QT and RR interval analysis in genetic cardiac diseases using the AccuQT and advanced heart rate variability methods

Abstract

The QT interval is a key indicator in assessing arrhythmia risk, evaluating drug safety, and supporting clinical diagnosis in cardiology. The QT interval is significantly influenced by heart rate so it must be accurately corrected to ensure reliable clinical interpretation. Conventional correction formulas, such as Bazett's formula, are widely utilized but often criticized for inaccuracies, either under- or overcorrecting QT intervals in different physiological conditions. The recently developed AccuQT method, utilizing transfer entropy for QT correction, has demonstrated superior consistency in healthy populations and improved accuracy in diagnosing long QT syndrome (LQTS) compared to conventional approaches. In this study, we evaluate the AccuQT method using 24-h Holter recordings from patients with various genetic heart diseases, including hypertrophic cardiomyopathy (HCM) and LQTS, compared to the healthy controls. Additionally, we analyzed heart rate variability with the recently developed scaled-dependent detrended fluctuation analysis (DFA). The mean QTc using the AccuQT method in the patient group was significantly longer (476 ms) than in the healthy population (410 ms), as expected. The Bazett's formula resulted in significantly longer mean QTc in the healthy population (460 ms) and in patient group (490 ms). The DFA scaling exponent was lower at short scales for patient group compared to healthy controls. It also detected a difference between HCM patients with clinical disease and asymptomatic gene carriers with no signs of the disease. In conclusion, the AccuQT method provides reliable QT interval correction in patients with genetic cardiac diseases, demonstrating superior precision compared to Bazett's formula. AccuQT effectively captures time-dependent QT interval changes, enhancing diagnostic accuracy. Additionally, scale-dependent DFA analysis shows promise in differentiating patients with clinical hypertrophic cardiomyopathy from asymptomatic gene carriers, suggesting potential utility in earlier identification of at-risk individuals.