The impact of dialysate flow rate on haemodialysis adequacy: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Background Patients with kidney failure treated with maintenance haemodialysis (HD) require appropriate small molecule clearance. Historically, a component of measuring ‘dialysis adequacy’ has been quantified using urea kinetic modelling that is dependent on the HD prescription. However, the impact of dialysate flow rate on urea clearance remains poorly described in vivo and its influence on other patient-important outcomes of adequacy is uncertain. Methods We searched Embase, MEDLINE and the Cochrane Library from inception until April 2022 for randomized controlled trials and observational trials comparing a higher dialysate flow rate (800 ml/min) and lower dialysate flow rate (300 ml/min) with a standard dialysis flow rate (500 ml/min) in adults (age ≥18 years) treated with maintenance HD (>90 consecutive days). We conducted a random effects meta-analysis to estimate the pooled mean difference in dialysis adequacy as measured by Kt/V or urea reduction ratio (URR). Results A total of 3118 studies were identified. Of those, nine met eligibility criteria and four were included in the meta-analysis. A higher dialysate flow rate (800 ml/min) increased single-pool Kt/V by 0.08 [95% confidence interval (CI) 0.05–0.10, P < .00001] and URR by 3.38 (95% CI 1.97–4.78, P < .00001) compared with a dialysate flow rate of 500 ml/min. Clinically relevant outcomes including symptoms, cognition, physical function and mortality were lacking and studies were generally at a moderate risk of bias due to issues with randomization sequence generation, allocation concealment and blinding. Conclusion A higher dialysate flow increased urea-based markers of dialysis adequacy. Additional high-quality research is needed to determine the clinical, economic and environmental impacts of higher dialysate flow rates.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".