Transmural Ventricular Heterogeneities Play a Major Role in Determining T-Wave Morphology at Different Extracellular Potassium Levels

End-stage renal disease (ESRD) affects more than 10% of the world population. ESRD patients present impaired potassium homeostasis, which increases the risk for ventricular arrhythmias and sudden cardiac death. Noninvasive estimation of serum potassium, [K], before the patient experiences serious consequences is of major importance. In this study, we investigated the relationship of [K] with three T-wave morphological descriptors: the T-wave width (Tw), slope-to-amplitude ratio (TSA) and temporal morphological variability (dw) from ECGs of 12 ESRD patients undergoing hemodialysis and from simulated ECGs. Spearman’s correlation coefficients between the descriptors Tw, TSA and dw and [K] were −0.5, 0.8 and 0.65, respectively. These associations were, however, highly patient-dependent. The high inter-individual variability in T-wave morphology, particularly observed at high [K], was reproduced in the simulations and could be explained by differences in transmural heterogeneities, with 10% variations in the proportion of midmyocardial cells leading to changes larger than 15% in T-wave morphology. In conclusion, T-wave morphological descriptors have the potential to be used as predictors of [K] in ESRD patients, but their associated inter-individual variability should be taken into account, especially under hyperkalemic conditions.

[1]  Y. Rudy,et al.  Ionic Current Basis of Electrocardiographic Waveforms: A Model Study , 2002, Circulation research.

[2]  Zhilin Qu,et al.  Electrophysiology of Hypokalemia and Hyperkalemia. , 2017, Circulation. Arrhythmia and electrophysiology.

[3]  T Alexander Quinn,et al.  Electrophysiological properties of computational human ventricular cell action potential models under acute ischemic conditions. , 2017, Progress in biophysics and molecular biology.

[4]  S. Severi,et al.  Validation of a novel method for non-invasive blood potassium quantification from the ECG , 2012, 2012 Computing in Cardiology.

[5]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[6]  A. Lanari,et al.  ELECTROCARDIOGRAPHIC EFFECTS OF POTASSIUM. I. PERFUSION THROUGH THE CORONARY BED. , 1964, American heart journal.

[7]  Blanca Rodríguez,et al.  Impact of ionic current variability on human ventricular cellular electrophysiology. , 2009, American journal of physiology. Heart and circulatory physiology.

[8]  Gioia Turitto,et al.  Electrolyte disorders and arrhythmogenesis. , 2011, Cardiology journal.

[9]  F. Hobbs,et al.  Global Prevalence of Chronic Kidney Disease – A Systematic Review and Meta-Analysis , 2016, PloS one.

[10]  M. Doblaré,et al.  Adaptive Macro Finite Elements for the Numerical Solution of Monodomain Equations in Cardiac Electrophysiology , 2010, Annals of Biomedical Engineering.

[11]  Marc Sabbe,et al.  The clinical value of the ECG in noncardiac conditions. , 2004, Chest.

[12]  Pablo Laguna,et al.  Variability of Ventricular Repolarization Dispersion Quantified by Time-Warping the Morphology of the T-Waves , 2017, IEEE Transactions on Biomedical Engineering.

[13]  Yoram Rudy,et al.  Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation , 2011, PLoS Comput. Biol..