Electrolyte and pH dependence of heart rate during hemodialysis: a computer model analysis

Effect of dialysate bicarbonate and sodium on blood pH in maintenance hemodialysis-A prospective study

INTRODUCTION

The validity of adjusting dialysate bicarbonate based on pre-hemodialysis (HD) serum bicarbonate is unclear. There are no studies of the impact of dialysate sodium on blood pH.

AIMS

To understand the impact of dialysate bicarbonate and sodium on blood pH.

METHODS

Two hundred arterialized blood samples were obtained on the third session of HD with four configurations of dialysate: sodium (140, 137 mEq/L) and bicarbonate (38, 32 mEq/L).

RESULTS

The correlation between pre-HD serum bicarbonate and pH was modest (r = 0.6). A lower dialysate sodium (p = 0.035) and a higher bicarbonate (p = 0.02) associated with a higher post-HD blood pH. The frequency of pre-HD blood pH of <7.4 and a post-HD blood pH of >7.5 did not differ for samples with serum bicarbonate <22, 22-26, or >26 mEq/L.

DISCUSSION/CONCLUSIONS

Adjusting dialysate buffer based on pre-HD serum bicarbonate is unnecessary. A higher bicarbonate and lower dialysate sodium associate with post-HD alkalemia.

Monitoring blood potassium concentration in hemodialysis patients by quantifying T-wave morphology dynamics

We investigated the ability of time-warping-based ECG-derived markers of T-wave morphology changes in time (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w}$$\end{document}dw) and amplitude (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_a$$\end{document}da), as well as their non-linear components (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d_w^{{\mathrm{NL}}}}$$\end{document}dwNL and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d_a^{\mathrm{NL}}}$$\end{document}daNL), and the heart rate corrected counterpart (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w,c}$$\end{document}dw,c), to monitor potassium concentration (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+]) changes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta [K^+]$$\end{document}Δ[K+]) in end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). We compared the performance of the proposed time-warping markers, together with other previously proposed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+] markers, such as T-wave width (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_w$$\end{document}Tw) and T-wave slope-to-amplitude ratio (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{S/A}$$\end{document}TS/A), when computed from standard ECG leads as well as from principal component analysis (PCA)-based leads. 48-hour ECG recordings and a set of hourly-collected blood samples from 29 ESRD-HD patients were acquired. Values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_w$$\end{document}dw, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_a$$\end{document}da, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d_w^{\mathrm{NL}}}$$\end{document}dwNL, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d_a^{\mathrm{NL}}}$$\end{document}daNL and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w,c}$$\end{document}dw,c were calculated by comparing the morphology of the mean warped T-waves (MWTWs) derived at each hour along the HD with that from a reference MWTW, measured at the end of the HD. From the same MWTWs \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_w$$\end{document}Tw and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{S/A}$$\end{document}TS/A were also extracted. Similarly, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta [K^+]$$\end{document}Δ[K+] was calculated as the difference between the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+] values at each hour and the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+] reference level at the end of the HD session. We found that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w}$$\end{document}dw and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w,c}$$\end{document}dw,c showed higher correlation coefficients with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta [K^+]$$\end{document}Δ[K+] than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{S/A}$$\end{document}TS/A—Spearman’s (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho$$\end{document}ρ) and Pearson’s (r)—and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_w$$\end{document}Tw—Spearman’s (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho$$\end{document}ρ)—in both SL and PCA approaches being the intra-patient median \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho \ge 0.82$$\end{document}ρ≥0.82 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r \ge 0.87$$\end{document}r≥0.87 in SL and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho \ge 0.82$$\end{document}ρ≥0.82 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r \ge 0.89$$\end{document}r≥0.89 in PCA respectively. Our findings would point at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w}$$\end{document}dw and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d_{w,c}$$\end{document}dw,c as the most suitable surrogate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta [K^+]$$\end{document}Δ[K+], suggesting that they could be potentially useful for non-invasive monitoring of ESRD-HD patients in hospital, as well as in ambulatory settings. Therefore, the tracking of T-wave morphology variations by means of time-warping analysis could improve continuous and remote \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+] monitoring of ESRD-HD patients and flagging risk of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[K^{+}]$$\end{document}[K+]-related cardiovascular events.

