Improving arteriovenous fistula rate: Effect on hemodialysis quality
Bibliographic record
Abstract
Vascular access (VA) is the lifeline for patients with end-stage renal disease on regular hemodialysis (HD). Tunneled catheters have been associated with increased risk of luminal thrombosis, infection, hospitalization, and high cost. Our aims were to follow the "Fistula First Initiative," avoid or reduce the rate of catheter insertion, improve the rate of arteriovenous fistula (AVF) use, and study the effect of increased AVF use on quality of dialysis and patient's outcome. A VA program has been established in collaboration with an enthusiastic and professional vascular surgery team to manage 358 patients who have been on regular HD treatment for a period ranging from 1 to 252 months. The mean ± standard deviation age of patients was 52 ± 15 years with 62% male patients. Over a period of 2 years, 408 procedures were performed. These include 293 AVFs and 56 arteriovenous grafts (AVGs). Other procedures include 39 permanent catheter insertions, 8 AVF aneurysmectomy, removal of 6 AVGs, embolectomy of 4 AVGs, excision of 1 AVG lymphocele, and ligation of 1 AVF. This program resulted in significant increase in AVF rate from 35% to 82%; reduction in catheter rate from 62% to 10.9%; infection rate down from 6.6% to 0.6%; VA clotting down from 5.1% to 1.0%; and increase in average blood flow rate from 214 ± 32 to 298 ± 37 mL/min (P < 0.01). These results have been associated with improved average single pool Kt/V from 0.88 ± 0.19 to 1.28 ± 0.2 (P < 0.01); increased hemoglobin from 9.2 ± 1.2 to 10.9 ± 0.9 g/dL (P < 0.01); improved serum albumin from 3.2 ± 0.5 to 3.7 ± 0.4 g/dL (P < 0.05); reduction in administered erythropoietin dose by 19%; and significant drop in hospitalization rate from 6.1% to 3.8%. These results confirm the great benefits of AVF on quality of HD and patient outcome, and clearly affirm that AVF should always be considered first.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".