Mauro Pietribiasi

Kinetics of plasma refilling during hemodialysis sessions with different initial fluid status.

Removal of fluid excess from the plasma volume by ultrafiltration during hemodialysis (HD) is balanced by plasma refilling from the interstitium, driven mainly by the increase in plasma oncotic pressure. We calculated the plasma refilling coefficient (Kr, a parameter expressing the ratio of refilling rate to the increase in oncotic pressure) for nine patients, each undergoing two HD sessions differing by pretreatment fluid status and session time (shorter session, SH, 3.5 h, and longer session, LH, 4.5h). Relative blood volume change was measured online, and solute concentrations were measured regularly during the sessions. The volume of body compartments was measured by bioimpedance. The patients were more volume expanded before LH session (higher initial body mass and total body water). Oncotic pressure was similar for both sessions. The refilling rate, despite higher fluid overload in the LH sessions, was similar for both sessions. The final Kr values stabilized on similar levels (SH: 136.6 ± 55.6 ml/mm Hg/h and LH: 150.7 ± 73.6 ml/mm Hg/h) at similar times, notwithstanding the difference in initial fluid overload between the two groups, suggesting that Kr at dry weight is relatively insensitive to the initial fluid status of the patient.

Can the three pore model correctly describe peritoneal transport of protein

The three pore model (3PM) includes large pores for the description of protein leak to the peritoneal cavity during peritoneal dialysis. However, the reliability of this description has been not fully tested against clinical data yet. Peritoneal transport parameters were estimated using 3PM, extended 3p model (with estimation of fraction of large pores, ext3PM), ext3PM with modified size of pores and proteins (mext3PM), and simplified two pore (2PM, small and ultrasmall pores) models for 32 patients on peritoneal dialysis investigated using the sequential peritoneal equilibration test (consecutive peritoneal equilibration test [PET]: glucose 2.27%, 4 h, and miniPET: glucose 3.86%, 1 h). Urea, creatinine, glucose, sodium, phosphate, albumin, and IgM concentrations were measured in dialysis fluid and plasma. Ext3PM and mext3PM, with large pore fraction of about 0.14, provided a good description of fluid and small solute kinetics, but their predictions for albumin transport were less accurate. Two pore model precisely described the data on fluid and small solute transport. The 3p models could not describe the diffusive–convective transport of albumin as precisely as the transport of fluid, small solutes, and IgM. The 2p model (not applicable for proteins) was an efficient tool for modeling fluid and small solute transport.

Modelling Transcapillary Transport of Fluid and Proteins in Hemodialysis Patients.

Background The kinetics of protein transport to and from the vascular compartment play a major role in the determination of fluid balance and plasma refilling during hemodialysis (HD) sessions. In this study we propose a whole-body mathematical model describing water and protein shifts across the capillary membrane during HD and compare its output to clinical data while evaluating the impact of choosing specific values for selected parameters. Methods The model follows a two-compartment structure (vascular and interstitial space) and is based on balance equations of protein mass and water volume in each compartment. The capillary membrane was described according to the three-pore theory. Two transport parameters, the fractional contribution of large pores (αLP) and the total hydraulic conductivity (LpS) of the capillary membrane, were estimated from patient data. Changes in the intensity and direction of individual fluid and solute flows through each part of the transport system were analyzed in relation to the choice of different values of small pores radius and fractional conductivity, lymphatic sensitivity to hydraulic pressure, and steady-state interstitial-to-plasma protein concentration ratio. Results The estimated values of LpS and αLP were respectively 10.0 ± 8.4 mL/min/mmHg (mean ± standard deviation) and 0.062 ± 0.041. The model was able to predict with good accuracy the profiles of plasma volume and serum total protein concentration in most of the patients (average root-mean-square deviation < 2% of the measured value). Conclusions The applied model provides a mechanistic interpretation of fluid transport processes induced by ultrafiltration during HD, using a minimum of tuned parameters and assumptions. The simulated values of individual flows through each kind of pore and lymphatic absorption rate yielded by the model may suggest answers to unsolved questions on the relative impact of these not-measurable quantities on total vascular refilling and fluid balance.

Peritoneal Fluid Transport rather than Peritoneal Solute Transport Associates with Dialysis Vintage and Age of Peritoneal Dialysis Patients

During peritoneal dialysis (PD), the peritoneal membrane undergoes ageing processes that affect its function. Here we analyzed associations of patient age and dialysis vintage with parameters of peritoneal transport of fluid and solutes, directly measured and estimated based on the pore model, for individual patients. Thirty-three patients (15 females; age 60 (21–87) years; median time on PD 19 (3–100) months) underwent sequential peritoneal equilibration test. Dialysis vintage and patient age did not correlate. Estimation of parameters of the two-pore model of peritoneal transport was performed. The estimated fluid transport parameters, including hydraulic permeability (LpS), fraction of ultrasmall pores (α u), osmotic conductance for glucose (OCG), and peritoneal absorption, were generally independent of solute transport parameters (diffusive mass transport parameters). Fluid transport parameters correlated whereas transport parameters for small solutes and proteins did not correlate with dialysis vintage and patient age. Although LpS and OCG were lower for older patients and those with long dialysis vintage, αu was higher. Thus, fluid transport parameters—rather than solute transport parameters—are linked to dialysis vintage and patient age and should therefore be included when monitoring processes linked to ageing of the peritoneal membrane.

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.

Changes of peritoneal transport parameters with time on dialysis: assessment with sequential peritoneal equilibration test

Background Sequential peritoneal equilibration test (sPET) is based on the consecutive performance of the peritoneal equilibration test (PET, 4-hour, glucose 2.27%) and the mini-PET (1-hour, glucose 3.86%), and the estimation of peritoneal transport parameters with the 2-pore model. It enables the assessment of the functional transport barrier for fluid and small solutes. The objective of this study was to check whether the estimated model parameters can serve as better and earlier indicators of the changes in the peritoneal transport characteristics than directly measured transport indices that depend on several transport processes. Methods 17 patients were examined using sPET twice with the interval of about 8 months (230 ± 60 days). Results There was no difference between the observational parameters measured in the 2 examinations. The indices for solute transport, but not net UF, were well correlated between the examinations. Among the estimated parameters, a significant decrease between the 2 examinations was found only for hydraulic permeability LpS, and osmotic conductance for glucose, whereas the other parameters remained unchanged. These fluid transport parameters did not correlate with D/P for creatinine, although the decrease in LpS values between the examinations was observed mostly for patients with low D/P for creatinine. Conclusions We conclude that changes in fluid transport parameters, hydraulic permeability and osmotic conductance for glucose, as assessed by the pore model, may precede the changes in small solute transport. The systematic assessment of fluid transport status needs specific clinical and mathematical tools beside the standard PET tests.