Implementation of a Bayesian based advisory tool for target-controlled infusion of propofol using qCON as control variable.

This single blinded randomized controlled trial aims to assess whether the application of a Bayesian-adjusted CePROP (effect-site of propofol) advisory tool leads towards a more stringent control of the cerebral drug effect during anaesthesia, using qCON as control variable. 100 patients scheduled for elective surgery were included and randomized into a control or intervention group (1:1 ratio). In the intervention group the advisory screen was made available to the clinician, whereas it was blinded in the control group. The settings of the target-controlled infusion pumps could be adjusted at any time by the clinician. Cerebral drug effect was quantified using processed EEG (CONOX monitor, Fresenius Kabi, Bad Homburg, Germany). The time of qCON between the desired range (35-55) during anaesthesia maintenance was defined as our primary end point. Induction parameters and recovery times were considered secondary end points and coefficient of variance of qCON and CePROP was calculated in order to survey the extent of control towards the mean of the population. The desired range of qCON between 35 and 55 was maintained in 84% vs. 90% (p = 0.15) of the case time in the control versus intervention group, respectively. Secondary endpoints showed similar results in both groups. The coefficient of variation for CePROP was higher in the intervention group. The application of the Bayesian-based CePROP advisory system in this trial did not result in a different time of qCON between 35 and 55 (84 [21] vs. 90 [18] percent of the case time). Significant differences between groups were hard to establish, most likely due to a very high performance level in the control group. More extensive control efforts were found in the intervention group. We believe that this advisory tool could be a useful educational tool for novices to titrate propofol effect-site concentrations.

General purpose models for intravenous anesthetics, the next generation for target-controlled infusion and total intravenous anesthesia?

PURPOSE OF REVIEW: There are various pharmacokinetic-dynamic models available, which describe the time course of drug concentration and effect and which can be incorporated into target-controlled infusion (TCI) systems. For anesthesia and sedation, most of these models are derived from narrow patient populations, which restricts applicability for the overall population, including (small) children, elderly, and obese patients. This forces clinicians to select specific models for specific populations. RECENT FINDINGS: Recently, general purpose models have been developed for propofol and remifentanil using data from multiple studies and broad, diverse patient groups. General-purpose models might reduce the risks associated with extrapolation, incorrect usage, and unfamiliarity with a specific TCI-model, as they offer less restrictive boundaries (i.e., the patient "doesn't fit in the selected model") compared with the earlier, simpler models. Extrapolation of a model can lead to delayed recovery or inadequate anesthesia. If multiple models for the same drug are implemented in the pump, it is possible to select the wrong model for that specific case; this can be overcome with one general purpose model implemented in the pump. SUMMARY: This article examines the usability of these general-purpose models in relation to the more traditional models.

Utility of the SmartPilot® View advisory screen to improve anaesthetic drug titration and postoperative outcomes in clinical practice: a two-centre prospective observational trial.

BACKGROUND: The advisory system SmartPilot® View (Drägerwerk AG, Lübeck, Germany) provides real-time, demographically adjusted pharmacodynamic information throughout anaesthesia, including time course of effect-site concentrations of administered drugs and a measure of potency of the combined drug effect termed the "'Noxious Stimulation Response Index' (NSRI). This dual-centre, prospective, observational study assesses whether the availability of SmartPilot® View alters the behaviour of anaesthetic drug titration of anaesthetists and improves the Anaesthesia Quality Score (AQS; percentage of time spent with MAP 60-80 mm Hg and Bispectral Index [BIS] 40-60 [blinded]). METHODS: We recruited 493 patients scheduled for elective surgery in two university centres. A control group (CONTROL; n=170) was enrolled to observe drug titration in current practice. Thereafter, an intervention group was enrolled, for which SmartPilot® View was made available to optimise drug titration (SPV; n=188). The AQS, haemodynamic and hypnotic effects, recovery times, pain scores, and other parameters were compared between groups. RESULTS: There were 358 patients eligible for analysis. Anaesthesia quality score was similar between CONTROL and SPV (median AQS [Q1-Q3]) 25.3% [7.4-41.5%] and 22.2% [8.0-44.4%], respectively; P=0.898). Compared with CONTROL, SPV patients had less severe hypotension and hypertension, less BIS <40, faster tracheal extubation, and lower early postoperative pain scores. CONCLUSIONS: Adding SmartPilot® View information did not affect average drug titration behaviour. However, small improvements in control of MAP and BIS and early recovery suggest improved titration for some patients without increasing the risk of overdosing or underdosing. CLINICAL TRIAL REGISTRATION: NCT01467167.

Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists.

This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.

Influence of an "Electroencephalogram-Based" Monitor Choice on the Delay Between the Predicted Propofol Effect-Site Concentration and the Measured Drug Effect.

