title: Influence of real-time Bayesian forecasting of pharmacokinetic parameters on the precision of a rocuronium target-controlled infusion.
abstract: BACKGROUND
  Bayesian forecasting has been shown to improve the accuracy of pharmacokinetic/pharmacodynamic (PK/PD) models by adding measured values to a population model. It could be done in real time for neuromuscular blockers (NMB) using measured values of effect. This study was designed to assess feasibility and benefit of Bayesian forecasting during a rocuronium target-controlled infusion (TCI).
  
  
  METHODS
  After internal review board (IRB) approval and informed consent, 21 women scheduled for breast plastic surgery were included. Anesthesia was maintained with propofol, alfentanil, and controlled ventilation through a laryngeal mask. Rocuronium was delivered in TCI with Stanpump software and the Plaud population model. The target effect was 50% blockade until insertion of breast prosthesis; thereafter it was set to 0%. Response to train of four (TOF) at adductor pollicis was recorded using a force transducer. In ten patients, drug delivery was based on the population model. In the others, repeated measures values were entered in the software, and the PK model was adjusted to minimize the error in predicted effect. Model precision was compared between groups using mean prediction error and mean absolute prediction error.
  
  
  RESULTS
  At target 50%, model accuracy was not improved with Bayesian adjustments; conversely, post-infusion errors were significantly decreased. The first two measures had the most influence on the model changes.
  
  
  CONCLUSIONS
  Below clinical utility, such adjustments may be used to explore cofactors influencing interindividual and intraindividual variability in NMB dose-response relationship. Similar tools may also be developed for drugs in which a quantitative effect is available, such as electroencephalography (EEG) for hypnotics.
  
  
  CONCLUSIONS
  Real-time Bayesian forecasting combining measured values of effect with a population model is suitable to guide NMB-agent delivery using Stanpump software.
authors.affiliation: Department of Anesthesiology, Gustave Roussy Institute, Villejuif, France. motamed@igr.fr
authors.forename: Cyrus
authors.initials: C
authors.lastname: Motamed
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Devys
authors.forename: Bertrand
authors.initials: B
authors.lastname: Debaene
authors.forename: Valérie
authors.initials: V
authors.lastname: Billard
mesh_terms: D000465:Algorithms; D000732:Androstanols / Q000008:administration & dosage* / Q000493:pharmacokinetics* / Q000627:therapeutic use; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D016503:Drug Delivery Systems / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D007260:Infusion Pumps; D007262:Infusions, Intravenous; D008875:Middle Aged; D008954:Models, Biological; D016343:Monitoring, Intraoperative / Q000379:methods; D061886:Neuromuscular Monitoring / Q000379:methods; D003473:Neuromuscular Nondepolarizing Agents / Q000008:administration & dosage* / Q000493:pharmacokinetics* / Q000627:therapeutic use; D011446:Prospective Studies; D000077123:Rocuronium; D012984:Software; D013997:Time Factors
chemical_list: D000732:Androstanols; D003473:Neuromuscular Nondepolarizing Agents; D000077123:Rocuronium