Modeling the Effect of Propofol and Remifentanil Combinations for Sedation-Analgesia in Endoscopic Procedures Using an Adaptive Neuro Fuzzy Inference System (ANFIS)

BACKGROUND:The increasing demand for anesthetic procedures in the gastrointestinal endoscopy area has not been followed by a similar increase in the methods to provide and control sedation and analgesia for these patients. In this study, we evaluated different combinations of propofol and remifentanil, administered through a target-controlled infusion system, to estimate the optimal concentrations as well as the best way to control the sedative effects induced by the combinations of drugs in patients undergoing ultrasonographic endoscopy. METHODS:One hundred twenty patients undergoing ultrasonographic endoscopy were randomized to receive, by means of a target-controlled infusion system, a fixed effect-site concentration of either propofol or remifentanil of 8 different possible concentrations, allowing adjustment of the concentrations of the other drug. Predicted effect-site propofol (Cepro) and remifentanil (Ceremi) concentrations, parameters derived from auditory evoked potential, autoregressive auditory evoked potential index (AAI/2) and electroencephalogram (bispectral index [BIS] and index of consciousness [IoC]) signals, as well as categorical scores of sedation (Ramsay Sedation Scale [RSS] score) in the presence or absence of nociceptive stimulation, were collected, recorded, and analyzed using an Adaptive Neuro Fuzzy Inference System. The models described for the relationship between Cepro and Ceremi versus AAI/2, BIS, and IoC were diagnosed for inaccuracy using median absolute performance error (MDAPE) and median root mean squared error (MDRMSE), and for bias using median performance error (MDPE). The models were validated in a prospective group of 68 new patients receiving different combinations of propofol and remifentanil. The predictive ability (Pk) of AAI/2, BIS, and IoC with respect to the sedation level, RSS score, was also explored. RESULTS:Data from 110 patients were analyzed in the training group. The resulting estimated models had an MDAPE of 32.87, 12.89, and 8.77; an MDRMSE of 17.01, 12.81, and 9.40; and an MDPE of −1.86, 3.97, and 2.21 for AAI/2, BIS, and IoC, respectively, in the absence of stimulation and similar values under stimulation. Pk values were 0.82, 0.81, and 0.85 for AAI/2, BIS, and IoC, respectively. The model predicted the prospective validation data with an MDAPE of 34.81, 14.78, and 10.25; an MDRMSE of 16.81, 15.91, and 11.81; an MDPE of −8.37, 5.65, and −1.43; and Pk values of 0.81, 0.8, and 0.8 for AAI/2, BIS, and IoC, respectively. CONCLUSION:A model relating Cepro and Ceremi to AAI/2, BIS, and IoC has been developed and prospectively validated. Based on these models, the (Cepro, Ceremi) concentration pairs that provide an RSS score of 4 range from (1.8 &mgr;g·mL−1, 1.5 ng·mL−1) to (2.7 &mgr;g·mL−1, 0 ng·mL−1). These concentrations are associated with AAI/2 values of 25 to 30, BIS of 71 to 75, and IoC of 72 to 76. The presence of noxious stimulation increases the requirements of Cepro and Ceremi to achieve the same degree of sedative effects.

[1]  Hugo Vereecke,et al.  Ability of the Bispectral Index, Autoregressive Modelling with Exogenous Input-derived Auditory Evoked Potentials, and Predicted Propofol Concentrations to Measure Patient Responsiveness during Anesthesia with Propofol and Remifentanil , 2003, Anesthesiology.

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Dwayne R. Westenskow,et al.  Fuzzy logic for model adaptation of a pharmacokinetic-based closed loop delivery system for pancuronium , 1997, Artif. Intell. Medicine.

[4]  T. Kazama,et al.  Optimal propofol plasma concentration during upper gastrointestinal endoscopy in young, middle-aged, and elderly patients. , 2000, Anesthesiology.

[5]  J. Bruhn,et al.  Pharmacodynamic Interaction between Propofol and Remifentanil Regarding Hypnosis, Tolerance of Laryngoscopy, Bispectral Index, and Electroencephalographic Approximate Entropy , 2004, Anesthesiology.

[6]  A. Hoeft,et al.  Maximizing Prediction Probability PK as an Alternative Semiparametric Approach to Estimate the Plasma Effect-Site Equilibration Rate Constant ke0 , 2009, Anesthesia and analgesia.

[7]  S. H. Lee,et al.  Population pharmacokinetic and pharmacodynamic models of remifentanil in healthy volunteers using artificial neural network analysis. , 2007, British journal of clinical pharmacology.

[8]  S. Shafer,et al.  The Influence of Method of Administration and Covariates on the Pharmacokinetics of Propofol in Adult Volunteers , 1998, Anesthesiology.

[9]  S L Shafer,et al.  A comparison of spectral edge, delta power, and bispectral index as EEG measures of alfentanil, propofol, and midazolam drug effect , 1997, Clinical pharmacology and therapeutics.

[10]  Warren D. Smith,et al.  Measuring the Performance of Anesthetic Depth Indicators , 1996, Anesthesiology.

[11]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[12]  S. Henneberg,et al.  Autoregressive Modeling with Exogenous Input of Middle-Latency Auditory-Evoked Potentials to Measure Rapid Changes in Depth of Anesthesia , 1996, Methods of Information in Medicine.

[13]  Pere Caminal,et al.  Validation of the Index of Consciousness (IoC) during sedation/analgesia for ultrasonographic endoscopy , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Xu-Sheng Zhang,et al.  Derived fuzzy knowledge model for estimating the depth of anesthesia , 2001, IEEE Transactions on Biomedical Engineering.

[15]  Steven L. Shafer,et al.  Measuring the predictive performance of computer-controlled infusion pumps , 1992, Journal of Pharmacokinetics and Biopharmaceutics.

[16]  S L Shafer,et al.  Influence of Age and Gender on the Pharmacokinetics and Pharmacodynamics of Remifentanil: I. Model Development , 1997, Anesthesiology.

[17]  D. Chernik,et al.  Validity and Reliability of the Observer's: Assessment of Alertness/Sedation Scale Study with Intravenous Midazolam , 1990, Journal of clinical psychopharmacology.

[18]  S L Shafer,et al.  The influence of age on propofol pharmacodynamics. , 1999, Anesthesiology.

[19]  M Jospin,et al.  Validation of the index of consciousness during sevoflurane and remifentanil anaesthesia: a comparison with the bispectral index and the cerebral state index. , 2008, British journal of anaesthesia.

[20]  S. Shafer,et al.  Efficient Trial Design for Eliciting a Pharmacokinetic– Pharmacodynamic Model–based Response Surface Describing the Interaction between Two Intravenous Anesthetic Drugs , 2002, Anesthesiology.

[21]  Steven L. Shafer,et al.  Algorithms to rapidly achieve and maintain stable drug concentrations at the site of drug effect with a computer-controlled infusion pump , 1992, Journal of Pharmacokinetics and Biopharmaceutics.

[22]  M. Ramsay,et al.  Controlled Sedation with Alphaxalone-Alphadolone , 1974, British medical journal.

[23]  Erik Olofsen,et al.  Response Surface Modeling of Remifentanil–Propofol Interaction on Cardiorespiratory Control and Bispectral Index , 2003, Anesthesiology.