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.