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.

Population Pharmacodynamics of Propofol and Sevoflurane in Healthy Volunteers Using a Clinical Score and the Patient State Index: A Crossover Study.

BACKGROUND: The population pharmacodynamics of propofol and sevoflurane with or without opioids were compared using the endpoints no response to calling the person by name, tolerance to shake and shout, tolerance to tetanic stimulus, and two versions of a processed electroencephalographic measure, the Patient State Index (Patient State Index-1 and Patient State Index-2). METHODS: This is a reanalysis of previously published data. Volunteers received four anesthesia sessions, each with different drug combinations of propofol or sevoflurane, with or without remifentanil. Nonlinear mixed effects modeling was used to study the relationship between drug concentrations, clinical endpoints, and Patient State Index-1 and Patient State Index-2. RESULTS: The C50 values for no response to calling the person by name, tolerance to shake and shout, and tolerance to tetanic stimulation for propofol (µg · ml) and sevoflurane (vol %; relative standard error [%]) were 1.62 (7.00)/0.64 (4.20), 1.85 (6.20)/0.90 (5.00), and 2.82 (15.5)/0.91 (10.0), respectively. The C50 values for Patient State Index-1 and Patient State Index-2 were 1.63 µg · ml (3.7) and 1.22 vol % (3.1) for propofol and sevoflurane. Only for sevoflurane was a significant difference found in the pharmacodynamic model for Patient State Index-2 compared with Patient State Index-1. The pharmacodynamic models for Patient State Index-1 and Patient State Index-2 as a predictor for no response to calling the person by name, tolerance to shake and shout, and tetanic stimulation were indistinguishable, with Patient State Index50 values for propofol and sevoflurane of 46.7 (5.1)/68 (3.0), 41.5 (4.1)/59.2 (3.6), and 29.5 (12.9)/61.1 (8.1), respectively. Post hoc C50 values for propofol and sevoflurane were perfectly correlated (correlation coefficient = 1) for no response to calling the person by name and tolerance to shake and shout. Post hoc C50 and Patient State Index50 values for propofol and sevoflurane for tolerance to tetanic stimulation were independent within an individual (correlation coefficient = 0). CONCLUSIONS: The pharmacodynamics of propofol and sevoflurane were described on both population and individual levels using a clinical score and the Patient State Index. Patient State Index-2 has an improved performance at higher sevoflurane concentrations, and the relationship to probability of responsiveness depends on the drug used but is unaffected for Patient State Index-1 and Patient State Index-2.

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 MOAA/S independent of the drug used. CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.

Electroencephalography and Brain Oxygenation Monitoring in the Perioperative Period.

Maintaining brain function and integrity is a pivotal part of anesthesiological practice. The present overview aims to describe the current role of the 2 most frequently used monitoring methods for evaluation brain function in the perioperative period, ie, electroencephalography (EEG) and brain oxygenation monitoring. Available evidence suggests that EEG-derived parameters give additional information about depth of anesthesia for optimizing anesthetic titration. The effects on reduction of drug consumption or recovery time are heterogeneous, but most studies show a reduction of recovery times if anesthesia is titrated along processed EEG. It has been hypothesized that future EEG-derived indices will allow a better understanding of the neurophysiological principles of anesthetic-induced alteration of consciousness instead of the probabilistic approach most often used nowadays.Brain oxygenation can be either measured directly in brain parenchyma via a surgical burr hole, estimated from the venous outflow of the brain via a catheter in the jugular bulb, or assessed noninvasively by near-infrared spectroscopy. The latter method has increasingly been accepted clinically due to its ease of use and increasing evidence that near-infrared spectroscopy-derived cerebral oxygen saturation levels are associated with neurological and/or general perioperative complications and increased mortality. Furthermore, a goal-directed strategy aiming to avoid cerebral desaturations might help to reduce these complications. Recent evidence points out that this technology may additionally be used to assess autoregulation of cerebral blood flow and thereby help to titrate arterial blood pressure to the individual needs and for bedside diagnosis of disturbed autoregulation.

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.

Neurally mediated propagating discrete clustered contractions superimposed on myogenic ripples in ex vivo segments of human ileum.

Narrow muscle strips have been extensively used to study intestinal contractility. Larger specimens from laboratory animals have provided detailed understanding of mechanisms that underlie patterned intestinal motility. Despite progress in animal tissue, investigations of motor patterns in large, intact specimens of human gut ex vivo have been sparse. In this study, we tested whether neurally dependent motor patterns could be detected in isolated specimens of intact human ileum. Specimens (n = 14; 7-30 cm long) of terminal ileum were obtained with prior informed consent from patients undergoing colonic surgery for removal of carcinomas. Preparations were set up in an organ bath with an array of force transducers, a fiberoptic manometry catheter, and a video camera. Spontaneous and distension-evoked motor activity was recorded, and the effects of lidocaine, which inhibits neural activity, were studied. Myogenic contractions (ripples) occurred in all preparations (6.17 ± 0.36/min). They were of low amplitude and formed complex patterns by colliding and propagating in both directions along the specimen at anterograde velocities of 4.1 ± 0.3 mm/s and retrogradely at 4.9 ± 0.6 mm/s. In five specimens, larger amplitude clusters of contractions were seen (discrete clustered contractions), which propagated aborally at 1.05 ± 0.13 mm/s and orally at 1.07 ± 0.09 mm/s. These consisted of two to eight phasic contractions that aligned with ripples. These motor patterns were abolished by addition of lidocaine (0.3 mM). The ripples continued unchanged in the presence of this neural blocking agent. These results demonstrate that both myogenic and neurogenic motor patterns can be studied in isolated specimens of human small intestine.

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram.

BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings. METHODS: Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores. RESULTS: The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80. CONCLUSIONS: We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.