Model-based drug administration: current status of target-controlled infusion and closed-loop control

Purpose of review : Drug administration might be optimized by incorporating pharmacokinetic-dynamic (PK/PD) principles and control engineering theories. This review gives an update of the actual status of target-controlled infusion (TCI) and closed-loop computer-controlled drug administration and the ongoing research in the field. Recent findings : TCI is becoming mature technology clinically used in many countries nowadays with proven safety. Nevertheless, changing populations might require adapting the established PK/PD models. As TCI requires accurate PK/PD models, new models have been developed which should now be incorporated into the pumps to allow more general use of this technology. Closed-loop administration of hypnotic drugs using an electro-encephalographic-derived-controlled variable has been well studied and has been shown to outperform manual administration. Computer administration for other drugs and fluids have been studied recently. Feasibility has been shown for systems controlling multiple components of anaesthesia, but more work is required to show clinical safety and efficiency. Summary : Evidence in the literature is increasing that TCI and closed-loop technology could assist the anaesthetists to optimize drug administration during anaesthesia

Les usages du dégagisme dans la campagne présidentielle du mouvement La France insoumise

Si on s'appuie sur une définition du populisme comme étant un discours simplifiant à l'extrême les enjeux politiques en se référant à une mythologie nationale (Hermet, 1997: 46), alors on serait a priori tentés de classer le simple positionnement anti-système comme une manifestation de cette pulsion simplificatrice (Wiles, 1969). La tabula rasa des usages du passé est une démarche classique en France pour prétendre refonder une pratique politique. Le mouvement La France insoumise a été créé par le leader de gauche Jean-Luc Mélenchon le 10 février 2016 autour d´un programme socialiste et écologiste, l'Avenir en commun. L'objectif était à la fois d'imposer une vision idéologique des rapports sociaux et de préparer les échéances électorales à venir. Au cours de la campagne des élections présidentielles, Jean-Luc Mélenchon est revenu à plusieurs reprises sur l'idée selon laquelle il fallait éliminer par le vote une bonne partie des représentants en place parce qu´ils ont trahi le sens du mandat qui leur avait été confié par le peuple. Les journalistes ont alors invoqué la notion de dégagisme pour théoriser succinctement le vote de rejet des élites en place

Performance of the Eleveld pharmacokinetic model to titrate propofol in an obese Japanese patient population

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Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers

Background: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used. Methods: In total, 102 subjects receiving propofol (N =36 ; 16 male/20 female), sevoflurane (N =36 ; 16 male/20 female), or dexmedetomidine (N = 30 ; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model. Results: The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states. Conclusions: The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOANS independent of the drug used

Tracking electroencephalographic changes using distributions of linear models : application to propofol-vased depth of anesthesia monitoring

Objective: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. Methods: Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average-ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi fractal dimension. Results: The highest average testing sensitivity of 59% (chance sensitivity: 17%) was found for ARMA (2, 1) models and Higuchi fractal dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%). Conclusion: The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared with a distribution approach based on a high performing anesthesia monitoring measure. Significance: These techniques hold potential for anesthesia monitoring and may be generally applicable for tracking brain states

Differential effects of phenylephrine and norepinephrine on peripheral tissue oxygenation during general anaesthesia: a randomised controlled trial

BACKGROUND: Phenylephrine and norepinephrine are two vasopressors commonly used to counteract anaesthesia-induced hypotension. Their dissimilar working mechanisms may differentially affect the macro and microcirculation, and ultimately tissue oxygenation. OBJECTIVES: We investigated the differential effect of phenylephrine and norepinephrine on the heart rate (HR), stroke volume (SV), cardiac index (CI), cerebral tissue oxygenation (SctO(2)) and peripheral tissue oxygenation (SptO(2)), and rate-pressure product (RPP). DESIGN: A randomised controlled study. SETTING: Single-centre, University Medical Center Groningen, The Netherlands. PATIENTS: Sixty normovolaemic patients under balanced propofol/remifentanil anaesthesia. INTERVENTIONS: If the mean arterial pressure (MAP) dropped below 80% of the awake state value, phenylephrine (100 mu g + 0.5 mu g kg(-1) min(-1)) or norepinephrine (10 mu g + 0.05 mu g kg(-1) min(-1)) was administered in a randomised fashion. MAIN OUTCOME MEASURES: MAP, HR, SV, CI, SctO(2), SptO(2) and rate-pressure product (RPP) analysed from 30 s before drug administration until 240 s thereafter. RESULTS: Phenylephrine and norepinephrine caused an equivalent increase in MAP [Delta = 13 (8 to 22) and Delta = 13 (9 to 19) mmHg, respectively] and SV [Delta = 6 +/- 6 and Delta = 5 +/- 7 ml, respectively], combined with a significant equivalent decrease in HR (both Delta = -8 +/- 6 bpm), CI (both Delta = -0.2 +/- 0.3 l min(-1) m(-2)) and SctO(2) and an unchanged RPP (Delta = 345 +/- 876 and Delta = 537 +/- 1076 mmHg min(-1)). However, SptO(2) was slightly but statistically significantly (P < 0.05) decreased after norepinephrine [Delta = -3 (-6 to 0)%] but not after phenylephrine administration [Delta = 0 (-1 to 1)%]. In both groups, SptO(2) after vasopressor was still higher than the awake value. CONCLUSION: In normovolaemic patients under balanced propofol/remifentanil anaesthesia, phenylephrine and norepinephrine produced similar clinical effects when used to counteract anaesthesia-induced hypotension. After norepinephrine, a fall in peripheral tissue oxygenation was statistically significant, but its magnitude was not clinically relevant

