Long-Term Intervention Rate with Drug-Coated Balloons for Dysfunctional Arteriovenous (AV) Fistulas: Meeting KDOQI Targets in the IN.PACT AV Access Trial
Bibliographic record
Abstract
Background: A key KDOQI Guidelines target is to have ≤3 percutaneous or surgical interventions per year to maintain AV fistula (AVF) patency. We aimed to define the intervention and thrombosis rates per AVF-years through 36 months from the IN.PACT AV Access trial. Methods: This global, 29-center, single-blinded pivotal study randomized participants with de novo or non-stented restenotic obstructive lesion(s) of upper extremity AVF 1:1 to treatment with an IN.PACT AV paclitaxel drug-coated balloon (DCB; n=170) or standard percutaneous transluminal angioplasty (PTA; n=160). Participants were followed for 36 months; intervention rates and fistula-years of use were captured and compared to KDOQI guidelines target. 36 month thrombosis rates were compared between DCB and PTA groups. Results: Of the 330 participants randomized, 133 completed their 3-year visit. Including the index procedure, the rates of intervention ranged 1.24-2.55 per AVF-year for DCB group and 1.48-3.06 per AVF-year for PTA group (Table). The cumulative incidence rate of access circuit thrombosis at 36 months was 8.2% (10) in DCB group and 18.3% (19) in PTA group with a hazard ratio of 0.457 (95% confidence interval: 0.212- 0.983; P = 0.04). Conclusions: The need for interventions to maintain patency and thrombosis rate was reduced with use of DCB compared with PTA at the 1-, 2-, and 3-year timepoints post index procedure for the treatment of dysfunctional AVF. At the 3-year timepoint, both DCB and PTA groups met the KDOQI targets for AVF interventions per year to maintain patency. Number of target lesion interventions per fistula yearsAVF, arteriovenous fistula; DCB, drug-coated balloon; No, number; PTA, percutaneous transluminal angioplasty.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".