Factors associated with unsuccessful utilization and early failure of the arterio-venous fistula for hemodialysis
Bibliographic record
Abstract
BACKGROUND: Arteriovenous fistulae survive longer than grafts and catheters. However, their short term outcomes may not be as good. We sought to determine whether fistulae created in patients referred to a nephrologist less than 3 months before dialysis start show higher risk of unsuccessful use and early failure. METHODS: All patients receiving a new vascular access over a six-year period at three centres were enrolled. Logistic and Cox's regression techniques were used to model late referral on successful utilization for <6 consecutive HD-sessions and time to failure within the first month from access creation, adjusting for demographics, comorbidities and surgical strategies. RESULTS: Among the 535 subjects enrolled, 513 received a fistula. Without considering revisions, 119 fistulae (23.2%) were not successfully used and 61 (11.9%) failed early. Independent predictors of unsuccessful utilization were late referral (Odds Ratio 2.15 [95% Confidence Interval 1.23, 3.75]), vascular diseases (1.86 [1.16, 2.97]), absence of treated hypertension (2.07 [1.17, 3.68]), and heart failure limited to late referrals (10.74 [4, 28.82]). Late referral (Hazard Ratio 1.72 [1.05, 2.81]), absence of treated hypertension (1.80 [1.02, 3.18]) and heart failure (2.34 [1.34, 4.08]) also predicted primary early failure. CONCLUSIONS: Late patient referral and presence of cardiovascular diseases, particularly heart failure, are potentially modifiable risk factors for short-term outcomes improvement of hemodialysis fistulae.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".