Assessment of the Stability of the Individual‐Based Correction of QT Interval for Heart Rate

Abstract

Modeling the relationship between QT intervals and previous R-R values remains a challenge of modern quantitative electrocardiography. The technique based on an individual regression model computed from a set of QT-R-R measurements is presented as a promising alternative. However, a large set of QT-R-R measurements is not always available in clinical trials and there is no study that has investigated the minimum number of QT-R-R measurements needed to obtain a reliable individual QT-R-R model. In this study, we propose guidelines to ensure appropriate use of the regression technique for heart rate correction of QT intervals. Holter recordings from 205 healthy subjects were included in the study. QT-R-R relationships were modeled using both linear and parabolic regression techniques. Using a bootstrapping technique, we computed the stability of the individual correction models as a function of the number of measurements, the range of heart rate, and the variance of R-R values. The results show that the stability of QT-R-R individual models was dependent on three factors: the number of measurements included in its design, the heart-rate range used to design the model, and the T-wave amplitude. Practically our results showed that a set of 400 QT-R-R measurements with R-R values ranging from 600 to 1000 ms ensure a stable and reliable individual correction model if the amplitude of the T wave is at least 0.3 mV. Reducing the range of heart rate or the number of measurements may significantly impact the correction model. We demonstrated that a large number of QT-R-R measurements (approximately 400) is required to ensure reliable individual correction of QT intervals for heart rate.