Catheter lock heparin concentration: Effects on tissue plasminogen activator use in tunneled cuffed catheters
Bibliographic record
Abstract
The optimal cost-effective heparin concentration for locking tunneled cuffed hemodialysis catheters (TCC) is unclear. We performed a retrospective review of tissue plasminogen activator (tpa) use in TCC in 2 hemodialysis units that used different heparin concentrations for TCC lock to evaluate the effectiveness of lower dose heparin as a lock for TCC. Catheter blood flow rate per treatment, units of heparin given during treatments, patient hemoglobin values and use of warfarin, and tpa use were compared for all patients using TCC for at least 3 months in 2 in-center hemodialysis units between 11/04 and 5/05. Both units used the same type of catheters and biocompatible, non-re-use dialyzers. Unit A used heparin 1000 U/mL for catheter locks, and Unit B used heparin 10,000 U/mL for catheter locks. Twelve of 19 Unit A patients, tpa and 14 of 45 Unit B patients received intracatheter during the study period (p=0.0009). There were no differences in the number of patients on warfarin, treatment blood flow rate, or mean hemoglobin levels between the 2 groups. The mean heparin units given during hemodialysis treatments was higher in Unit A patients (3.92+/-2.2 vs. 3.83+/-2.5 1000 U, p=0.05). Assuming a 4.1 mL total catheter lumen volume, the cost of heparin 1000 U/mL lock was 0.20 dollars per treatment and heparin 10,000 U/mL cost 2.67 dollars/treatment; tpa cost 89.02 dollars/use. Using the 10,000 U/mL heparin as a catheter lock was associated with less frequent use of tpa. However, the significantly lower cost of the 1000 U/mL heparin could result in significant savings despite higher tpa use. This retrospective, uncontrolled study of a small number of patients suggests that comparing low and high heparin concentrations as a TCC lock would be worthwhile. Prospective studies would be helpful to define the most appropriate and cost-effective lock for TCC.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".