Eliminating contraction during culture maintains global and local Ca2+ dynamics in cultured rabbit pacemaker cells

Pacemaker cells residing in the sinoatrial node generate the regular heartbeat. Ca²⁺ signaling controls the heartbeat rate-directly, through the effect on membrane molecules (NCX exchange, K⁺ channel), and indirectly, through activation of calmodulin-AC-cAMP-PKA signaling. Thus, the physiological role of signaling in pacemaker cells can only be assessed if the Ca²⁺ dynamics are in the physiological range. Cultured cells that can be genetically manipulated and/or virally infected with probes are required for this purpose. Because rabbit pacemaker cells in culture experience a decrease in their spontaneous action potential (AP) firing rate below the physiological range, Ca²⁺ dynamics are expected to be affected. However, Ca²⁺ dynamics in cultured pacemaker cells have not been reported before. We aim to a develop a modified culture method that sustains the global and local Ca²⁺ kinetics along with the AP firing rate of rabbit pacemaker cells. We used experimental and computational tools to test the viability of rabbit pacemaker cells in culture under various conditions. We tested the effect of culture dish coating, pH, phosphorylation, and energy balance on cultured rabbit pacemaker cells function. The cells were maintained in culture for 48 h in two types of culture media: one without the addition of a contraction uncoupler and one enriched with either 10 mM BDM (2,3-Butanedione 2-monoxime) or 25 μM blebbistatin. The uncoupler was washed out from the medium prior to the experiments. Cells were successfully infected with a GFP adenovirus cultured with either BDM or blebbistatin. Using either uncoupler during culture led to the cell surface area being maintained at the same level as fresh cells. Moreover, the phospholamban and ryanodine receptor densities and their phosphorylation level remained intact in culture when either blebbistatin or BDM were present. Spontaneous AP firing rate, spontaneous Ca²⁺ kinetics, and spontaneous local Ca²⁺ release parameters were similar in the cultured cells with blebbistatin as in fresh cells. However, BDM affects these parameters. Using experimental and a computational model, we showed that by eliminating contraction, phosphorylation activity is preserved and energy is reduced. However, the side-effects of BDM render it less effective than blebbistatin.

Human atrial cell models to analyse haemodialysis-related effects on cardiac electrophysiology: work in progress

During haemodialysis (HD) sessions, patients undergo alterations in the extracellular environment, mostly concerning plasma electrolyte concentrations, pH, and volume, together with a modification of sympathovagal balance. All these changes affect cardiac electrophysiology, possibly leading to an increased arrhythmic risk. Computational modeling may help to investigate the impact of HD-related changes on atrial electrophysiology. However, many different human atrial action potential (AP) models are currently available, all validated only with the standard electrolyte concentrations used in experiments. Therefore, they may respond in different ways to the same environmental changes. After an overview on how the computational approach has been used in the past to investigate the effect of HD therapy on cardiac electrophysiology, the aim of this work has been to assess the current state of the art in human atrial AP models, with respect to the HD context. All the published human atrial AP models have been considered and tested for electrolytes, volume changes, and different acetylcholine concentrations. Most of them proved to be reliable for single modifications, but all of them showed some drawbacks. Therefore, there is room for a new human atrial AP model, hopefully able to physiologically reproduce all the HD-related effects. At the moment, work is still in progress in this specific field.

Genesis and regulation of the heart automaticity

The heart automaticity is a fundamental physiological function in higher organisms. The spontaneous activity is initiated by specialized populations of cardiac cells generating periodical electrical oscillations. The exact cascade of steps initiating the pacemaker cycle in automatic cells has not yet been entirely elucidated. Nevertheless, ion channels and intracellular Ca(2+) signaling are necessary for the proper setting of the pacemaker mechanism. Here, we review the current knowledge on the cellular mechanisms underlying the generation and regulation of cardiac automaticity. We discuss evidence on the functional role of different families of ion channels in cardiac pacemaking and review recent results obtained on genetically engineered mouse strains displaying dysfunction in heart automaticity. Beside ion channels, intracellular Ca(2+) release has been indicated as an important mechanism for promoting automaticity at rest as well as for acceleration of the heart rate under sympathetic nerve input. The potential links between the activity of ion channels and Ca(2+) release will be discussed with the aim to propose an integrated framework of the mechanism of automaticity.

Model based sensitivity analysis of arterial pressure response to hemodialysis induced hypovolemia

The role of hemodynamic and regulatory factors in the arterial pressure response to hemodialysis induced hypovolemia was investigated by means of a computer model of the cardiovascular system, including the main short-term pressure regulatory mechanisms. The model mimics the arterial and venous systemic circulation, Starling's law and inotropic heart regulation, arterial and cardiopulmonary baroreflex controls of resistance, and capacitance vessels. All of the model parameters have a clear physiologic meaning: 10 represent the systemic circulation, 4 describe cardiac pump performance, and 3 characterize baroreflex regulation. Sensitivity analysis is performed to determine the effect of each parameter on the pressure response to mild hypovolemia (a 10% blood volume reduction after 4 hours). The results demonstrate that circulatory parameters, such as resistances and compliances, have no relevant effect upon the pressure response. Conversely, regulation of venous capacity seems to play a pivotal role in sustaining arterial pressure during hemodialysis induced hypovolemia. Regulation of systemic peripheral resistance exerts a compensatory action only as long as the blood volume reduction is < 5%, but it is inadequate to compensate for a larger blood volume reduction when venous capacity regulation is absent. A paradoxical arterial pressure increase during hypovolemia can be referred to a prevalence of cardiopulmonary afferences in the regulatory process.