Impact of Dialysate Flow Rates on Dialysis Adequacy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Patients with kidney failure on hemodialysis (HD) require adequate removal of uremic solutes and fluid. Historically, dialysis adequacy is measured by multiplying dialyzer clearance of urea (K) by duration of dialysis session (t) adjusted for patient volume of distribution (V). There are many ways of improving dialysis adequacy, including, increasing dialysis time and frequency, maximizing blood flow rates, and using higher surface area dialyzers. However, the relative impact of dialysate flow rates on dialysis adequacy is poorly described. This systematic review and meta-analysis examines the impact of dialysate flow rates on dialysis adequacy. Methods: We searched EMBASE, MEDLINE, and the Cochrane Library from inception until April 2022 for randomized controlled trials of any design and observational studies comparing higher dialysate flow rates (>500mL/min) and lower dialysate flow rates (<500mL/min) vs. a standard dialysate flow rate (500 mL/min) in adults (age ≥18 years) treated with chronic HD (>90 consecutive days) for the outcome of dialysis adequacy, measured by Kt/V. We used random effects meta-analysis to estimate pooled mean difference in Kt/V at fixed dialysis durations, blood flows and dialyzers. Results: A total of 3118 studies were identified in the literature search. Of those, 11 met eligibility criteria and were included for analysis. In the 10 comparisons (n = 732) of a higher dialysate flow rate (560-800 mL/min) vs. a dialysate flow rate of 500 mL/min, a higher dialysate flow rate was associated with an increase in single pooled Kt/V (spKt/V) of 0.12 (95% CI: 0.06-0.18). In the 2 comparisons (n = 24) of a dialysate flow rate of 500 mL/min vs. a lower dialysate flow rate of 300 mL/min, a dialysate flow rate of 500 mL/min showed an increase in spKt/V (mean difference = 0.16) but limited data precluded a meta-analysis. Conclusions: In our systematic review and meta-analysis, we found a higher dialysate flow rate is associated with an improvement in dialysis adequacy compared with a standard dialysate flow rate. More studies are needed to compare a dialysate flow rate of 500 mL/min vs. a dialysate flow rate of 300 mL/min as some self-care HD systems are unable to attain dialysate flow rates >500 mL/min due to the use of batch-based dialysate or limitations of water systems. Funding: Commercial Support - Quanta Dialysis Technologies
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".