Complications of the Arteriovenous Fistula: A Systematic Review
Bibliographic record
Abstract
The implementation of patient-centered care requires an individualized approach to hemodialysis vascular access, on the basis of each patient’s unique balance of risks and benefits. This systematic review aimed to summarize current literature on fistula risks, including rates of complications, to assist with patient-centered decision making. We searched Medline from 2000 to 2014 for English-language studies with prospectively captured data on ≥100 fistulas. We assessed study quality and extracted data on study design, patient characteristics, and outcomes. After screening 2292 citations, 43 articles met our inclusion criteria (61 unique cohorts; n >11,374 fistulas). Median complication rates per 1000 patient days were as follows: 0.04 aneurysms (14 unique cohorts; n =1827 fistulas), 0.11 infections (16 cohorts; n >6439 fistulas), 0.05 steal events (15 cohorts; n >2543 fistulas), 0.24 thrombotic events (26 cohorts; n =4232 fistulas), and 0.03 venous hypertensive events (1 cohort; n =350 fistulas). Risk of bias was high in many studies and event rates were variable, thus we could not present pooled results. Studies generally did not report variables associated with fistula complications, patient comorbidities, vessel characteristics, surgeon experience, or nursing cannulation skill. Overall, we found marked variability in complication rates, partly due to poor quality studies, significant heterogeneity of study populations, and inconsistent definitions. There is an urgent need to standardize reporting of methods and definitions of vascular access complications in future clinical studies to better inform patient and provider decision making.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".