Multiscale Complexity Analysis of Short QT Interval Variability Series Stratifies the Arrhythmic Risk of Long QT Syndrome Type 1 Patients

Vlasta Bari1, Beatrice De Maria2, Giulia Girardengo3, Emanuele Vaini1, Beatrice Cairo4, Lia Crotti3, Paul Brink5, Peter Schwartz3, Alberto Porta6
1Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, 2IRCCS Istituti Clinici Scientifici Maugeri, Istituto di Milano; Department of Electronics Information and Bioengineering, Politecnico di Milano, 3Center for Cardiac Arrhythmias of Genetic Origin, IRCCS Istituto Auxologico Italiano, Centro Diagnostico San Carlo, 4Department of Biomedical Sciences for Health, University of Milan, 5Department of Internal Medicine, University of Stellenbosch, 6Department of Biomedical Sciences for Health, University of Milan;Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato


Background: Long QT syndrome type 1 (LQT1) is an inherited disease increasing the risk for life threatening arrhythmias especially in situations of high sympathetic drive such as during emotive/physical stress and daytime. Cardiac autonomic control assessment, as derived from heart period (HP) and QT interval variability, was found useful to differentiate asymptomatic mutation carriers (AMCs) from symptomatic ones (SMCs). We propose the application of a linear model-based multiscale complexity (MSC) approach aiming at stratifying the arrhythmic risk of LQT1 patients. Methods: The adopted MSC analysis assessed complexity of the components of an autoregressive model as the complement to 1 of their pole modulus. The method was applied to 300 consecutive HPs and QTs extracted from 24h Holter recordings in 7 AMCs (age:42±12, 2 males), 22 SMCs (age:37±15, 8 males) and 13 healthy non-mutation carriers (NMCs, age:38±1, 6 males) belonging to the same founder population. Series were extracted during daytime and nighttime and analysis was iterated over the entire period. MSC was assessed in the typical frequency bands of short-term variability analysis, namely low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands and compared to a single scale model-based complexity approach grounded on the computation of the prediction error variance. Time domain HP and QT indices were calculated as well. Results: Both HP time domain and complexity indices could not differentiate groups. Solely MSC analysis of QT interval variability allowed to separate AMCs by showing that during daytime AMCs had a reduced complexity in the LF band compared to NMCs and SMCs. Conclusions: MSC analysis of QT interval variability can be fruitfully exploited to improve risk stratification in LQT1 patients and suggests that having a reduced complexity at time scales typical of the sympathetic control directed to the ventricles might be protective.