Pharmacokinetic parameter sets of alfentanil revisited: optimal parameters for use in target controlled infusion and anaesthesia display systems

Background In open TCI and anaesthesia display systems, the choice of pharmacokinetic (PK) parameter sets of opioids is clinically relevant. Accuracy and bias of the PK models may be affected by administration mode and the co-administered hypnotic drug. We retrospectively evaluated the performance of eight PK parameter sets for alfentanil in two data sets (infusion and bolus application). Methods With the dosing history from two studies in orthopaedic patients anaesthetized with propofol or inhalation anaesthetics the alfentanil plasma concentration over time was calculated with eight PK parameter sets. Median absolute performance error (MDAPE), log accuracy, median performance error (MDPE), log bias, Wobble, and Divergence were computed. Mann-Whitney rank test with Bonferroni correction was used for comparison between bolus and infusion data, repeated measures analysis of variance on ranks was used for comparison among parameter sets. Results The parameters by Scott (original and weight adjusted) and Fragen had a MDAPE ≤30% and a median log accuracy <0.15 independent of the administration mode, while MDPE was within ±20% and log bias nearly within ±0.1, respectively. The sets by Maitre and Lemmens were within these limits only in the bolus data. All other parameter sets were outside these limits. Conclusions In healthy orthopaedic patients, the PK parameters by Scott and by Maitre were equally valid when alfentanil was given as repeated boluses. When given as infusion, the Maitre parameters were less accurate and subject to a significant bias. We cannot exclude that the difference between bolus and infusion is partially because of the different hypnotics use

Clinical pharmacokinetics and pharmacodynamics of propofol

Propofol is an intravenous hypnotic drug that is used for induction and maintenance of sedation and general anaesthesia. It exerts its effects through potentiation of the inhibitory neurotransmitter -aminobutyric acid (GABA) at the GABA(A) receptor, and has gained widespread use due to its favourable drug effect profile. The main adverse effects are disturbances in cardiopulmonary physiology. Due to its narrow therapeutic margin, propofol should only be administered by practitioners trained and experienced in providing general anaesthesia. Many pharmacokinetic (PK) and pharmacodynamic (PD) models for propofol exist. Some are used to inform drug dosing guidelines, and some are also implemented in so-called target-controlled infusion devices, to calculate the infusion rates required for user-defined target plasma or effect-site concentrations. Most of the models were designed for use in a specific and well-defined patient category. However, models applicable in a more general population have recently been developed and published. The most recent example is the general purpose propofol model developed by Eleveld and colleagues. Retrospective predictive performance evaluations show that this model performs as well as, or even better than, PK models developed for specific populations, such as adults, children or the obese; however, prospective evaluation of the model is still required. Propofol undergoes extensive PK and PD interactions with both other hypnotic drugs and opioids. PD interactions are the most clinically significant, and, with other hypnotics, tend to be additive, whereas interactions with opioids tend to be highly synergistic. Response surface modelling provides a tool to gain understanding and explore these complex interactions. Visual displays illustrating the effect of these interactions in real time can aid clinicians in optimal drug dosing while minimizing adverse effects. In this review, we provide an overview of the PK and PD of propofol in order to refresh readers' knowledge of its clinical applications, while discussing the main avenues of research where significant recent advances have been made

Etomidate and its Analogs:A Review of Pharmacokinetics and Pharmacodynamics

Etomidate is a hypnotic agent that is used for the induction of anesthesia. It produces its effect by acting as a positive allosteric modulator on the gamma-aminobutyric acid type A receptor and thus enhancing the effect of the inhibitory neurotransmitter gamma-aminobutyric acid. Etomidate stands out among other anesthetic agents by having a remarkably stable cardiorespiratory profile, producing no cardiovascular or respiratory depression. However, etomidate suppresses the adrenocortical axis by the inhibition of the enzyme 11 beta-hydroxylase. This makes the drug unsuitable for administration by a prolonged infusion. It also makes the drug unsuitable for administration to critically ill patients. Etomidate has relatively large volumes of distributions and is rapidly metabolized by hepatic esterases into an inactive carboxylic acid through hydrolyzation. Because of the decrease in popularity of etomidate, few modern extensive pharmacokinetic or pharmacodynamic studies exist. Over the last decade, several analogs of etomidate have been developed, with the aim of retaining its stable cardiorespiratory profile, whilst eliminating its suppressive effect on the adrenocortical axis. One of these molecules, ABP-700, was studied in extensive phase I clinical trials. These found that ABP-700 is characterized by small volumes of distribution and rapid clearance. ABP-700 is metabolized similarly to etomidate, by hydrolyzation into an inactive carboxylic acid. Furthermore, ABP-700 showed a rapid onset and offset of clinical effect. One side effect observed with both etomidate and ABP-700 is the occurrence of involuntary muscle movements. The origin of these movements is unclear and warrants further research

Acute and life-threatening remifentanil overdose resulting from the misuse of a syringe pump.

