Beat-to-beat QT dynamics in paroxysmal atrial fibrillation.

BACKGROUND: QT dynamics parameters are used only in sinus rhythm. However, because many patients with paroxysmal atrial fibrillation undergo antiarrhythmic treatment that changes QT, developing methods for measuring QT dynamics during atrial fibrillation is important. OBJECTIVES: The purpose of this study was to evaluate whether QT dynamics in atrial fibrillation can be measured more reliably if additional RR intervals are included in the QT calculation. METHODS: QT and RR intervals were measured in 15 patients with atrial fibrillation and sinus rhythm on the same 24-hour Holter recording. Full QT adaptation is not instantaneous but lags behind over several beats. To correct for this lag, we adapted a weighted average method using five successive RR intervals. Linear regression was performed on (QT, RR) and (QT, RR(modified)) pairs. Variability ratio (standard deviation of all QT intervals/standard deviation of all RR intervals) and modified variability ratio (standard deviation of all QT intervals/modified standard deviation of all RR intervals) were calculated. RESULTS: QT-RR slope was reduced in atrial fibrillation compared with sinus rhythm (0.076 +/- 0.009 vs 0.113 +/- 0.0013, P = .0005). When correcting for lag, using the QT-RR(modified) slope, the slope in atrial fibrillation became similar to the slope in sinus rhythm (0.126 +/- 0.013 vs 0.126 +/- 0.013, P = .9547). The variability ratio was reduced in atrial fibrillation compared with sinus rhythm (0.175 +/- 0.017 vs 0.240 +/- 0.031, P = .009), but when correcting for the lag, the modified variability ratio was similar in atrial fibrillation and sinus rhythm (0.262 +/- 0.029 vs 0.267 +/- 0.038, P = .80). CONCLUSION: The results of this study demonstrate that QT dynamics can be measured reliably in atrial fibrillation using 24-hour Holter recordings.

QT dynamics in risk stratification after myocardial infarction.

OBJECTIVES: The purpose of this study was to compare measures of repolarization dynamics (QT dynamics) with other Holter risk predictors, left ventricular systolic function, and demographic characteristics to establish whether QT dynamics add independent information on risk stratification after myocardial infarction (MI). A novel QT dynamics parameter, the QT/RR variability ratio (VR), was introduced in this study. BACKGROUND: Abnormal repolarization contributes to arrhythmogenesis, and quantification of QT dynamics may have prognostic value after MI. METHODS: A 24-hour Holter recording was performed in 481 consecutive MI patients. Recordings from 311 patients were included in the analysis. QT/RR slope and intercept, mean and standard deviation of all QT, QTc, and RR intervals, and VR (defined as the ratio between the standard deviation of all QT intervals and the standard deviation of all RR interval) were calculated. Ventricular premature beats and ventricular tachycardia were counted. RESULTS: During 3-year follow-up, 70 deaths from all causes occurred. All parameters except mean of all QT intervals and standard deviation of all QTc intervals univariately predicted all-cause mortality. In multivariate Cox analysis, only VR per 0.1 (hazard ratio [HR]: 1.9 [1.5-2.4]), left ventricular ejection fraction per 5% (HR: 1.2 [1.1-1.3]), ventricular premature beats per 10 beats/hour (HR: 1.03 [1.002-1.06]), and age per 10 years (HR: 1.6 [1.3-2.0]) independently predicted all-cause mortality. CONCLUSIONS: Measures of QT dynamics univariately predicted total mortality. VR, left ventricular ejection fraction, ventricular premature beats, and age made up the optimal Cox model for risk stratification after MI. VR seems to be a promising risk factor for identifying sudden arrhythmic death.

Beat-to-beat QT dynamics in healthy subjects.

BACKGROUND: Measures of QT dynamics express repolarization abnormalities that carry prognostic information, but the reproducibility of beat-to-beat QT dynamics has never been established. The QT interval is prolonged at night, but how the circadian rhythm and heart rate influence the dynamic QT measurements is still unsettled. The aims of the present study were: (1) to describe the reproducibility of beat-to-beat QT dynamics with respect to intrasubject, between-subject, and between-observer variability and (2) to describe the normal range, circadian variation, and heart rate dependence of QT dynamics. METHODS: Ambulatory Holter recordings were performed three times on 20 healthy volunteers and were analyzed by two experienced cardiologists. Slope and intercept of the QT/RR regression, the variability of QT and R-R intervals expressed as the standard deviation, and the relation between QT and RR variability expressed as a variability ratio were measured among other QT dynamics. RESULTS: The reproducibility of all QT dynamics was good. All QT dynamics showed circadian variation when calculated on an hourly basis. The day/night variation in slope could be explained by the differences in heart rate, whereas the day/night variation in intercept was heart rate independent. CONCLUSION: The present study shows that reliable automatic QT measurements could be performed, encouraging further evaluation of the clinical value of QT dynamics in risk stratification of cardiac patients.

Heart rate versus heart rate variability in risk prediction after myocardial infarction.

INTRODUCTION: The aim of this study was to evaluate and compare heart rate and heart rate variability (HRV) in risk prediction after acute myocardial infarction (MI) and to evaluate the effect of beta-blocker treatment on the prognostic performance of heart rate and HRV. METHODS AND RESULTS: Three hundred sixty-six patients underwent 24-hour Holter recording 1 to 6 days after an MI. HRV was expressed as the standard deviation of all normal-to-normal intervals. Left ventricular systolic function was evaluated using the wall motion index. Half of the patients were taking a beta-blocker at the time of Holter recording. Mean follow-up was 44 months (median 34) after MI. By the end of follow-up, 82 patients had died. Mortality at 1 and 3 years was 12.5% and 22.6%, respectively. HRV, heart rate, wall motion index, number of ventricular premature beats per hour, and ventricular tachycardia were all significantly (P < 0.05) associated with mortality in univariate analysis, independent of beta-blocker therapy. In multivariate Cox analysis, only heart rate, wall motion index, number of ventricular premature beats per hour, and age had independent prognostic value (P < 0.001). In any model, including heart rate, HRV had no predictive value. CONCLUSION: The prognostic information of HRV is contained completely in heart rate, which carries prognostic information further than that of HRV. This result was independent of beta-blocker treatment.