Classification of drug‐induced hERG potassium‐channel block from electrocardiographic T‐wave features using artificial neural networks. Issue 6 (26th July 2019)

• Record Type: Journal Article
• Authors: Morettini, Micaela; Peroni, Chiara; Sbrollini, Agnese; Marcantoni, Ilaria; Burattini, Laura
• Is Part Of: Annals of noninvasive electrocardiology. Volume 24:Issue 6(2019:Nov.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-26
• Subjects: artificial neural network -- cardiac repolarization -- dofetilide -- drug‐induced long QT syndrome -- hERG channels; Electrocardiography -- Periodicals; Arrhythmia -- Periodicals; 616.1207547
• DOI: 10.1111/anec.12679
• Languages: English
• ISSNs: 1082-720X

Abstract

Abstract: Background: Human ether‐à‐go‐go‐related gene (hERG) potassium‐channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T‐wave features represents a noninvasive way to accurately evaluate the TdP risk in drug‐safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography‐based classification of the hERG potassium‐channel block. Methods: The data were taken from the "ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" Physionet database; they consisted of median vector magnitude (VM) beats of 22 healthy subjects receiving a single 500 μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time‐points ranging from 0.5 hr before to 14.0 hr after dofetilide administration. For each VM, changes in two indexes accounting for the early and the late phases of repolarization, ΔERD30% and ΔTS /A, respectively, were computed as difference between values at each postdose time‐point and the predose time‐point. Thus, the dataset contained 286 ΔERD30% ‐ΔTS /A pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two‐layer feedforward ANN with two target classes: high block (HB) and low block (LB). Optimal ANN (OANN) was identified using the training and validation sets and tested on the test set. Results: Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. Conclusion: OANN represents a reliable tool for noninvasive assessment of the hERG potassium‐channel block.