In the perioperative setting, syringe pumps are frequently used. They guarantee constant plasma levels of hypnotics, opioids, cardiovascular medication, insulin or other drugs. We present a case in which an inadvertent rapid intravenous injection of 2 mg remifentanil occurred due to the misuse of a syringe pum

Gastric pH and residual volume after 1 and 2 h fasting time for clear fluids in children†

Introduction Current guidelines suggest a fasting time of 2 h for clear fluids, which is often exceeded in clinical practice, leading to discomfort, dehydration and stressful anaesthesia induction to patients, especially in the paediatric population. Shorter fluid fasting might be a strategy to improve patient comfort but has not been investigated yet. This prospective clinical trial compares gastric pH and residual volume after 1 vs 2 h of preoperative clear fluid fasting. Methods Children (1-16 yr, ASA I or II) undergoing elective procedures in general anaesthesia requiring tracheal intubation were randomized into group A with 60 min or B with 120 min preoperative clear fluid fasting. To determine gastric pH and residual volume, the gastric content was sampled in supine, left and right lateral patient position using an oro-gastric tube after intubation. Data are median (interquartile range) for group A or B (P<0.05). Results In total, 131 children aged 1.01-16.23 yr were included; gastric pH was determined in 120 cases. Patient characteristic data were similar between the two groups, except for gender (46/33 males in group A/B; P=0.02). Despite significantly shorter fasting times for clear fluids in group A compared with group B (76/136 min; P<0.001), no significant difference was observed regarding gastric pH [1.43 (1.30-1.56)/1.44 (1.29-1.68), P=0.66] or residual volume [0.43 (0.21-0.84)/0.46 (0.19-0.78) ml kg−1, P=0.47]. Conclusion One hour clear fluid fasting does not alter gastric pH or residual volume significantly compared with 2 h fasting. Clinical trial registration The study was approved by the local ethics committee (KEK-ZH-Nr. 2011-0034) and registered with ClinicalTrials.gov (NCT01516775

Dexmedetomidine Induced Deep Sedation Mimics Non-Rapid Eye Movement Stage 3 Sleep:Large Scale Validation using Machine Learning

STUDY OBJECTIVES: Dexmedetomidine induced electroencephalogram (EEG) patterns during deep sedation is comparable with natural sleep patterns. Using large scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine induced deep sedation indeed mimics natural sleep patterns. METHODS: We used EEG recordings from three sources in this study: 8707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine induced sedation levels were assessed using the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) score. We extracted twenty-two spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. RESULTS: The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine induced deep sedation (MOAA/S = 0) with AUC > 0.8 outperforming other machine learning models. Power in the delta band (0-4Hz) was selected as an important feature for prediction in addition to power in theta (4-8 Hz) and beta (16-30Hz) bands. CONCLUSIONS: Using a large scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine induced deep sedation state mimics N3 sleep EEG patterns

Autonomous systems in anesthesia : where do we stand in 2020? A narrative review

As most of us are aware, almost every facet of our society is becoming, for better or worse, progressively more technology-dependent. Technological advancement has made autonomous systems, also known as robots, an integral part of our life in several fields, including medicine. The application of robots in anesthesia could be classified into 3 types of robots. The first ones are pharmacological robots. These robots are based on closed-loop systems that allow better-individualized anesthetic drug titration for optimal homeostasis during general anesthesia and sedation. Recent evidence also demonstrates that autonomous systems could control hemodynamic parameters proficiently outperforming manual control in the operating room. The second type of robot is mechanical. They enable automated motorized reproduction of tasks requiring high manual dexterity level. Such robots have been advocated to be more accurate than humans and, thus, could be safer for the patient. The third type is a cognitive robot also known as decision support system. This type of robot is able to recognize crucial clinical situation that requires human intervention. When these events occur, the system notifies the attending clinician, describes relevant related clinical observations, proposes pertinent therapeutic options and, when allowed by the attending clinician, may even administer treatment. It seems that cognitive robots could increase patients' safety. Robots in anesthesia offer not only the possibility to free the attending clinicians from repetitive tasks but can also reduce mental workload allowing them to focus on tasks that require human intelligence such as analytical and clinical approach, lifesaving decision-making capacity, and interpersonal interaction. Nevertheless, further studies have yet to be done to test the combination of these 3 types of robots to maintain simultaneously the homeostasis of multiple biological variables and to test the safety of such combination on a large-scale population