Estimating the Probability of Cellular Arrhythmias with Simplified Statistical Models that Account for Experimentally Observed Uncertainty
AbstractEarly after-depolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias in, for example, Long QT Syndrome and heart failure. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses often pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EADs. Specifically, we develop an LRM for predicting the probability of EADs (P(EAD)) as a function of slow-activating delayed rectifier current (IKs) parameters, and for identifying those parameters with greatest influence on P(EAD). This LRM, which requires negligible computational resources, is also used to demonstrate how uncertainties in experimentally measured values of IKs model parameters influence P(EAD). We refer to this as arrhythmia sensitivity analysis. In the investigation of five different IKs parameters associated with Long QT syndrome 1 (LQTS1) mutations, the predicted P(EAD) when rank ordered for 6 LQTS1 mutations matches the trend in risk from patients with the same mutations as measured by clinical cardiac event rates. We also demonstrate the degree to which parameter uncertainties map to uncertainty of P(EAD), with IKs conductance having the greatest impact. These results demonstrate the potential for arrhythmia risk prediction using model-based approaches for estimation of P(EAD).