Background: The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. Methods: Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo–steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the “prestimulation” and “poststimulation” BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). Results: The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50 SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. Conclusions: By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of “stimulation” on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index. (Anesthesiology 2014; 120:1390-9)

Background: The interaction of sevoflurane and opioids can be described by response surface modeling using the hierarchical model. We expanded this for combined administration of sevoflurane, opioids, and 66 vol.% nitrous oxide (N 2 O), using historical data on the motor and hemodynamic responsiveness to incision, the minimal alveolar concentration, and minimal alveolar concentration to block autonomic reflexes to nociceptive stimuli, respectively. Methods: Four potential actions of 66 vol.% N 2 O were postulated: (1) N 2 O is equivalent to A ng/ml of fentanyl (additive); (2) N 2 O reduces C 50 of fentanyl by factor B; (3) N 2 O is equivalent to X vol.% of sevoflurane (additive); (4) N 2 O reduces C 50 of sevoflurane by factor Y. These four actions, and all combinations, were fitted on the data using NONMEM (version VI, Icon Development Solutions, Ellicott City, MD), assuming identical interaction parameters (A, B, X, Y) for movement and sympathetic responses. Results: Sixty-six volume percentage nitrous oxide evokes an additive effect corresponding to 0.27 ng/ml fentanyl (A) with an additive effect corresponding to 0.54 vol.% sevoflurane (X). Parameters B and Y did not improve the fit. Conclusion: The effect of nitrous oxide can be incorporated into the hierarchical interaction model with a simple extension. The model can be used to predict the probability of movement and sympathetic responses during sevoflurane anesthesia taking into account interactions with opioids and 66 vol.% N 2 O.

Background Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models. Methods Forty patients preoperatively received different combined concentrations of remifentanil (0-12 ng/ml) and sevoflurane (0.5-3.5 vol.%) according to a criss-cross design (160 concentration pairs, four per patient). After having reached pseudosteady state, the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy was recorded. For the analysis of the probability of tolerance, five different interaction models were tested: Greco, Reduced Greco, Minto, Scaled C50(O) Hierarchical, and Fixed C50(O) Hierarchical model. All calculations were performed with NONMEM VI (Icon Development Solutions, Ellicott City, MD). Results The pharmacodynamic interaction between sevoflurane and remifentanil was strongly synergistic for both the hypnotic and the analgesic components of anesthesia. The Greco model did not result in plausible parameter estimates. The Fixed C50(O) Hierarchical model performed slightly better than the Scaled C50(O) Hierarchical and Reduced Greco models, whereas the Minto model fitted less well. Conclusion We showed the importance of exploring various surface model approaches when studying drug interactions. The Fixed C50(O) Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Background The noxious stimulation response index (NSRI) is a novel anesthetic depth index ranging between 100 and 0, computed from hypnotic and opioid effect-site concentrations using a hierarchical interaction model. The authors validated the NSRI on previously published data. Methods The data encompassed 44 women, American Society of Anesthesiology class I, randomly allocated to three groups receiving remifentanil infusions targeting 0, 2, and 4 ng/ml. Propofol was given at stepwise increasing effect-site target concentrations. At each concentration, the observer assessment of alertness and sedation score, the response to eyelash and tetanic stimulation of the forearm, the bispectral index (BIS), and the acoustic evoked potential index (AAI) were recorded. The authors computed the NSRI for each stimulation and calculated the prediction probabilities (PKs) using a bootstrap technique. The PKs of the different predictors were compared with multiple pairwise comparisons with Bonferroni correction. Results The median (95% CI) PK of the NSRI, BIS, and AAI for loss of response to tetanic stimulation was 0.87 (0.75-0.96), 0.73 (0.58-0.85), and 0.70 (0.54-0.84), respectively. The PK of effect-site propofol concentration, BIS, and AAI for observer assessment of alertness and sedation score and loss of eyelash reflex were between 0.86 (0.80-0.92) and 0.92 (0.83-0.99), whereas the PKs of NSRI were 0.77 (0.68-0.85) and 0.82 (0.68-0.92). The PK of the NSRI for BIS and AAI was 0.66 (0.58-0.73) and 0.63 (0.55-0.70), respectively. Conclusion The NSRI conveys information that better predicts the analgesic component of anesthesia than AAI, BIS, or predicted propofol or remifentanil concentrations. Prospective validation studies in the clinical setting are needed.

