Evidence map›Paper›PMID 30592754›Full record

ArticlePloS one2018

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

Mauro Pietribiasi, Jacek Waniewski, Alicja Wójcik-Załuska, Wojciech Załuska, Bengt Lindholm

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Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Mauro PietribiasiNalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, Poland.ORCID 0000-0001-8224-783X
Jacek WaniewskiNalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences, Warsaw, Poland.
Alicja Wójcik-ZałuskaDepartment of Rehabilitation and Physiotherapy, Medical University of Lublin, Lublin, Poland.
Wojciech ZałuskaDepartment of Nephrology, Medical University of Lublin, Lublin, Poland.
Bengt LindholmRenal Medicine and Baxter Novum, Karolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMathematical 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 (Jv,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.

methodsThe 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, JpMAX. From the data we estimated JpMAX and two other parameters linked to transcapillary transport of fluid and protein: the capillary filtration coefficient Lp and its large pores fraction αLP. In an Expanded model one more generic solute was included to evaluate the impact of the number of substances appearing in the equation describing Jv,ISIC.

resultsIn the baseline model, median values (interquartile range) of estimated parameters were: Lp: 11.63 (7.9, 14.2) mL/min/mmHg, αLP: 0.056 (0.050, 0.058), and JpMAX: 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, αLP: 0.046 (0.038, 0.052), and JpMAX: 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) %.

conclusionsThe 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 JpMAX indicating that this parameter is highly sensitive to the number of species modeled, and that the value of JpMAX should be interpreted only in relation to this factor.

Indexed as

Models, BiologicalModels, StatisticalRenal DialysisAdultAgedAged, 80 and overAlgorithmsBiological Transport, ActiveBiomarkersDialysis SolutionsFemaleHumansKidney Failure, ChronicMaleMiddle AgedPotassiumBiomarkersDialysis SolutionsPotassiumSodium

Identifiers

PMID30592754
PMCPMC6310262

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.