Intra-subject covariates of QT variability indices: influence of ECG noise.

Background/objectives: Electrocardiogram (ECG) assessment of increased beat-to-beat QT interval variability indices has repeatedly been proposed to indicate adverse outcomes in different clinical populations. Nevertheless, the reported values of the indices appear poorly reproducible. Consequently, there is no clear consensus on the physiologic normal values. The limited stability of the indices might be linked to their relation to heart rate and other ECG factors such as the underlying heart rate variability. We have therefore investigated covariates of QT variability indices to answer the question of whether correction for covariates might, in principle, increase the reproducibility. Methods: In a population of 251 healthy subjects (mean age 33.6 ± 9.1 years, 108 females) who underwent repeated provocative manoeuvres, ECG measurements were performed in recordings obtained during different postural positions. In each subject, measurements of QT variability and of its plausible covariates were made in 32 separate sections of the recordings. The measurements included mean normal-to-normal RR intervals, mean QT intervals, standard deviations of RR and QT intervals, and root mean square of successive differences of QT and RR intervals. In each repeated postural position, the measurements were made in 5-minute intervals of stable heart rate. Electrocardiographic noise was assessed based on signal-to-noise ratios. Univariable and multivariable regression analyses were performed in each subject separately to assess the stability of QT variability relationship to the covariates. Results: Univariably, QT variability indices were significantly more strongly related to ECG noise than to other covariates. The regression coefficients relating QT variability to other covariates were also highly variable. In multivariable regression analyses with backwards elimination, ECG noise remained a statistically significant covariate of QT variability indices in approximately one third and one half of the population in the regression analyses of standard deviation of QT intervals and of root mean square of successive QT differences, respectively. Conclusions: It is not feasible to reduce the inconsistency of QT variability expressions by correcting them for other ECG measurements. When QT variability measures are used in clinical studies, e.g., studies of risk prediction, influence of ECG noise needs to be considered. Multivariable regression models of QT variability need to involve ECG noise data to eliminate unfounded conclusions.