Cost‐utility analysis of 4% tetrasodium ethylenediaminetetraacetic acid, taurolidine, and heparin lock to prevent central line–associated bloodstream infections in children with intestinal failure
Bibliographic record
Abstract
BACKGROUND: Central line-associated bloodstream infections (CLABSI) are a serious complication in children with intestinal failure. This study assessed the incremental costs of 4% tetrasodium ethylenediaminetetraacetic acid (EDTA) compared with taurolidine lock and heparin lock per quality-adjusted life-year (QALY) gained in children with intestinal failure from the healthcare payer and societal perspective. METHODS: A Markov cohort model of a 1-year-old child with intestinal failure was simulated until the age of 17 years (time horizon), with a cycle length of 1 month. The health outcome measure was QALYs, with results expressed in terms of incremental costs and QALYs. Model parameters were obtained from published literature and institutional data. Deterministic, probabilistic, and scenario sensitivity analyses were performed. RESULTS: 4% Tetrasodium EDTA was dominant (more effective and less expensive) compared with taurolidine and heparin, yielding an additional 0.17 QALYs with savings of CAD$88,277 compared with heparin, and an additional 0.06 QALYs with savings of CAD$52,120 compared with taurolidine lock from the healthcare payer perspective. From the societal perspective, 4% tetrasodium EDTA resulted in savings of CAD$90,696 compared with heparin and savings of CAD$36,973 compared with taurolidine lock. CONCLUSIONS: This model-based analysis indicates that 4% tetrasodium EDTA can be considered the optimal strategy compared with taurolidine and heparin in terms of cost-effectiveness. The decision uncertainty can be reduced by conducting further research on the model input parameters. An expected value of perfect information analysis can identify what model input parameters would be most valuable to focus on.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".