Background In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. Methods The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). Results Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. Conclusion The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Background The authors developed and applied a method to optimize the combination of bupivacaine, fentanyl, and clonidine for continuous postoperative lumbar epidural analgesia. Methods One hundred eighteen patients undergoing knee or hip surgery participated in the study. Postoperative epidural analgesia during 48 h after surgery was optimized under restrictions dictated by side effects. Initially, eight combinations of bupivacaine, fentanyl, and clonidine (expressed as drug concentration in the solution administered) were empirically chosen and investigated. To determine subsequent combinations, an optimization model was applied until three consecutive steps showed no decrease in pain score. For the first time in a clinical investigation, a regression model was applied when the optimization procedure led to combinations associated with unacceptable side effects. Results The authors analyzed 12 combinations with an allowed bupivacaine concentration range of 0-2.5 mg/ml, a fentanyl concentration range of 0-5 microg/ml, and a clonidine concentration range of 0-5 microg/ml. The best combinations of bupivacaine, fentanyl, and clonidine concentrations were 1.0 mg/ml-1.4 microg/ml-0.5 microg/ml, 0.9 mg/ml-3.0 microg/ml-0.3 microg/ml, 0.6 mg/ml-2.5 microg/ml-0.8 microg/ml, and 1.0 mg/ml-2.4 microg/ml-1.0 microg/ml, respectively, all producing a similarly low pain score. The incidence of side effects was low. The application of the regression model to combinations associated with high incidence of motor block successfully directed the optimization procedure to combinations within the therapeutic range. Conclusions The results support further study of the combinations of bupivacaine, fentanyl, and clonidine mentioned above for postoperative analgesia after knee and hip surgery. This novel optimization method may be useful in clinical research.

Background According to previous studies, the addition of ketamine to morphine for intravenous patient-controlled analgesia (PCA) may be beneficial. The authors developed and applied a new model to optimize the combination of morphine, ketamine, and a lockout interval for PCA after lumbar spine and hip surgery. Methods One-hundred two patients undergoing lumbar spine or hip surgery participated in the study. The analgesic effect of PCA during 48 h after surgery was optimized under restrictions dictated by side effects. Initially, eight combinations of morphine, ketamine (expressed as drug concentration in the solution administered), and a lockout interval (i.e., minimal allowed time between two consecutive PCA boluses) were empirically chosen and investigated. To determine subsequent combinations, an optimization model was applied until three consecutive steps showed no decrease in pain score. Results The authors analyzed 12 combinations with an allowed morphine and ketamine range in a PCA solution of 0-2 mg/ml and a lockout interval range of 5-12 min. During the optimization procedure, a reduction in mean pain scores with a low incidence of side effects was observed. The procedure converged to a morphine-to-ketamine ratio of 1:1 and a lockout interval of 8 min. Conclusions Using a novel method to analyze drug combinations, the study supports combinations of morphine with ketamine in a ratio of 1:1 and a lockout interval of 8 min for postoperative PCA following spine and hip surgery.

Background Several experimental pain models have been used to measure opioid effects in humans. The aim of the current study was to compare the qualities of five frequently used experimental pain tests to measure opioid effects. Methods The increase of electrical, heat, and pressure pain tolerance and the decrease of ice-water and ischemic pain perception was determined at baseline and at four different plasma concentrations of alfentanil (n = 7) administered as target controlled infusion or placebo (n = 7). A linear mixed-effects modeling (NONMEM) was performed to detect drug, placebo, and time effect as well as interindividual and intraindividual variation of effect. Results Only the electrical, ice-water, and pressure pain tests are sensitive to assess a concentration-response curve of alfentanil. At a plasma alfentanil concentration of 100 ng/ml, the increase in pain tolerance compared with baseline was 42.0% for electrical pain, 22.2% for pressure pain, and 21.7% for ice-water pain. The slope of the linear concentration-response curve had an interindividual coefficient of variation of 58.3% in electrical pain, 35.6% in pressure pain, and 60.0% in ice-water pain. The residual error including intraindividual variation at an alfentanil concentration of 100 ng/ml was 19.4% for electrical pain, 6.1% for pressure pain, and 13.0% for ice-water pain. Electrical pain was affected by a significant placebo effect, and pressure pain was affected by a significant time effect. Conclusion Electrical, pressure, and ice-water pain, but not ischemic and heat pain, provide significant concentration-response curves in the clinically relevant range of 200 ng/ml alfentanil or lower. The power to detect a clinically relevant shift of the curve is similar in the three tests. The appropriate test(s) for pharmacodynamic studies should be chosen according to the investigated drug(s) and the study design.