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Record W4412817074 · doi:10.1177/20543581251356182

The Effect of Dialysate Bicarbonate Concentration or Oral Bicarbonate Supplementation on Outcomes in Patients on Maintenance Dialysis: A Systematic Review and Meta-Analysis

2025· review· en· W4412817074 on OpenAlexaff
Ashlee M. Azizudin, Samuel A. Silver, Amit X. Garg, Zoe K. Friedman, Andrea Cowan, Catherine M. Clase, Amber O. Molnar

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2025
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsWestern UniversityMcMaster UniversityQueen's UniversityImpact
Fundersnot available
KeywordsBicarbonateMedicineMeta-analysisDialysisNephrologyIntensive care medicineInternal medicineSodium bicarbonateUrologyChemistry

Abstract

fetched live from OpenAlex

Background: Metabolic acidosis is a common complication of kidney failure that is treated with bicarbonate supplementation. The addition of bicarbonate to the dialysis solution and oral bicarbonate supplementation are used to treat metabolic acidosis in patients receiving dialysis, but the treatment approach that is best for patient health remains unknown. Objective: The purpose of this study was to determine whether the concentration of dialysate bicarbonate or the use of oral bicarbonate supplementation alters the risk of mortality, hospitalizations, cardiovascular and nutritional outcomes, and laboratory measurements in patients treated with maintenance dialysis. Design: Systematic review and meta-analysis. Setting: Any country of origin. Patients: Adult patients (≥18 years) receiving maintenance dialysis. Measurements: Extracted data included demographic characteristics and outcomes such as mortality, hospitalizations, cardiovascular events, surrogate markers of nutrition, and pre-dialysis and post-dialysis levels of serum bicarbonate, pH, calcium, potassium, and parathyroid hormone. Methods: We searched MEDLINE, Embase, CENTRAL, and Google Scholar through October 7, 2024 for studies examining dialysate bicarbonate concentration and/or oral bicarbonate supplementation in adults undergoing maintenance dialysis. Meta-analysis was performed for pre-dialysis serum bicarbonate and for pre-dialysis and post-dialysis calcium and potassium. Results: We identified 37 studies (n = 24,782 patients) with patients treated with hemodialysis (13 randomized trials, 10 non-randomized interventional studies, 14 observational studies) and 4 studies (n = 347 patients) with patients receiving peritoneal dialysis (3 randomized trials, 1 non-randomized interventional study). No randomized trials reported mortality or hospitalizations in hemodialysis patients. Studies reporting cardiovascular outcomes (n = 20) were small with inconsistent results. Most studies reporting nutritional outcomes (n = 21) reported no significant differences with dialysate bicarbonate concentration or oral bicarbonate supplementation but were small in sample size (largest study n = 200). Meta-analysis of parallel-group randomized trials comparing dialysate bicarbonate >35 mmol/L with ≤35 mmol/L found a mean difference of 3.5 mmol/L (95% confidence interval [CI] -0.6 to 7.7) in pre-dialysis serum bicarbonate. Limitations: Non-English and gray literature were excluded. Most studies were small or observational in nature, and heterogeneity further limited the ability to perform meta-analysis of outcomes such as mortality, hospitalizations, and cardiovascular outcomes. Conclusions: The evidence for the effect of higher vs lower dialysate bicarbonate concentration and oral bicarbonate supplementation on clinical outcomes in dialysis patients is very uncertain. There is a need for large, high-quality randomized controlled trials in this area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0180.034
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.345
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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