Estimating the probability of early afterdepolarizations and predicting arrhythmic risk associated with long QT syndrome type 1 mutations

Early afterdepolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias. Simulations using biophysically detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias; however, such analyses can pose a huge computational burden. We have previously developed a highly simplified approach in which logistic regression models (LRMs) map parameters of complex cell models to the probability of ectopic beats. Here, we extend this approach to predict the probability of EADs (P(EAD)) as a mechanistic metric of arrhythmic risk. We use the LRM to investigate how changes in parameters of the slow-activating delayed rectifier current (IKs) affect P(EAD) for 17 different long QT syndrome type 1 (LQTS1) mutations. In this LQTS1 clinical arrhythmic risk prediction task, we compared P(EAD) for these 17 mutations with two other recently published model-based arrhythmia risk metrics (AP morphology metric across populations of myocyte models and transmural repolarization prolongation based on a onedimensional [1D] tissue-level model). These model-based risk metrics yield similar prediction performance; however, each fails to stratify clinical risk for a significant number of the 17 studied LQTS1 mutations. Nevertheless, an interpretable ensemble model using multivariate linear regression built by combining all of these model-based risk metrics successfully predicts the clinical risk of 17 mutations. These results illustrate the potential of computational approaches in arrhythmia risk prediction.SIGNIFICANCE An early after-depolarization (EAD) is an abnormal cellular electrical event that can trigger dangerous arrhythmias in the heart. We use our previously developed method to build a logistic regression model (LRM) that estimates the probability of EAD (P(EAD)) as a function of myocyte model parameters. Using this LRM along with two other recently published model-based arrhythmia risk metrics, we estimate risk of arrhythmia for 17 long QT syndrome type 1 (LQTS1) mutations. Results show that all approaches have inadequate prediction performance. We then develop an interpretable ensemble model based on all four model-based risk metrics. The ensemble model faithfully stratifies the risk of arrhythmia for each long QT mutation. These results indicate the potential of computational approaches in arrhythmic risk prediction.