Background: 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.
Method: 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.
Results: 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.
Conclusion: We demonstrated that a large number of QT–R-R measurements (∼400) is required to ensure reliable individual correction of QT intervals for heart rate.