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Background: Comprehensive, patient‐specific models are essential to study calcium deposition and mobilization during dialysis. We aim to develop tools to support clinical prescriptions with a more accurate approach for the prediction of calcium mobilization while also considering major electrolytes and catabolites. Methods: We modified a multi‐solute model predicting patient‐specific dialysis response by incorporating a calcium buffer to represent bone exchanges. Data from four centers, involving 127 patients with six sessions each, were utilized. For each patient, three sessions were allocated for model training (ID123), while the remaining sessions were for validation (PRED456). The normalized root mean square error (nRMSE%) was used to evaluate both descriptive and predictive accuracy. Correlations between initial data and calcium exchanges were also assessed. Results: The overall nRMSE% for ID123 was 3.92%. For PRED456, it was 3.46% (ranging from a minimum of 1.17% for [Na+] to a maximum of 6.62% for [urea]). The median nRMSE% for plasma calcium varied between 1.13 and 8.32 for SHD sessions, depending on whether Ca_dialysis fluid (Cad) was ≥ or <1.50 mmol/L, respectively. For HDF sessions, the range was between 2.90 and 5.89. A significant and moderate correlation was found between overall calcium removal and the buffer balance. The most robust correlation observed was between the amount of calcium administered via post‐dilution fluid and the overall calcium removal in the dialysis filter. Conclusions: Identical therapy settings do not uniformly affect calcium mobilization, and our approach offers insight into calcium distribution across body compartments. This understanding will enhance clinical prescription practices. [ABSTRACT FROM AUTHOR]

Background: Parametric multipool kinetic models were used to describe the intradialytic trends of electrolytes, breakdown products, and body fluids volumes during hemodialysis. Therapy customization can be achieved by the identification of parameters, allowing patient‐specific modulation of mass and fluid balance across dialyzer, capillary, and cell membranes. This study wants to evaluate the possibility to use this approach to predict the patient's intradialytic response. Methods: 6 sessions of 68 patients (DialysIS© project) were considered. Data from the first three sessions were used to train the model, identifying the patient‐specific parameters, that, together with the treatment settings and the patient's data at the session start, could be used for predicting the patient's specific time course of solutes and fluids along the sessions. Na+, K+, Cl−, Ca2+, HCO3−, and urea plasmatic concentrations and hematic volume deviations from clinical data were evaluated. Results: nRMSE predictive error is on average equal to 4.76% when describing the training sessions, and only increases by 0.97 percentage points on average in independent sessions of the same patient. Conclusions: The proposed predictive approach represents a first step in the development of tools to support the clinician in tailoring the patient's prescription. [ABSTRACT FROM AUTHOR]

Hemodialysis is the most common therapy to treat renal insufficiency. However, notwithstanding the recent improvements, hemodialysis is still associated with a non-negligible rate of comorbidities, which could be reduced by customizing the treatment. Many differential compartment models have been developed to describe the mass balance of blood electrolytes and catabolites during hemodialysis, with the goal of improving and controlling hemodialysis sessions. However, these models often refer to an average uremic patient, while on the contrary the clinical need for customization requires patient-specific models. In this work, we assume that the customization can be obtained by means of patient-specific model parameters. We propose and validate a Bayesian approach to estimate the patient-specific parameters of a multi-compartment model, and to predict the single patient's response to the treatment, in order to prevent intra-dialysis complications. The likelihood function is obtained by means of a discretized version of the multi-compartment model, where the discretization is in terms of a Runge-Kutta method to guarantee convergence, and the posterior densities of model parameters are obtained through Markov Chain Monte Carlo simulation. Results show fair estimations and the applicability in the clinical practice. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: A reliable method of intradialysis calcium mass balance quantification is far from been established. We herein investigated the use of a single‐pool variable‐volume Calcium kinetic model to assess calcium mass balance in chronic and stable dialysis patients. Methods: Thirty‐four patients on thrice‐weekly HD were studied during 240 dialysis sessions. All patients were dialyzed with a nominal total calcium concentration of 1.50 mmol/L. The main assumption of the model is that the calcium distribution volume is equal to the extracellular volume during dialysis. This hypothesis is assumed valid if measured and predicted end dialysis plasma water ionized calcium concentrations are equal. A difference between predicted and measured end‐dialysis ionized plasma water calcium concentration is a deviation on our main hypothesis, meaning that a substantial amount of calcium is exchanged between the extracellular volume and a nonmodeled compartment. Findings: The difference between predicted and measured values was 0.02 mmol/L (range −0.08:0.16 mmol/L). With a mean ionized dialysate calcium concentration of 1.25 mmol/L, calcium mass balance was on average negative (mean ± SD −0.84 ± 1.33 mmol, range −5.42:2.75). Predialysis ionized plasma water concentration and total ultrafiltrate were the most important predictors of calcium mass balance. A significant mobilization of calcium from the extracellular pool to a nonmodeled pool was calculated in a group of patients. Discussion: The proposed single pool variable‐volume Calcium kinetic model is adequate for prediction and quantification of intradialysis calcium mass balance, it can evaluate the eventual calcium transfer outside the extracellular pool in clinical practice. [ABSTRACT FROM AUTHOR]