An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations.
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2010-08
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INTRODUCTION: We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non-steady-state conditions, accounting for the lag time between sevoflurane effect-site concentration (Ce) and end-tidal (ET) concentration would improve predictions. METHODS: Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET-based and a Ce-based prediction. Similarly, 2 predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared with observations with graphical and temporal analyses. RESULTS: While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (> or = 99%). However, after termination of the anesthetic, models exhibited a wide range of predictions at emergence (1%-97%). Although wide, the Ce-based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET-based predictions. For the ET-based model, 45% of the patients awoke within 2 min of the 50% model predicted probability of unresponsiveness and 65% awoke within 4 min. For the Ce-based model, 45% of the patients awoke within 1 min of the 50% model predicted probability of unresponsiveness and 85% awoke within 3.2 min. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce- and ET-based models. DISCUSSION: Results confirmed, in part, our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large-scale evaluations with numerous patients are needed to better characterize model performance.
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Johnson, Ken B, Noah D Syroid, Dhanesh K Gupta, Sandeep C Manyam, Nathan L Pace, Cris D LaPierre, Talmage D Egan, Julia L White, et al. (2010). An evaluation of remifentanil-sevoflurane response surface models in patients emerging from anesthesia: model improvement using effect-site sevoflurane concentrations. Anesth Analg, 111(2). pp. 387–394. 10.1213/ANE.0b013e3181afe31c Retrieved from https://hdl.handle.net/10161/10242.
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Dhanesh Kumar Gupta
The overall theme of my research is the application of clinical pharmacology tools to the individualization of the care of high-risk surgical patients, especially those undergoing neurosurgical procedures. Current research focuses on creating pharmacokinetic-pharmacodynamic models to allow simulation of dose-concentration-effect relationships that will result in reduced toxicity while maximizing efficacy of intravenous opioids and hypnotics. The perioperative period is a time when patients are exposed to a multitude of drugs from a different classes, some of which may attenuate while others may augment the deleterious cascade of events that starts in the operating room and result in worse neuro-oncologic, neurovascular, or pain outcomes, even after the perioperative medication has been discontinued. Analytical techniques for perioperative “big data” have not been combined with the clinical pharmacology toolbox to create dose-response models that can help optimize perioperative care. Through collaboration with pharmacometricians and informaticians, care paths can be developed in an iterative fashion to expose the innards of the perioperative black box.
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