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Background: With the growing use of pharmacokinetic (PK)-driven drug delivery and/or drug advisory displays, identifying the PK model that best characterizes propofol plasma concentration (Cp) across a variety of dosing conditions would be useful. We tested the accuracy of 3 compartmental models and 1 physiologically based recirculatory PK model for propofol to predict the time course of propofol Cp using concentration-time data originated from studies that used different infusion schemes., Methods: Three compartmental PK models for propofol, called the "Marsh," the "Schnider," and the "Schüttler" models, and 1 physiologically based recirculatory model called the "Upton" model, were used to simulate the time course of propofol Cp. To test the accuracy of the models, we used published measured plasma concentration data that originated from studies of manual (bolus and short infusion) and computer-controlled (target-controlled infusion [TCI] and long infusion) propofol dosing schemes. Measured/predicted (M/P) propofol Cp plots were constructed for each dataset. Bias and inaccuracy of each model were assessed by median prediction error (MDPE) and median absolute prediction error (MDAPE), respectively., Results: The M/P propofol Cp in the 4 PK models revealed bias in all 3 compartmental models during the bolus and short infusion regimens. In the long infusion, a worse M/P propofol Cp at higher concentration was seen for the Marsh and the Schüttler models than for the 2 other models. Less biased M/P propofol Cp was found for all models during TCI. In the bolus group, after 1 min, a clear overprediction was seen for all 3 compartmental models for the entire 5 min; however, this initial error resolved after 4 min in the Schnider model. The Upton model did not predict propofol Cp accurately (major overprediction) during the first minute. During the bolus and short infusion, the Marsh model demonstrated worse MDPE and MDAPE compared with the 3 other models. During short infusion, MDAPE for the Schnider and Schüttler models was better than the Upton and the Marsh models. All models showed similar MDPE and MDAPE during TCI simulations. During long infusion, the Marsh and the Schüttler models underestimated the higher plasma concentrations., Conclusion: When combining the performance during various infusion regimens, it seems that the Schnider model, although still not perfect, is the recommended model to be used for TCI and advisory displays.

Background: The combination of propofol-remifentanil for procedural deep sedation in spontaneously breathing patients is characterized by the frequent incidence of side effects, especially respiratory depression. These side effects may be due to either the drug combination or the drug delivery technique. Target-controlled infusion (TCI) might optimize drug delivery. In this prospective, randomized, double-blind study in patients undergoing elective colonoscopy, we thus tried to answer two questions: first, if adding remifentanil to propofol surpasses the disadvantages of the combination of these two products, and second, if administration of remifentanil via TCI decreases the incidence of side effects, compared to manually controlled administration., Methods: Patients undergoing elective colonoscopy were randomly assigned to receive remifentanil via manually controlled continuous infusion (MCI) (0.125 microg x kg(-1) x min(-1) for 2 min followed by a continuous infusion of 0.05 microg x kg(-1) x min(-1)), TCI remifentanil (1 ng/mL), or placebo (normal saline either as TCI or manual infusion of equivalent rate). All patients received TCI propofol, adjusted to a target concentration level that provided deep sedation in which patients were not responsive to verbal commands, but maintained spontaneous ventilation without assistance., Results: Significantly more patients in the placebo group showed movement, cough and hiccup, which transiently interfered with the examination. There were no clinically significant differences in hemodynamic or recovery variables among all groups. Remifentanil administered via TCI resulted in a decrease in propofol requirements. The incidence of hypopnea and apnea was less frequent when remifentanil was administered via TCI compared to MCI (TCI n = 7, MCI n = 16, P < 0.05)., Conclusion: The combination of remifentanil and propofol for deep sedation in spontaneously breathing patients, offered better conditions for colonoscopy than propofol used as a single drug. Remifentanil administered via TCI resulted in a decrease in propofol dosing and in a lower incidence in apnea and respiratory depression (TCI n = 7, MCI n = 16, P < 0.05), compared to manually controlled administration of remifentanil.

Background: Closed-loop control of the hypnotic component of anesthesia has been proposed in an attempt to optimize drug delivery. Here, we introduce a newly developed Bayesian-based, patient-individualized, model-based, adaptive control method for bispectral index (BIS) guided propofol infusion into clinical practice and compare its accuracy and clinical feasibility under direct observation of an anesthesiologist versus BIS guided, effect compartment controlled propofol administration titrated by the anesthesiologist during ambulatory gynecological procedures., Methods: Forty ASA patients were randomly allocated to the closed-loop or manual control group. All patients received midazolam 1 mg IV and alfentanil 0.5 mg IV before induction. In the closed-loop control group, propofol was administered using the previously described closed-loop control system to reach and maintain a target BIS of 50. In the manual control group, the propofol effect-site concentration was adapted at the discretion of the anesthesiologist to reach and maintain a BIS as close as possible to 50. Induction characteristics, performance, and robustness during maintenance and recovery times were compared. Hemodynamic and respiratory stability were calculated as clinical feasibility parameters., Results: The closed-loop control system titrated propofol administration accurately resulting in BIS values close to the set point. The closed-loop control system was able to induce the patients within clinically accepted time limits and with less overshoot than the manual control group. Automated control resulted in beneficial recovery times. Our closed-loop control group showed similar acceptable clinical performance specified by similar hemodynamic, respiratory stability, comparable movement rates, and quality scores than the manual control group., Conclusions: The Bayesian-based closed-loop control system for propofol administration using the BIS as a controlled variable performed accurate during anesthesia for ambulatory gynecological procedures. This control system is clinical feasibility and can be further validated in clinical practice.

