The Long‐Term Patency Rate and Factors Influencing Dysfunction of the Autogenous Arteriovenous Fistula in Hemodialysis Patients: A Retrospective Study
Bibliographic record
Abstract
INTRODUCTION: Autogenous arteriovenous fistula (AV fistula) dysfunction continues to be a widespread clinical challenge, adversely impacting both patients and society as a whole. The aim of this study was to investigate the long-term patency rate of AV fistula, explore the factors that contribute to AV fistula dysfunction, and present the findings in a way that can guide clinical practice. METHODS: This retrospective cohort study enrolled patients who underwent AV fistula creation and subsequent hemodialysis at a tertiary A-level hospital in Chongqing, China. Demographic, clinical, and laboratory characteristics of the patients, as well as AV fistula dysfunction, were retrospectively reviewed from electronic health records. Cox proportional hazards regression analysis was used to analyze the factors influencing AV fistula dysfunction, and a forest plot was created to visualize the results. Additionally, Kaplan-Meier survival analysis was used to analyze AV fistula survival. FINDINGS: This study analyzed 226 patients undergoing hemodialysis, demonstrating cumulative AV fistula patency rates of 82.1% at 12 months, 60.7% at 36 months, 45.4% at 60 months, and 33.5% at 84 months. Univariate Cox proportional hazard regression analysis identified six variables associated with AV fistula dysfunction (p < 0.1): body mass index (BMI), preemptive AV fistula creation, diabetes, total cholesterol, albumin, and uric acid. Subsequent multivariate analysis revealed four independent predictors for dysfunction: elevated BMI (HR: 1.58, p = 0.016), preemptive AV fistula creation (HR: 0.67, p = 0.029), albumin (HR: 2.83, p < 0.001), and uric acid (HR: 1.57, p = 0.020). DISCUSSION: Our study findings indicated that overweight, hypoalbuminemia, and high concentrations of uric acid were independent risk factors for AV fistula dysfunction. In contrast, preemptive AV fistula creation was an independent protective factor against AV fistula dysfunction. Therefore, early interventions and surveillance for these factors should be performed to improve long-term AV fistula patency rates.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".