A Peritoneal Dialysis Access Quality Improvement Initiative: A Single-Center Experience
Bibliographic record
Abstract
Background Little evidence exists regarding optimal peritoneal dialysis (PD) access insertion pathways, benchmarking for patency targets, and definitions of access dysfunction. Methods This quality improvement (QI) project evaluated patients with PD catheters inserted at a single center in Toronto, Canada, following: establishment of PD catheter insertion protocols, a PD access coordinator, PD access operator training, and outcomes reporting. We define primary vs secondary PD catheter dysfunction by presentation before/after initial home PD treatment. We report catheter dysfunction rates, interventions restoring PD catheter patency (interventional radiology [IR] vs advanced laparoscopic [AL]) (embedded vs non-embedded) between 2012 and 2017. Results A total of 297 first PD catheters were inserted between January 2012 and December 2017. Interventional radiology PD catheters ( n = 94) were placed in older patients with greater comorbidities and less prior abdominal surgery than AL-placed catheters. Indications for IR insertion included need for urgent dialysis given resource availability (36.2% [ n = 34]) and prohibitive surgical risk (26.6% [ n = 25]). Interventional radiology-inserted catheters had overall (primary and secondary) dysfunction rates of 17%. Non-embedded AL catheters had 16.1% overall dysfunction. Embedded AL-inserted PD catheters had a 24.6% overall dysfunction rate. Among all dysfunctional catheters, IR manipulation was successful in 31% ( n = 11), and surgical revision was necessary in all unsuccessful cases with either lysis of adhesions or omentopexy to establish patency. Conclusion Our PD catheter QI initiative involved tracking, outcome reporting, defining PD catheter dysfunction and PD access insertion pathway development, yielding important insights into opportunities for program improvement. Multicenter research initiatives are needed to further improve PD access dysfunction definitions and to establish the best benchmarks for these metrics.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".