Background: Implementing Bayesian methods in a model-based closed-loop system requires the integration of a standard response model with a patient-specific response model. This process makes use of specific modeling weights, called Bayesian variances, which determine how the specific model can deviate from the standard model. In this study we applied simulations to select the Bayesian variances yielding the optimal controller for a Bayesian-based closed-loop system for propofol administration using the Bispectral Index (BIS) as a controlled variable., Methods: The relevant Bayesian variances determining the modeling process were identified. Each set of such Bayesian variances represents a potential controller. The set, which will result in optimal control, was estimated using calculations on a simulated population. We selected 625 candidate sets. Similar to our previous closed-loop performance study, we applied a simulation protocol to evaluate controller performance. Our population consisted of 416 virtual patients, generated using population characteristics from previous work. A BIS offset trajectory similar to a surgical case was used., Results: We were able to develop, describe, and optimize the parameter setting for a patient-individualized model-based closed-loop controller using Bayesian optimization. Selection of the optimal set yields a controller performing with the following median absolute prediction errors at BIS targets 30, 50, and 70: 12.9 +/- 2.87, 7.59 +/- 0.74, and 5.76 +/- 1.03 respectively., Conclusions: We believe this system can be introduced safely into clinical testing for both induction and maintenance of anesthesia under direct observation of an anesthesiologist.

Background: The authors hypothesized a difference in plasma-effect site equilibration, depicted by a first-order constant k(e0), depending on the injection rate of propofol., Methods: Sixty-one patients received 2.5 mg/kg propofol given as a bolus or as a 1-, 2-, or 3-min infusion. The Bispectral Index was used to monitor drug effect. Propofol predicted plasma concentration was calculated using a three-compartment model and the effect site concentration over time as the convolution between the predicted plasma concentration and the disposition function of the effect site concentration. The authors evaluated the influence of the infusion rate on the k(e0) by comparing the model with one k(e0) for all groups with models estimating different k(e0) values for each group. The authors also assessed the accuracy of two pharmacokinetic models after bolus injection., Results: The best model based was a fixed (Bispectral Index > or = 90) plus sigmoidal model (Bispectral Index < 90) with two values of k(e0), one for the bolus (t(1/2) k(e0) = 1.2 min) and one for the infusions (t(1/2) k(e0) = 2.2 min). However, the tested pharmacokinetic models poorly predicted the arterial concentrations in the first minutes after bolus injection. Simulations showed the requirement for two k(e0) values for bolus and infusion was mostly a compensation for the inaccurate prediction of arterial concentrations after a bolus., Conclusion: Propofol plasma-effect site equilibration occurs more rapidly after a bolus than after rapid infusion, based on the electroencephalogram as a drug effect measure, mostly because of misspecification of the pharmacokinetic model in the first minutes after bolus.

Data on the effects of isovolemic hemodilution (IH) on oxygenation during one-lung ventilation (OLV) are lacking. We studied 47 patients with hemoglobin >14 g/dL who were scheduled for lung surgery (17 with normal lung function [group NL], 17 with chronic obstructive pulmonary disease [COPD] [group COPD], and 13 with COPD as control for time/anesthesia effects [group CTRL]). Anesthesia was standardized. The tracheas were intubated with a double-lumen tube. Ventilatory settings and fraction of inspired oxygen remained constant. The study was performed with patients in the supine position before surgery. OLV was initiated for 15 min. Two-lung ventilation was reinstituted, and IH was performed (500 mL); an identical volume of hydroxyethyl starch was administered. Subsequently, OLV was again performed for 15 min. In group CTRL, the same sequences of OLV were performed without IH. At the end of each period of OLV, pulmonary mechanics and blood gases were recorded. Data were analyzed by analysis of variance (mean +/- sd). In group NL and group CTRL, the arterial oxygen partial pressure remained constant, whereas it decreased in group COPD from 119 +/- 21 mm Hg before IH to 86 +/- 16 mm Hg after IH (P <0.01). Mild IH impairs gas exchange during OLV in COPD patients, but not in patients with normal lung function.

Unlabelled: There is concern about the toxicity of Compound (Co) A. Absorbents differ in the production of Co A during minimal-flow sevoflurane anesthesia. Strong alkali-free Amsorb does not produce Co A. It was our aim to study Superia, another new NaOH- and KOH-free CO(2) absorbent, in minimal-flow anesthesia, compared with KOH-free Sofnolime. After Ethics Committee approval, 14 consenting adult patients were included randomly by using Superia or Sofnolime as the CO(2) absorbent in the compact 750-mL canister of an ADU ventilator. After propofol and remifentanil administration, sevoflurane was given in oxygen and air (500 mL/min; fraction of inspired oxygen, 0.4), aiming at an end-tidal concentration of 2.3%-2.5%; ventilation aimed for 33-35 mm Hg PETCO(2). Compound A inspired (Co A(insp)) and expired (Co A(exp)) samples were taken for analysis, and canister temperatures were measured for 150 min. Statistical analysis was performed with the Friedman test or the Mann-Whitney U-test where appropriate. Correction for multiple testing was used. In the Superia group, no significant amount of Co A was formed, whereas in the Sofnolime group, maximum median (range) inspiratory values of 25 ppm (16 ppm) were found. The intergroup difference was P < 0.05. No difference was noticed between the two groups for the canister CO(2) absorbent temperature., Implications: During minimal-flow 2.3%-2.5% end-tidal sevoflurane, no compound A (Co A) is formed with the NaOH- and KOH-free CO(2) absorbent Superia. Although Co A values with KOH-free Sofnolime are still within reported safe limits, Superia is definitely an alternative for safe clinical practice.