Risk Factors Associated with Arteriovenous Fistula Failure after First Radiologic Intervention
Bibliographic record
Abstract
PURPOSE: Improving arteriovenous fistula (AVF) patency is an integral part of the care of hemodialysis patients, often requiring procedures such as percutaneous transluminal angioplasty (PTA). However, these interventions may fail to reduce AVF dysfunction and failure. The purpose of this study was to determine predictive factors for subsequent AVF failure post-PTA. METHODS: Data from 155 consecutive AVFs in 155 patients at a single institution who had undergone a first PTA and had at least 1 year of follow-up data were analyzed. Using survival analysis, we assessed primary and secondary patency, and identified predictive factors taking into account competing risks. RESULTS: Of the 155 patients, 52% required multiple subsequent PTAs; 32% of the AVFs were not in use prior to the first PTA. At first PTA, 83% had outflow vein stenosis (OVS), 26% had multiple stenoses and 43% of stenoses were longer than 2 cm. During follow-up, 1-, 2-, 3-year postintervention primary patency was 41%, 32%, 32% and secondary patency was 80%, 71% and 68%. AVFs with stenoses greater than 2 cm or OVS were at higher risk of requiring multiple PTAs (p = 0.04, 0.006). Factors associated with requiring a second PTA included stenosis greater than 2 cm (hazard ratio (HR) = 1.8, 95% confidence interval (CI) = 1.2-2.9), OVS (HR = 2.5, 95% CI = 1.1-5.4) and primary renal diagnosis of diabetes or renal vascular diseases (HR = 1.8, 95% CI = 1.1-2.9); after adjustments for competing risks, OVS and stenosis length remained associated with requiring subsequent PTAs. CONCLUSIONS: The location and size of the AVF stenosis at first PTA appear to be consistent factors associated with worse postintervention primary patency.
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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 | 0.007 |
| 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) | 0.002 | 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".