Is hemodiafiltration superior to high‐flow hemodialysis in reducing all‐cause and cardiovascular mortality in kidney failure patients? A meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Introduction Hemodiafiltration (HDF) and high‐flux hemodialysis (hf‐HD) are different methods of kidney replacement therapy (KRT) used for the treatment of kidney failure patients. A debate has raged over the last decade about the survival benefit of patients with the use of HDF compared with hf‐HD, but with divergent results from randomized controlled trials. Therefore, this study aimed to perform a meta‐analysis to compare HDF and hf‐HD regarding all‐cause and cardiovascular mortality. Methods PubMed and Cochrane databases were searched until July 19, 2023, for randomized clinical trials comparing HDF and hf‐HD in patients on maintenance dialysis. A meta‐analysis was performed using Stata 16.1, applying fixed or random effect models according to the heterogeneity between studies. Findings Of the 496 studies found, five met the inclusion criteria. Compared with the hf‐HD group, the risk ratio (RR) for all‐cause mortality with HDF use was 0.76 (95% CI: 0.67–0.88, I2 = 0%). HDF was associated with lower cardiovascular mortality, although the sensitivity analysis showed that the result differed between scenarios. Subgroup analysis showed lower all‐cause mortality among patients without diabetes in the HDF group compared with hf‐HD (RR 0.66, 95% CI: 0.51–0.81, I2 = 0%), but not in diabetic patients (RR = 0.89, 95% CI: 0.65–1.12, I2 = 0.0%). A subgroup analysis considering convection volumes was not performed, but the studies with the highest weight in the meta‐analysis described convection volume as more than 20 L/session. Discussion More clinical studies considering critical risk factors, such as advanced age and preexisting cardiovascular disease, are needed to confirm the supremacy of HDF over hf‐HD on the survival of patients treated by these two forms of kidney replacement therapy.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.062 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".