Exploring Urea Clearance Measurement in a Wearable Sorbent-Based Peritoneal Dialysis (PD) Device
Bibliographic record
Abstract
Background: In recent times, evaluating dialysis adequacy has shifted from a single solute target, i.e. Kt/Vurea, to a more holistic assessment of patients. However, some jurisdictions still mandate Kt/Vurea targets for reimbursement purposes. Methods: Viva Kompact (VK) is a wearable, sorbent-based PD device. It regenerates 250mL of dialysate at a flow rate of 2L/hr over 56 cycles. The urea removal process in the sorbent during regeneration of dialysate makes it challenging to accurately assess solute removal, making standard PD Kt/Vurea formula of drained dialysate non-representative of the actual dialysis dose delivered. During the pre-pivotal study, VK clearance dynamics was explored – multiple outflow (leaving peritoneum) and inflow samples were taken at Cycle 4, 8, 40 and 54. These were done during full day training sessions and the first day of the 7-day Treatment period. Results: 12 subjects’ data were analysed; 10 male, dialysis vintage: 7-94 months, transport status: 1 High, 7 High A., 3 Low A., 1 Low and 1 anuric. Figure 1 shows urea levels throughout 7 hours of tidal therapy of 50 therapies, averaged across each subject; outflow urea during tidal therapy were lower than in final drain, supporting the notion that using final drain urea in the Kt/Vurea formula would not be representative of tidal clearance. During inflow, urea was mostly at undetected levels(<0.8mmol/L). As such, to cater for differences, it was proposed that tidal clearance be calculated separately from final drain clearance with a modified formula (Figure 2): Conclusion: This study highlights the need of a modified Kt/Vurea formula for sorbent-based dialysis. While it is still used as a measure of PD efficacy, the quality of PD should be approached holistically, without relying only on small-solute clearances. Funding: Commercial Support - Vivance Pte LtdFigure 1Figure 2
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".