Reducing propofol waste during TIVA by pre-operative estimation of requirement: A single-center retrospective analysis
Authors' conclusion
Using the Eleveld TCI model to estimate pre-operative propofol requirements could significantly reduce waste and avoid excessive syringe changes, even when surgical duration is uncertain.
Study context
- Population
- Adults undergoing GA with conventional propofol TIVA, university tertiary care provider
- Participants
- 229 adults
- Exposure
- Algorithm-guided drawing-up based on estimated propofol requirement (Cet 3.0 or ECBIS45) vs conventional TIVA practice
- Outcome
- Predicted propofol amount for target-controlled infusion (TCI) and waste with three drawing-up strategies (50 ml vial only, 20 ml vial only, vial combination based on estimated requirements)
Methodological detailssingle-centre, in silico TCI-TIVA simulations (Eleveld model), Monte-Carlo simulation
- Single-centre
- In silico TCI-TIVA simulations (Eleveld model)
- Monte-Carlo simulation
Main finding
As reported in the abstract
Predicted propofol waste
- Propofol requirements predicted using Cet 3.0 and ECBIS45 similar to actual consumption
- Algorithm-guided waste comparable to 20 ml vial-only strategy
- Cet 3.0 vs Conventional TIVA: CI −61.0 to −27.0; P < 0.0001
- ECBIS45 vs Conventional TIVA: CI −60.0 to −26.7; P = 0.001
Observational data: associations, not effects.
Secondary outcomes
Syringe changes
- Fewer syringe changes with algorithm-guided drawing-up than 20 ml vial-only strategy
Waste with surgical duration estimation errors (Monte-Carlo)
- Waste significantly lower with errors up to 20% for Cet 3.0
- P = 0.007
- Waste significantly lower with errors up to 30% for ECBIS45
- P = 0.01
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Reducing propofol waste during TIVA by pre-operative estimation of requirement: A single-center retrospective analysis. J Clin Anesth. Published 19 February 2026. doi:10. 1016/ j. jclinane. 2026. 112160
Composed by AI from the published abstract and checked against it. How Briefs are made →