Patients suffering from end stage of chronic kid- ney disease (CKD) often undergo haemodialysis to normalize the electrolyte concentrations. Moreover, cardiovascular disease (CVD) is the main cause of death in CKD patients. To study the connection between CKD and CVD, we investi- gated the effects of an electrolyte variation on cardiac signals (action potential and ECG) using a computational model. In a first step, simulations with the Himeno et al. ventricular cell model were performed on cellular level with different extra- cellular sodium ([Na+]o), calcium ([Ca2+]o) and potassium ([K+]o) concentrations as occurs in CKD patients. [Ca2+]o and [K+]o changes caused variations in different features describ- ing the morphology of the AP. Changes due to a [Na+]o varia- tion were not as prominent. Simulations with [Ca2+]o varia- tions were also carried out on ventricular ECG level and a 12-lead ECG was computed. Thus, a multiscale simulator from ion channel to ECG reproducing the calcium-dependent inactivation of ICaL was achieved. The results on cellular and ventricular level agree with results from literature. Moreover, we suggest novel features representing electrolyte changes that have not been described in literature. These results could be helpful for further studies aiming at the estimation of ionic concentrations based on ECG recordings.
Multi-scale computational modeling of cardiac electrophysiology has fostered our understanding of the genesis of the ECG. While current models capture the relevant processes under physiological and many disease conditions with high fidelity, proper representation of the conditions in the extracellular milieu remains challenging. The recent human ventricular myocyte model by Himeno et al. is one of the first biophysical models which faithfully represents the dependence of the action potential (AP) duration on the extracellular calcium concentration ([Ca2+]o). Here, we present a heterogeneous formulation of the Himeno et al. cellular model and integrate it into a multi-scale framework to compute body surface ECGs. We propose three variants of the Himeno et al. model to account for transmural heterogeneity. The ionic current level parameter sets representing subendocardial, M, and subepicardial cell types were informed by the experimental data presented with the O’Hara-Rudy model and tuned to match AP level features such as repolarization stability. As shown in a previous work by Keller et al., an apico-basal gradient of IKs conductance is a likely mechanism causing concordant T-waves. Therefore, we increased the IKs conductance in the Himeno et al. model at the apex by a factor of 3.5 compared to the base to obtain an APD shortening of 12.5%. The model setup comprising transmural and apico-basal heterogeneity yielded a physiological ventricular ECG comparable to previous setups building on the ten Tusscher et al. cellular model. Our novel setup allows to study, for the first time, how realistic changes of the AP under hypo- and hypercalcaemic conditions translate to changes in the ECG. Resulting QT prolongation under hypocalcaemic conditions quantitatively matched human experimental data. In conclusion, the setup presented here provides a tool to study the effect of altered calcium levels in the extracellular milieu of the heart, as e. g. occurring during renal failure, across multiple spatial scales mechanistically.
Student Theses (1)
M. Hernandez Mesa. Analysis of the Effects of Serum Calcium Changes on the ECG in a Computational Model. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Bachelorarbeit. 2017
Approximately 13% of the worldwide population suffers from chronic kidney disease (CKD). Patients suffering from CKD often undergo dialysis therapies to compensate the changes of the ionic concentrations. Moreover, cardiovascular disease (CVD) is the main cause of death in CKD patients. In this study, the connection between the variation of the electrolyte concentrations and the electrophysiology of the heart will be studied, in order to justify the relation between both diseases. The results are based on a computational model, as it is a helpful, cheap and an ethically accepted tool for studying cardiac electrophysiology. The ventricular cell model presented by Himeno et al. was used for analyzing a variation of extracellular sodium, potassium and calcium within different ranges as done in a previous work. Simulations were carried out on cellular level and on tissue level for a ventricular simulation. On cellular level calcium and potassium changes showed variations in the form of the action potentials, and of parameters like the action potential duration or the resting membrane voltage. Changes in the action potential due to a variation of extracellular sodium were not as prominent as for calcium and potassium. In order to simulate the depolarization dispersion of the cells within the ventricles, an apicobasal and a transmural formulation of the Himeno et al. model were proposed. Results from simulations with these formulations accorded with results obtained from in vivo experiments. As a next step, ventricular simulations were carried out and forward calculated for the extraction of the electrocardiogram (ECG). First, the Himeno et al. model was evaluated for tissue simulations, by analyzing the wave propagation. As the model was considered capable for tissue modelling in the tissue study, ventricular simulations with extracellular ionic variation were performed. To evaluate the realism of the resulting ECGs, features were calculated and compared as done in a previous work. A dependence of the evaluated features on the extracellular ionic concentration was found. The results were in accordance with the literature for those features that have already been investigated. In this study the relation between electrolyte concentrations and the electrophysiology of the heart was proved by analyzing the morphology of the action potential and the ECG. These results could be helpful for further studies investigating the reconstruction of the ionic concentrations based on signal processing of the ECG.