Predictive factors for arteriovenous fistula maturation: A prospective study
Bibliographic record
Abstract
INTRODUCTION: Arteriovenous fistula (AVF) maturation failure remains common despite preoperative ultrasound mapping. Identifying predictive biomarkers can help anticipate primary failure and reducing invasive procedures. Our study aimed to identify clinical and analytical risk factors for primary AVF failure or delay. METHODS: A prospective study (October 2022-March 2023) included adult patients scheduled for AVF creation. In all patients, a preoperative ultrasound mapping was conducted and AVF maturation assessed at least 6 weeks post-surgery. Clinical, analytical, and demographic data were collected. FINDINGS: Eighty patients were included, 62.5% male, and mean age 66.3 years. For distal anastomosis, preoperative vein (3.8 ± 1.2 vs. 2.8 ± 0.6 mm; p 0.002) and supply artery (2.5 ± 0.4 vs. 2.0 ± 0.3 mm; p 0.001) diameters were significant factors impacting primary failure. Also, for proximal anastomosis, the artery diameter (2.4 ± 0.4 vs. 2.0 ± 0.4 mm; p 0.01) had an impact on AVF maturation. ROC curves established for distal AVF a vein diameter cutoff of 3.25 mm (AUC 77.2%) and artery cut-off of 2.35 mm (AUC 74.6%) and for proximal AVF an artery cutoff of 2.25 mm (AUC 76.5%). Distal AVF creation correlated with higher primary failure risk (p < 0.001). No correlation was found between the primary failure rate and the presence of central venous catheter or serum results. In a sub analysis, we found that patients with central venous catheter had higher levels of inflammatory markers. DISCUSSION: Our study highlights the importance of preoperative evaluation, ultrasound mapping, and careful AVF site selection. Recognizing vein and artery diameter thresholds for optimal outcomes is crucial. Avoiding central venous catheters in suitable patients can positively impact AVF results.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".