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

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

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

Qualitative development and content validation of the "SPART" model:a focused ethnography study of observable diagnostic and therapeutic activities in the emergency medical services care process

Abstract Background Clinical reasoning is a crucial task within the Emergency Medical Services (EMS) care process. Both contextual and cognitive factors make the task susceptible to errors. Understanding the EMS care process’ structure could help identify and address issues that interfere with clinical reasoning. The EMS care process is complex and only basically described. In this research, we aimed to define the different phases of the process and develop an overarching model that can help detect and correct potential error sources, improve clinical reasoning and optimize patient care. Methods We conducted a focused ethnography study utilizing non-participant video observations of real-life EMS deployments combined with thematic analysis of peer interviews. After an initial qualitative analysis of 7 video observations, we formulated a tentative conceptual model of the EMS care process. To test and refine this model, we carried out a qualitative, thematic analysis of 28 video-recorded cases. We validated the resulting model by evaluating its recognizability with a peer content analysis utilizing semi-structured interviews. Results Based on real-life observations, we were able to define and validate a model covering the distinct phases of an EMS deployment. We have introduced the acronym “SPART” to describe ten different phases: Start, Situation, Prologue, Presentation, Anamnesis, Assessment, Reasoning, Resolution, Treatment, and Transfer. Conclusions The “SPART” model describes the EMS care process and helps to understand it. We expect it to facilitate identifying and addressing factors that influence both the care process and the clinical reasoning task embedded in this process

Intranasal dexmedetomidine in elderly subjects with or without beta blockade:a randomised double-blind single-ascending-dose cohort study

BACKGROUND: The aim of this double-blind, placebo-controlled, single-ascending-dose study was to determine the safety and tolerability of intranasal dexmedetomidine in the elderly.; METHODS: We randomly assigned 48 surgical patients ≥ ¥65 yr of age to receive single intranasal doses of dexmedetomidine or placebo (5:1 ratio) in four sequential dose cohorts: 0.5, 1.0, 1.5, and 2.0 mug kg-1. Each dose cohort comprised two groups of six subjects: a group of subjects using beta-blockers and a group not taking beta-blockers. Vital signs and sedation depth (Modified Observer's Assessment of Alertness and Sedation [MOAA/S] and bispectral index) were measured for 2 h after administration. Blood samples were taken to determine dexmedetomidine plasma concentrations.; RESULTS: One subject (1.0 mug kg-1) had acute hypotension requiring ephedrine. Systolic arterial BP decreased >30% in 15 of 40 subjects (37.5%) receiving dexmedetomidine, lasting longer than 5 min in 11 subjects (27.5%). The MAP decreased >30% (>5 min) in 10%, 20%, 50%, and 30% of subjects receiving dexmedetomidine 0.5, 1.0, 1.5, and 2.0 mug kg-1, respectively, irrespective of beta-blocker use. HR decreased 10-26%. MOAA/S score ≤ 3 occurred in 18 (45%) subjects; eight (20%) subjects receiving dexmedetomidine showed no signs of sedation. Tmax was 70 min. Cmax was between 0.15 ng ml-1 (0.5 mug kg-1) and 0.46 ng ml-1 (2.0 mug kg-1).; CONCLUSIONS: Intranasal dexmedetomidine in elderly subjects had a sedative effect, but caused a high incidence of profound and sustained hypotension irrespective of beta-blocker use. The technique is unsuitable for routine clinical use.; CLINICAL TRIAL REGISTRATION: NTR5513 (The Netherlands Trial Registry 5513)

What's New in Intravenous Anaesthesia?:New Hypnotics, New Models and New Applications

New anaesthetic drugs and new methods to administer anaesthetic drugs are continually becoming available, and the development of new PK-PD models furthers the possibilities of using arget controlled infusion (TCI) for anaesthesia. Additionally, new applications of existing anaesthetic drugs are being investigated. This review describes the current situation of anaesthetic drug development and methods of administration, and what can be expected in the near future

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning:A Data-Repurposing Approach

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method. METHODS: Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC). RESULTS: The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2-88.3)%, AUC (95% CI) = 0.89 (0.82-0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep. CONCLUSIONS: We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆

Background: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. Methods: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. Results: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by E-max models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 mu g ml(-1), respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. Conclusions: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations