The Impact of Surgical Timing on Outcomes in Patients Undergoing Arteriovenous FistulaCreation for Hemodialysis. A Meta-analysis
Bibliographic record
Abstract
Background: Arteriovenous fistula (AVF) creation is the preferred method for vascular access in hemodialysis patients, offering better long-term patency, fewer complications, and lower infection rates compared to other options. However, the timing of AVF creation plays a critical role in determining outcomes. This meta-analysis examines the impact of surgical timing on AVF success, focusing on primary failure rates, maturation time, patency, and postoperative complications. Objectives: We sought to evaluate whether the timing of AVF creation impacts outcomes in hemodialysis patients and to provide evidence-based recommendations for clinical practice. Methods: Following PRISMA guidelines, a comprehensive meta-analysis was conducted from major databases (PubMed, Embase, Cochrane) until 2024 that included data. We selected studies based on defined criteria, and assessed for quality using the Newcastle-Ottawa Scale and Cochrane Risk of Bias tool. Primary failure rates, maturation time, primary and secondary patency and postoperative complications were analyzed as outcomes. Heterogeneity was addressed with random effects models, and subgroup analyses were carried out on the basis of patient characteristics. Results: Early AVF creation (creating an AVF ≥ 6 months prior to dialysis initiation) was shown to reduced primary failure rate by 50%, shortened maturation time by 2 to 3 weeks and increase primary patency rates by up to 35%. Early AVF creation, on the other hand, was also accompanied by fewer postoperative complications, including infection and thrombosis, versus delayed AVF creation. Consistent benefits were seen for different patient profiles in subgroup analyses. Conclusion: Early AVF creation is associated with superior outcomes, including lower failure rates, quicker maturation, and fewer complications. These findings support early AVF as the preferred practice in hemodialysis care, enhancing long-term patient outcomes and quality of care.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.072 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".