Human factors in information technology

BACKGROUND: Current electroencephalogram (EEG)-derived measures provide information on cortical activity and hypnosis but are less accurate regarding subcortical activity, which is expected to vary with the degree of antinociception. Recently, the neurophysiologically based EEG measures of cortical input (CI) and cortical state (CS) have been shown to be prospective indicators of analgesia/antinociception and hypnosis, respectively. In this study, we compared CI and an alternate measure of CS, the composite cortical state (CCS), with the Bispectral Index (BIS) and another recently developed measure of antinociception, the composite variability index (CVI). CVI is an EEG-derived measure based on a weighted combination of BIS and estimated electromyographic activity. By assessing the relationship between these indices for equivalent levels of hypnosis (as quantified using the BIS) and the nociceptive-antinociceptive balance (as determined by the predicted effect-site concentration of remifentanil), we sought to evaluate whether combining hypnotic and analgesic measures could better predict movement in response to a noxious stimulus than when used alone. METHODS: Time series of BIS and CVI indices and the raw EEG from a previously published study were reanalyzed. In our current study, the data from 80 patients, each randomly allocated to a target hypnotic level (BIS 50 or BIS 70) and a target remifentanil level (Remi-0, -2, -4 or -6 ng/mL), were included in the analysis. CCS, CI, BIS, and CVI were calculated or quantified at baseline and at a number of intervals after the application of the Observer's Assessment of Alertness/Sedation scale and a subsequent tetanic stimulus. The dependency of the putative measures of antinociception CI and CVI on effect-site concentration of remifentanil was then quantified, together with their relationship to the hypnotic measures CCS and BIS. Finally, statistical clustering methods were used to evaluate the extent to which simple combinations of antinociceptive and hypnotic measures could better detect and predict response to stimulation. RESULTS: Before stimulation, both CI and CVI differentiated patients who received remifentanil from those who were randomly allocated to the Remi-0 group (CI: Cohen's d = 0.65, 95% confidence interval, 0.48-0.83; CVI: Cohen's d = 0.72, 95% confidence interval, 0.56-0.88). Strong correlations between BIS and CCS were found (at different periods: 0.55 < R-2 < 0.68, P < 0.001). Application of the Observer's Assessment of Alertness/Sedation stimulus was associated with changes in CI and CCS, whereas, subsequent to the application of both stimuli, changes in all measures were seen. Pairwise combinations of CI and CCS showed higher sensitivity in detecting response to stimulation than CVI and BIS combined (sensitivity [99% confidence interval], 75.8% [52.7%-98.8%] vs 42% [15.4%-68.5%], P = 0.006), with specificity for CI and CCS approaching significance (52% [34.7%-69.3%] vs 24% [9.1%-38.9%], P = 0.0159). CONCLUSIONS: Combining electroencephalographically derived hypnotic and analgesic quantifiers may enable better prediction of patients who are likely to respond to tetanic stimulation

The history of target-controlled infusion

Target-controlled infusion (TCI) is a technique of infusing IV drugs to achieve a user-defined predicted (target) drug concentration in a specific body compartment or tissue of interest. In this review, we describe the pharmacokinetic principles of TCI, the development of TCI systems, and technical and regulatory issues addressed in prototype development. We also describe the launch of the current clinically available systems

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioral responsiveness was monitored and frontal electroencephalographic signals were recorded. The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modeling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Ritmodel, along with considerations of extensions of the Jansen-Ritmodel, suggests that the Jansen-Ritmodel combined with unscented Kalman filtering provides a valuable reference point for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information. Crown Copyright (C) 2016 Published by Elsevier Inc. All rights reserved