Range Of RR Intervals Research Articles

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals.

BackgroundPrediction of End-Systole time is of utmost importance for cardiac MRI to correctly associate acquired k-space lines during reconstruction of cine acquisitions. This prediction is usually based on the patient’s heart rate using Weissler’s formula, which was calibrated by linear regression within a population and cannot account for individual variability.ObjectiveWe propose an automatic method to build a personalized model that better predicts end-systole.MethodsA phase contrast sequence was modified to acquire only central k-space line with 6.6ms temporal resolution, in a slice passing through the aorta during 128 heartbeats in 35 subjects. Segmentation of aorta and detection of end of systolic ejection was automatic. Duration of electromechanical systole duration as function of heart rate was determined for each subject separately.ResultsIn comparison with the global models, the adapted cardiac model predicted significantly better both echocardiographic end-systolic reference (bias = 0ms vs 17ms, p<0.001) and MRI measurements (bias = 6.8ms vs 17ms). Favorable impact was shown on the cine reconstruction of the 5 subjects with the higher cardiac variability (p = 0.02).ConclusionsPersonalization of cardiac model to the subject is feasible in MRI and reduces the error of prediction of systole.

“Zone of avoidance”: RR interval distribution in tachograms, histograms, and Poincaré plots of a Boxer dog

The RR intervals of sinus and ventricular beats were determined by analysis of a 24-h ambulatory electrocardiogram in a Boxer before and after treatment with sotalol. These RR intervals were plotted using tachograms, histograms, and Poincaré plots. The tachogram demonstrated a ‘band’ wherein a range of RR intervals was infrequent, the histogram did not take the form of a single Gaussian distribution of RR intervals, and the Poincaré plot showed nonhomogeneous beat-to-beat variability. This type of patterning was described as a “zone of avoidance” potentially caused by the clustering of beats within specific ranges. Treatment with sotalol enhanced the “zone of avoidance”. Further investigation is needed to understand the mechanism for this observation as well as any clinical implications.

Spontaneous changes in ventricular tachycardia cycle length

Understanding spontaneous fluctuations in ventricular tachycardia cycle length is required to develop algorithms for ventricular tachycardia detection and termination. Variations in cycle length, time to stable cycle length and the range of RR intervals during ventricular tachycardia were analyzed in 74 episodes of sustained monomorphic ventricular tachycardia induced in patients not taking antiarrhythmic medication. Linear regression demonstrated cycle length variability to decrease over time (41 +/- 24 to 17 +/- 19 ms, p less than 0.001). Slower ventricular tachycardia had more cycle length variability than faster ventricular tachycardia (p less than 0.001). Ventricular tachycardia that was initially more variable tended to remain more variable (p less than 0.001). Fifty-four percent of episodes stabilized within the first 15 beats, 75% by 30 beats and 93% by 50 beats. The number of beats to stable cycle length was independent of ventricular tachycardia rate. The average range in cycle length per episode was 127 +/- 72 ms; 12% of ventricular tachycardia episodes varied by less than 50 ms and 45% by less than 150 ms. The maximal range in RR intervals from a single episode of ventricular tachycardia was 290 ms. Therefore, ventricular tachycardia demonstrates a wide range of cycle lengths and has time-dependent changes in variability and stability. These cycle length changes should be considered in the algorithms for ventricular tachycardia detection and termination by automatic antitachycardia devices.