Bibliographic Details
| Title: |
A novel kinetic model estimating the urea concentration in plasma during non-invasive sweat-based monitoring in hemodialysis |
| Authors: |
Xiaoyu Yin, Sophie Adelaars, Elisabetta Peri, Eduard Pelssers, Jaap Den Toonder, Arthur Bouwman, Daan Van de Kerkhof, Massimo Mischi |
| Source: |
Frontiers in Physiology, Vol 16 (2025) |
| Publisher Information: |
Frontiers Media S.A., 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Physiology |
| Subject Terms: |
kidney failure, end-stage renal disease, patient monitoring, pharmacokinetic modeling, inverse modeling, Physiology, QP1-981 |
| More Details: |
IntroductionThe adequacy of hemodialysis (HD) in patients with end-stage renal disease is evaluated frequently by monitoring changes in blood urea concentrations multiple times between treatments. As monitoring of urea concentrations typically requires blood sampling, the development of sweat-sensing technology offers a possible less-invasive alternative to repeated venipuncture. Moreover, this innovative technology could enable personalized treatment in a home-based setting. However, the clinical interpretation of sweat monitoring is hampered by the limited literature on the correlation between urea concentrations in sweat and blood. This study introduces a pioneering approach to estimate blood urea concentrations using sweat urea concentration values as input.MethodsTo simulate the complex transport mechanisms of urea from blood to sweat, a novel pharmacokinetic transport model is proposed. Such a transport model, together with a double-loop optimization strategy from our previous work, was employed for patient-specific estimation of blood urea concentration. 32 patient samples of paired sweat and blood urea concentrations, collected both before and after HD, were used to validate the model.ResultsThis resulted in an excellent Pearson correlation coefficient (0.98, 95%CI: 0.95–0.99) and a clinically irrelevant bias (−0.181 mmol/L before and −0.005 mmol/L after HD).DiscussionThis model enabled the accurate estimation of blood urea concentrations from sweat measurements. By accurately estimating blood urea concentrations from sweat measurements, our model enables non-invasive and more frequent assessments of dialysis adequacy in ESRD patients. This approach could facilitate home-based and patient-friendly dialysis management, enhancing patient comfort while enabling more personalized treatment across diverse clinical settings. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1664-042X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fphys.2025.1547117/full; https://doaj.org/toc/1664-042X |
| DOI: |
10.3389/fphys.2025.1547117 |
| Access URL: |
https://doaj.org/article/f08317a53ff34b7f9db8e7331a4518d9 |
| Accession Number: |
edsdoj.f08317a53ff34b7f9db8e7331a4518d9 |
| Database: |
Directory of Open Access Journals |
