A prospective cohort study of predictors of upper extremity arteriovenous fistula maturation
Bibliographic record
Abstract
BACKGROUND: A substantial proportion of arteriovenous fistulas fail to function adequately for hemodialysis. Existing studies on arteriovenous fistula failure prediction assess patency rather than the more clinically relevant outcome of arteriovenous fistula function. We hypothesized that preoperative demographic and ultrasound characteristics, and postoperative assessment by an experienced vascular access nurse would predict which arteriovenous fistulas will not function adequately for hemodialysis. METHODS: Prospective cohort study of chronic kidney disease patients at a tertiary care center in Vancouver, Canada, with arteriovenous fistula creation between 2009 and 2013. Pre and postoperative clinical assessment and ultrasound blood vessel mapping were performed by an experienced vascular access nurse. The primary outcome was failure to achieve a fistula used successfully for hemodialysis (FUSH). RESULTS: Outcomes were assessed in 200 patients; 123 (61.5%) arteriovenous fistulas were radiocephalic. Overall, 26.5% of arteriovenous fistulas had FUSH failure (34.1% of lower arm vs 14.3% of upper arm, p = 0.002). Univariate predictors of FUSH failure included older age (p = 0.03), female sex (p = 0.05), smaller arterial diameter (p ⩽ 0.001), lower artery volume flow (p = 0.04), and smaller vein diameter (p = 0.01). In multivariable analysis, artery diameter (odds ratio: 0.44, 95% confidence interval: 0.28-0.68) most significantly predicted FUSH failure. Vascular access nurse assessment 6 weeks postoperatively correctly predicted outcome in 83.8% of FUSH and 65.0% of FUSH failure. CONCLUSION: Smaller artery diameter most strongly predicted FUSH failure. Early postoperative nursing assessment was more useful to predict FUSH than FUSH failure, and as such was insufficient in determining which arteriovenous fistulas should be abandoned as many predicted to fail could be salvaged with further intervention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".