Alicja Wojcik-Zaluska

Phosphate, urea and creatinine clearances: haemodialysis adequacy assessed by weekly monitoring

BACKGROUND The specific distribution of phosphate and the control mechanisms for its plasma level makes phosphate kinetics during haemodialysis (HD) considerably different from those of urea and creatinine and makes the quantitative evaluation of adequacy of phosphate removal difficult. We propose the application of equivalent continuous clearance (ECC) as a phosphate adequacy parameter and compare it with ECC for creatinine and urea. METHODS Three consecutive dialysis sessions were evaluated for 25 patients on maintenance HD. Concentrations of phosphate, urea and creatinine in plasma were measured every 1h during the treatment and 45 min after, and every 30 min in dialysate. ECC was calculated using the removed solute mass assessed in dialysate and weekly solute profile in plasma. Similar calculations were performed also for the midweek dialysis session only. Different versions of the reference concentration for ECC were applied. RESULTS ECC with peak average reference concentration was 5.4 ± 1.0 for phosphate, 7.0 ± 1.0 for urea and 4.7 ± 1.0 mL/min for creatinine. ECC for urea and creatinine were well correlated in contrast to the correlations of ECC for phosphate versus urea and creatinine. Midweek ECC were higher than weekly ECC, but they were well correlated for urea and creatinine, but only weakly for phosphate. CONCLUSIONS HD adequacy monitoring for phosphate may be performed using ECC, but it is less predictable than similar indices for urea and creatinine. The values of ECC for phosphate are within the range expected for its molecular size compared with those for urea and creatinine.

Phosphate kinetics during weekly cycle of hemodialysis sessions: Application of mathematical modeling

Both hyperphosphatemia and hypophosphatemia are associated with increased morbidity and mortality among patients on dialysis. The control of serum phosphate concentration is a considerable clinical problem. Our study aimed to improve understanding of phosphate kinetics in patients on dialysis using mathematical modeling. Three consecutive hemodialysis sessions with breaks of 2-2-3 days were monitored in 25 patients. Phosphate concentration was measured every hour and 45 min after the end of dialysis in blood serum and every 30 min in dialysate during each session. Volume of fluid compartments and body composition were assessed by bioimpedance. The pseudo one-compartment model was applied to describe the profile of phosphate in blood serum during intra- and interdialytic periods of 1-week cycle of three hemodialysis sessions. Model parameters, such as phosphate internal clearance (KM ) and the rate of phosphate mobilization (RM ), were correlated with the reduction of serum phosphate concentration during dialysis (Cpost /Cpre ) and with equivalent continuous clearance (ECC) for phosphate. KM correlated negatively with predialysis serum phosphate concentration. There was significant positive correlation between RM and age. Postdialysis volume of phosphate central compartment was lower than, but correlated to, extracellular water volume. Parameters of the pseudo one-compartment model, phosphate internal clearance, and the rate of phosphate inflow to the central compartment (the one accessible for dialysis) from other phosphate body reservoirs correlated with the indices of dialysis adequacy, such as reduction of serum phosphate and ECC. The pseudo one-compartment model can be successfully extended from a single hemodialysis to the standard weekly cycle of sessions and the model parameters strongly correlate with the adequacy parameters of dialytic removal of phosphate.

Phosphate Kinetics in Hemodialysis: Application of Delayed Pseudo One-Compartment Model.

Background/Aims: Various body-regulating mechanisms try to counteract rapid changes in serum phosphate levels during hemodialysis (HD). Neither recently proposed nor other existing standard compartment models are able to capture clinically observed intradialytic serum phosphate rebound. Methods: Phosphate serum concentration was frequently measured during 75 HD sessions in 25 patients. Time delay was introduced into the standard pseudo one-compartment model in order to reflect the time needed for the body-regulating mechanism to affect serum phosphate level. Results: Measured serum phosphate concentration at the end of 4 h dialysis session was on average larger than 1 h earlier (p value = 0.015). The model with time delay reproduced successfully 19 out of 21 and 9 out of 10 sessions with and without recorded intradialytic rebound, respectively. Conclusion: The intradialytic serum phosphate rebound is associated with the time delay reflecting efficacy of body-regulating mechanisms, that is, the larger the delay the larger is the intradialytic rebound.

Subject-specific pulse wave propagation modeling: Towards enhancement of cardiovascular assessment methods

Cardiovascular diseases are the leading cause of death worldwide. Pulse wave analysis (PWA) technique, which reconstructs and analyses aortic pressure waveform based on non-invasive peripheral pressure recording, became an important bioassay for cardiovascular assessment in a general population. The aim of our study was to establish a pulse wave propagation modeling framework capable of matching clinical PWA data from healthy individuals on a per-subject basis. Radial pressure profiles from 20 healthy individuals (10 males, 10 females), with mean age of 42 ± 10 years, were recorded using applanation tonometry (SphygmoCor, AtCor Medical, Australia) and used to estimate subject-specific parameters of mathematical model of blood flow in the system of fifty-five arteries. The model was able to describe recorded pressure profiles with high accuracy (mean absolute percentage error of 1.87 ± 0.75%) when estimating only 6 parameters for each subject. Cardiac output (CO) and stroke volume (SV) have been correctly identified by the model as lower in females than males (CO of 3.57 ± 0.54 vs. 4.18 ± 0.72 L/min with p-value < 0.05; SV of 49.5 ± 10.1 vs. 64.2 ± 16.8 ml with p-value = 0.076). Moreover, the model identified age related changes in the heart function, i.e. that the cardiac output at rest is maintained with age (r = 0.23; p-value = 0.32) despite the decreasing heart rate (r = −0.49; p-value < 0.05), because of the increase in stroke volume (r = 0.46; p-value < 0.05). Central PWA indices derived from recorded waveforms strongly correlated with those obtained using corresponding model-predicted radial waves (r > 0.99 and r > 0.97 for systolic (SP) and diastolic (DP) pressures, respectively; r > 0.77 for augmentation index (AI); all p—values < 0.01). Model-predicted central waveforms, however, had higher SP than those reconstructed by PWA using recorded radial waves (5.6 ± 3.3 mmHg on average). From all estimated subject-specific parameters only the time to the peak of heart ejection profile correlated with clinically measured AI. Our study suggests that the proposed model may serve as a tool to computationally investigate virtual patient scenarios mimicking different cardiovascular abnormalities. Such a framework can augment our understanding and help with the interpretation of PWA results.

