Logistic regression analysis of populations of electrophysiological models to assess proarrythmic risk

Abstract

Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na+, the acetylcholine-sensitive and ultra-rapid K+ channels, and the Na+/Ca2+ exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: • allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk; • is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca2+ sparks); • is computationally more efficient than one-at-a-time parameter sensitivity analysis.

Keywords

• Logistic regression analysis of populations of electrophysiological models