Anesthetic drugs & techniques

Reducing propofol waste during TIVA by pre-operative estimation of requirement: A single-center retrospective analysis

J Clin AnesthPublished Retrospective cohort study

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 →

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