Quantification of Dialytic Removal and Extracellular Calcium Mass Balance during a Weekly Cycle of Hemodialysis

Objectives The removal of calcium during hemodialysis with low calcium concentration in dialysis fluid is generally slow, and the net absorption of calcium from dialysis fluid is often reported. The details of the calcium transport process during dialysis and calcium mass balance in the extracellular fluid, however, have not been fully studied. Methods Weekly cycle of three dialysis sessions with interdialytic breaks of 2-2-3 days was monitored in 25 stable patients on maintenance hemodialysis with calcium concentration in dialysis fluid of 1.35 mmol/L. Total and ionic calcium were frequently measured in blood and dialysate. The volume of fluid compartments was measured by bioimpedance. Results Weekly dialytic removal of 12.79 ± 8.71 mmol calcium was found in 17 patients, whereas 9.48 ± 8.07 mmol calcium was absorbed per week from dialysis fluid in 8 patients. Ionic calcium was generally absorbed from dialysis fluid, whereas complexed calcium (the difference of total and ionic calcium in dialysis fluid) was removed from the body. The concentration of total calcium in plasma increased slightly during dialysis. The mass of total and ionic calcium in extracellular fluid decreased during dialysis in patients with the dialytic removal of calcium from the body and did not change in patients with the absorption of calcium from dialysis fluid. Conclusions We conclude that about one third of patients on dialysis with calcium 1.35 mmol/L in dialysis fluid may absorb calcium from dialysis fluid and therefore individual prescriptions of calcium concentration in dialysis fluid should be considered for such patients.

Patient-specific pulse wave propagation model identifies cardiovascular risk characteristics in hemodialysis patients

Risk of cardiovascular associated death in dialysis patients is the highest among all other co-morbidities. Improving the identification of patients with the highest cardiovascular risk to design an adequate treatment is, therefore, of utmost importance. There are several non-invasive cardiovascular state biomarkers based on the pulse (pressure) wave propagation properties, but their major determinants are not fully understood. In the current study we aimed to provide a framework to precisely dissect the information available in non-invasively recorded pulse wave in hemodialysis patients. Radial pressure wave profiles were recorded before, during and after two independent hemodialysis sessions in 35 anuric prevalent hemodialysis patients and once in a group of 32 healthy volunteers. Each recording was used to estimate six subject-specific parameters of pulse wave propagation model. Pressure profiles were also analyzed using SphygmoCor software (AtCor Medical, Australia) to derive values of already established biomarkers, i.e. augmentation index and sub-endocardial viability ratio (SEVR). Data preprocessing using propensity score matching allowed to compare hemodialysis and healthy groups. Augmentation index remained on average stable at 142 ± 28% during dialysis and had similar values in both considered groups. SEVR, whose pre-dialytic value was on average lower by 12% compared to healthy participants, was improved by hemodialysis, with post-dialytic values indistinguishable from those in healthy population (p-value > 0.2). The model, however, identified that the patients on hemodialysis had significantly increased stiffness of both large and small arteries compared to healthy counterparts (> 60% before dialysis with p-value < 0.05 or borderline) and that it was only transiently decreased during hemodialysis session. Additionally, correlation-based clustering revealed that augmentation index reflects the shape of heart ejection profile and SEVR is associated with stiffness of larger arteries. Patient-specific pulse wave propagation modeling coupled with radial pressure profile recording correctly identified increased arterial stiffness in hemodialysis patients, while regular pulse wave analysis based biomarkers failed to show significant differences. Further model testing in larger populations and investigating other biomarkers are needed to confirm these findings.

Does the plasma refilling coefficient change during hemodialysis sessions

The filtration coefficient in the Starling equation is an important determinant of plasma refilling during hemodialysis. A method for calculating from clinical data an estimate of the filtration coefficient, called the refilling coefficient, was proposed in the past. The assumption behind this method was that the only drive for refilling is the increase in plasma oncotic pressure, and the remaining Starling forces have negligible effect. The refilling coefficient was observed to decrease during hemodialysis, and this was interpreted as a change in the filtration coefficient. The purpose of our study was providing an alternative explanation for the behavior of the refilling coefficient and, using clinical data and mathematical modeling, to predict the values of the immeasurable Starling forces and provide the theoretical basis for the interpretation of the refilling coefficient as the filtration coefficient. Blood volume and bioimpedance data from 23 patients undergoing hemodialysis were used to calculate the refilling coefficient according to the original formulation and to fit a two-compartment model of protein and fluid transport. The changes in the other Starling forces were non-negligible, ranging from 19% to 60% of plasma oncotic pressure. The results showed that the decrease observed in the refilling coefficient is likely caused by neglecting important changes in the Starling forces while deriving the equation for the refilling coefficient. When these Starling forces were taken into account, constant filtration coefficient and dynamic refilling coefficient provided an equivalent description of the data in most cases. However, this was not true for a subgroup of sessions, which suggests that additional factors may also be responsible for the observed decrease in the refilling coefficient.