The Evaluation of Filtration Coefficients of Microvasculature for the Assessment of Fluid Status in Hemodialysis Patients

Vascular refilling coefficient is not a good marker of whole-body capillary hydraulic conductivity in hemodialysis patients: insights from a simulation study

Refilling of the vascular space through absorption of interstitial fluid by micro vessels is a crucial mechanism for maintaining hemodynamic stability during hemodialysis (HD) and allowing excess fluid to be removed from body tissues. The rate of vascular refilling depends on the imbalance between the Starling forces acting across the capillary walls as well as on their hydraulic conductivity and total surface area. Various approaches have been proposed to assess the vascular refilling process during HD, including the so-called refilling coefficient (Kr) that describes the rate of vascular refilling per changes in plasma oncotic pressure, assuming that other Starling forces and the flow of lymph remain constant during HD. Several studies have shown that Kr decreases exponentially during HD, which was attributed to a dialysis-induced decrease in the whole-body capillary hydraulic conductivity (L_pS). Here, we employ a lumped-parameter mathematical model of the cardiovascular system and water and solute transport between the main body fluid compartments to assess the impact of all Starling forces and the flow of lymph on vascular refilling during HD in order to explain the reasons behind the observed intradialytic decrease in Kr. We simulated several HD sessions in a virtual patient with different blood priming procedures, ultrafiltration rates, session durations, and constant or variable levels of L_pS. We show that the intradialytic decrease in Kr is not associated with a possible reduction of L_pS but results from the inherent assumption that plasma oncotic pressure is the only variable Starling force during HD, whereas in fact other Starling forces, in particular the oncotic pressure of the interstitial fluid, have an important impact on the transcapillary fluid exchange during HD. We conclude that Kr is not a good marker of L_pS and should not be used to guide fluid removal during HD or to assess the fluid status of dialysis patients.

Model of fluid and solute shifts during hemodialysis with active transport of sodium and potassium

Background

Mathematical models are useful tools to predict fluid shifts between body compartments in patients undergoing hemodialysis (HD). The ability of a model to accurately describe the transport of water between cells and interstitium (J_v,ISIC), and the consequent changes in intracellular volume (ICV), is important for a complete assessment of fluid distribution and plasma refilling. In this study, we propose a model describing transport of fluid in the three main body compartments (intracellular, interstitial and vascular), complemented by transport mechanisms for proteins and small solutes.

Methods

The model was applied to data from 23 patients who underwent standard HD. The substances described in the baseline model were: water, proteins, Na, K, and urea. Small solutes were described with two-compartment kinetics between intracellular and extracellular compartments. Solute transport across the cell membrane took place via passive diffusion and, for Na and K, through the ATPase pump, characterized by the maximum transport rate, Jp_MAX. From the data we estimated Jp_MAX and two other parameters linked to transcapillary transport of fluid and protein: the capillary filtration coefficient Lp and its large pores fraction α_LP. In an Expanded model one more generic solute was included to evaluate the impact of the number of substances appearing in the equation describing J_v,ISIC.

Results

In the baseline model, median values (interquartile range) of estimated parameters were: Lp: 11.63 (7.9, 14.2) mL/min/mmHg, α_LP: 0.056 (0.050, 0.058), and Jp_MAX: 5.52 (3.75, 7.54) mmol/min. These values were significantly different from those obtained by the Expanded model: Lp: 8.14 (6.29, 10.01) mL/min/mmHg, α_LP: 0.046 (0.038, 0.052), and Jp_MAX: 16.7 (11.9, 25.2) mmol/min. The relative RMSE (root mean squared error)averaged between all simulated quantities compared to data was 3.9 (3.1, 5.6) %.

Conclusions

The model was able to accurately reproduce most of the changes observed in HD by tuning only three parameters. While the drop in ICV was overestimated by the model, the difference between simulations and data was less than the measurement error. The biggest change in the estimated parameters in the Expanded model was a marked increase of Jp_MAX indicating that this parameter is highly sensitive to the number of species modeled, and that the value of Jp_MAX should be interpreted only in relation to this factor.

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.

Hemodialysis Ultrafiltration Rate Targets Should Be Scaled to Body Surface Area Rather than to Body Weight

The association between higher ultrafiltration rates and poor outcomes in hemodialysis patients has received increased attention, to the point that various regulatory entities are considering adding ultrafiltration rate as a quality measure to be monitored and controlled. Most of the discussion to date has focused on ultrafiltration rate scaled to body weight, or more correctly, body mass (ml/hour per kg). One outcome study suggests that ultrafiltration rate might best be not scaled at all to body size, as modestly higher ultrafiltration rate in very small-size patients may be associated with some survival benefit, probably via increased dietary intake. Outcomes studies also suggest that the risk of exceeding a weight-scaled ultrafiltration target may be magnified in very large patients, and that body weight-scaled ultrafiltration targets in such patients should be set a lower level. Here, we present an analysis, based on physiological hemodynamic arguments, that it would be better to scale ultrafiltration rate to body surface area rather than to body mass. Whatever ultrafiltration rate is scaled to, attempts to restrict ultrafiltration rate by limiting interdialytic weight gain in small, possibly malnourished patients, should be done cautiously, to prevent an inadvertent lowering of intake of calories and dietary protein.