RESUMEN
BACKGROUND: Long QT syndrome (LQTS) is a genetic cardiac disease, where the corrected QT (QTc) interval is prolonged. It can cause arrhythmias and lead to a sudden cardiac death. Duration of the QT interval depends on the heart rate and this dependency is treated with QT correction. However, the current QT correction methods have well known problems and limitations. OBJECTIVE: We study the relevance of QT correction method in evaluating the risk of LQTS. We evaluate the reliability of the present and recently developed QT correction methods to discriminate LQTS subjects from healthy controls. METHODS: We use the clinically prevalent QT correction methods, particularly Bazett and Fridericia, in comparison with the recently developed AccuQT method. The data of healthy controls and LQTS subjects is extracted from the Rochester THEW database. The analysis accounts for sex, major LQTS subtypes, and beta-blocker treatment. RESULTS: QT values corrected with AccuQT discriminate the healthy and LQTS samples with the best accuracy, leading to (TP, TN) = (0.87, 0.65) with the conventional 450 ms threshold for LQTS. Fridericia correction yields lower sensitivity (0.71), but comparable balanced accuracy, whereas Bazett shows significantly less accurate results due to overcorrection at lower heart rates. CONCLUSION: The selected QT correction method is important in the identification of LQTS. In particular, the use of Bazett correction should be questioned. Fridericia correction yields good results with respect to its simplicity. AccuQT has the best accuracy out of all the methods for LQTS discrimination. For practical applicability, however, AccuQT needs further validation in realistic clinical conditions.
RESUMEN
Background: The QT interval in the electrocardiogram (ECG) is a fundamental risk measure for arrhythmic adverse cardiac events. However, the QT interval depends on the heart rate and must be corrected accordingly. The present QT correction (QTc) methods are either simple models leading to under- or overcorrection, or impractical in requiring long-term empirical data. In general, there is no consensus on the best QTc method. Objective: We introduce a model-free QTc method-AccuQT-that computes QTc by minimizing the information transfer from R-R to QT intervals. The objective is to establish and validate a QTc method that provides superior stability and reliability without models or empirical data. Methods: We tested AccuQT against the most commonly used QT correction methods by using long-term ECG recordings of more than 200 healthy subjects from PhysioNet and THEW databases. Results: AccuQT overperforms the previously reported correction methods: the proportion of false-positives is reduced from 16% (Bazett) to 3% (AccuQT) for the PhysioNet data. In particular, the QTc variance is significantly reduced and thus the RR-QT stability is increased. Conclusion: AccuQT has significant potential to become the QTc method of choice in clinical studies and drug development. The method can be implemented in any device recording R-R and QT intervals.
RESUMEN
Aerobic and anaerobic thresholds of the three-zone exercise model are often used to evaluate the exercise intensity and optimize the training load. Conventionally, these thresholds are derived from the respiratory gas exchange or blood lactate concentration measurements. Here, we introduce and validate a computational method based on the RR interval (RRI) dynamics of the heart rate (HR) measurement, which enables a simple, yet reasonably accurate estimation of both metabolic thresholds. The method utilizes a newly developed dynamical detrended fluctuation analysis (DDFA) to assess the real-time changes in the dynamical correlations of the RR intervals during exercise. The training intensity is shown to be in direct correspondence with the time- and scale-dependent changes in the DDFA scaling exponent. These changes are further used in the definition of an individual measure to estimate the aerobic and anaerobic threshold. The results for 15 volunteers who participated in a cyclo-ergometer test are compared to the benchmark lactate thresholds, as well as to the ventilatory threshods and alternative HR-based estimates based on the maximal HR and the conventional detrended fluctuation analysis (DFA). Our method provides the best overall agreement with the lactate thresholds and provides a promising, cost-effective alternative to conventional protocols, which could be easily integrated in wearable devices. However, detailed statistical analysis reveals the particular strengths and weaknessess of each method with respect to the agreement and consistency with the thresholds-thus underlining the need for further studies with more data.