk markers originally developed for sinus rhythm can also be used in AF, provided that T-wave periodicity is emphasized. The time lag is a potentially useful biomarker for identifying patients at risk for SCD, guiding clinicians in adopting effective therapeutic decisions.
