Comparison of Intensive versus Standard Hemodialysis Central Venous Catheter Dysfunction Protocol Using rt-PA: A Quality Assurance Initiative
Bibliographic record
Abstract
PURPOSE: Catheter locking solutions such as recombinant tissue plasminogen activator (rt-PA) are used to treat and prevent clotting of hemodialysis (HD) catheters during HD treatments and the interdialytic period. However, evidence to guide the use of rt-PA for catheter dysfunction is limited. METHODS: We evaluated outcomes using two catheter dysfunction protocols in a cohort of consecutive prevalent dialysis patients (Jan 2013 to Sep 2014) undergoing HD with a tunneled catheter. In the intensive protocol, rt-PA was administered to all catheters based on blood flow and/or line reversal. In the standard protocol, rt-PA administration was based only on blood flow. The primary outcome was the rate of rt-PA use for catheter malfunction (rt-PA treatment days/1000 total line days; [TLD]). Secondary outcomes included the cost of rt-PA/TLD and the rate of catheter-related bacteremia. RESULTS: There were 26 and 35 patients managed by the intensive and standard protocols, respectively. The rate of rt-PA use was 52/1000 TLD (intensive) versus 39/1000 TLD (standard) (rate ratio 1.30, 95% CI 1.12-1.52 CI, p<0.001). The rate of bacteremia was 0.43 and 0.22/1000 TLD for the intensive and standard protocols, respectively (p = 0.491). The cost of rt-PA was CDN $5.58 and CDN $6.15 per TLD for the intensive protocol and standard protocol groups (p<0.001). CONCLUSIONS: Managing catheter dysfunction based on line reversal and blood flow as opposed to only blood flow was associated with a higher rate of rt-PA use, but at a reduced overall cost.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".