BACKGROUND: Clinicians can optimize propofol titration by using 2 sources of pharmacodynamic (PD) information: the predicted effect-site concentration for propofol (Ceprop) and the electroencephalographically (EEG) measured drug effect. Relation between these sources should be time independent, that is, perfectly synchronized. In reality, various issues corrupt time independency, leading to asynchrony or, in other words, hysteresis. This asynchrony can lead to conflicting information, making effective drug dosing challenging. In this study, we tried to quantify and minimize the hysteresis between the Ceprop (calculated using the Schnider model for propofol) and EEG measured drug effect, using nonlinear mixed-effects modeling (NONMEM). Further, we measured the influence of EEG-based monitor choice, namely Bispectral index (BIS) versus qCON index (qCON) monitor, on propofol PD hysteresis. METHODS: We analyzed the PD data from 165 patients undergoing propofol-remifentanil anesthesia for outpatient surgery. Drugs were administered using target-controlled infusion (TCI) pumps. Pumps were programmed with Schnider model for propofol and Minto model for remifentanil. We constructed 2 PD models (direct models) relating the Schnider Ceprop to the measured BIS and qCON monitor values. We quantified the models' misspecification due to hysteresis, on an individual level, using the root mean squared errors (RMSEs). Subsequently, we optimized the PD models' predictions by adding a lag term to both models (lag-time PD models) and quantified the optimization using the RMSE. RESULTS: There is a counterclockwise hysteresis between Ceprop and BIS/qCON values. Not accounting for this hysteresis results in a direct PD model with an effect-site concentration which produces 50% of the maximal drug effect (Ce50) of 6.24 and 8.62 µg/mL and RMSE (median and interquartile range [IQR]) of 9.38 (7.92-11.23) and 8.41(7.04-10.2) for BIS and qCON, respectively. Adding a modeled lag factor of 49 seconds to the BIS model and 53 seconds to the qCON model improved both models' prediction, resulting in similar Ce50 (3.66 and 3.62 µg/mL for BIS and qCON) and lower RMSE (median (IQR) of 7.87 (6.49-9.90) and 6.56 (5.28-8.57) for BIS and qCON. CONCLUSIONS: There is a significant "Ceprop versus EEG measured drug effect" hysteresis. Not accounting for it leads to conflicting PD information and false high Ce50 for propofol in both monitors. Adding a lag term improved the PD model performance, improved the "pump-monitor" synchrony, and made the estimates of Ce50 for propofol more realistic and less monitor dependent.

Oxygen Reserve Index: Validation of a New Variable.

BACKGROUND: Pulse oximetry-derived oxygen saturation is typically >97% in normoxia and hyperoxia, limiting its clinical use. The new Oxygen Reserve Index (ORi), a relative indicator of the partial pressure of oxygen dissolved in arterial blood (PaO2) in the range of 100-200 mm Hg, may allow additional monitoring of oxygen status. METHODS: In this prospective validation intervention study, 20 healthy volunteers were breathing standardized oxygen concentrations ranging from mild hypoxia (fraction of inspired oxygen = 0.14) to hyperoxia (fraction of inspired oxygen = 1.0) via a tight-fitting face mask. ORi was measured noninvasively by multiwavelength pulse co-oximetry using 2 finger sensors. These ORi values (unitless scale, 0.00-1.00) were compared with measured PaO2 values. Repeated-measurements correlation analysis was performed to assess the ORi/PaO2 relationship. ORi trending ability was assessed using a 4-quadrant plot. The area under the receiver operating characteristics curve was calculated to assess the prediction of hypoxia (low-ranged PaO2, <100 mm Hg). RESULTS: Within the ORi-sensitive range, a strong positive correlation was found between ORi and PaO2 for both sensors (R = 0.78 and 0.83; P < .0001). ORi trending of PaO2 was good within this range (concordance rate = 94%). The prediction of PaO2 <100 mm Hg was also good, with an area under the receiver operating characteristics curve of 0.91 and 99% sensitivity and 82% specificity. CONCLUSIONS: In this prospective volunteer validation study, a strong and positive correlation between PaO2 and ORi was found, together with a good trending ability. Based on these data, the future use of ORi as a continuous noninvasive monitoring tool for assessing oxygenation status in patients receiving supplemental oxygen might be supported.

Propofol Breath Monitoring as a Potential Tool to Improve the Prediction of Intraoperative Plasma Concentrations.

INTRODUCTION: Monitoring of drug concentrations in breathing gas is routinely being used to individualize drug dosing for the inhalation anesthetics. For intravenous anesthetics however, no decisive evidence in favor of breath concentration monitoring has been presented up until now. At the same time, questions remain with respect to the performance of currently used plasma pharmacokinetic models implemented in target-controlled infusion systems. In this study, we investigate whether breath monitoring of propofol could improve the predictive performance of currently applied, target-controlled infusion models. METHODS: Based on data from a healthy volunteer study, we developed an addition to the current state-of-the-art pharmacokinetic model for propofol, to accommodate breath concentration measurements. The potential of using this pharmacokinetic (PK) model in a Bayesian forecasting setting was studied using a simulation study. Finally, by introducing bispectral index monitor (BIS) measurements and the accompanying BIS models into our PK model, we investigated the relationship between BIS and predicted breath concentrations. RESULTS AND DISCUSSION: We show that the current state-of-the-art pharmacokinetic model is easily extended to reliably describe propofol kinetics in exhaled breath. Furthermore, we show that the predictive performance of the a priori model is improved by Bayesian adaptation based on the measured breath concentrations, thereby allowing further treatment individualization and a more stringent control on the targeted plasma concentrations during general anesthesia. Finally, we demonstrated concordance between currently advocated BIS models, relying on predicted effect-site concentrations, and our new approach in which BIS measurements are derived from predicted breath concentrations.