A linear model for estimating propofol individualized dosage

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

Rocha, C (Autor) Outra; Mendonca, T (Autor) FCUP; De Oliveira, M (Autor) Outra; Silva, ME (Autor) FEP

Ata de Conferência Internacional

Título: IFAC Proceedings Volumes (IFAC-PapersOnline)

Páginas: 337-342

8th IFAC Symposium on Biological and Medical Systems, BMS 2012

Budapest, 29 August 2012 through 31 August 2012

Indexação

ISI Web of Knowledge

Scopus - 0 Citações

Classificação Científica

CORDIS: Ciências Físicas > Matemática > Estatística

Outras Informações

ID Authenticus: P-008-CDD

DOI: 10.3182/20120829-3-hu-2029.00043

Abstract (EN): In the last decades propofol became established as an intravenous agent for the induction and maintenance of both sedation and general anesthesia procedures. In order to achieve the desired clinical effects appropriate infusion rate strategies must be designed. Moreover, it is important to avoid or minimize side effects which may be associated with adverse cardiorespiratory effects and delayed recovery. Nowadays, to attain these purposes the continuous propofol delivery is usually performed through target-controlled infusion (TCI) systems whose algorithms rely on pharmacokinetic and pharmacodynamic models (Schraag, 2001). This work presents statistical models to estimate both the infusion rate and the bolus administration. The modeling strategy relies on multivariate linear models for panel data (Wooldridge, 2002), based on patient characteristics such as age, height, weight and gender along with the desired target concentration. A clinical database collected with a RugLoopII device on 84 patients undergoing ultrasonographic endoscopy under sedation-analgesia with propofol and remifentanil, (Gambús et al., 2011), is used to estimate the models (training set with 74 cases) and assess their performance (test set with 10 cases). The results obtained in the test set comprising a broad range of characteristics are satisfactory since the models are able to predict bolus and infusion rates comparable to those of TCI. © 2012 IFAC.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6