Somatostatin as Inflow Modulator in Liver-transplant Recipients With Severe Portal Hypertension: A Randomized Trial.

OBJECTIVE: To investigate the safety and efficacy of somatostatin as liver inflow modulator in patients with end-stage liver disease (ESLD) and clinically significant portal hypertension (CSPH) undergoing liver transplantation (LT) (ClinicalTrials.gov number,01290172). BACKGROUND: In LT, portal hyperperfusion can severely impair graft function and survival, mainly in cases of partial LT. METHODS: Thirty-three patients undergoing LT for ESLD and CSPH were randomized double-blindly to receive somatostatin or placebo (2:1). The study drug was administered intraoperatively as 5-mL bolus (somatostatin: 500 µg), followed by a 2.5 mL/h infusion (somatostatin: 250 µg/h) for 5 days. Hepatic and systemic hemodynamics were measured, along with liver function tests and clinical outcomes. The ischemia-reperfusion injury (IRI) was analyzed through histological and protein expression analysis. RESULTS: Twenty-nine patients (18 receiving somatostatin, 11 placebo) were included in the final analysis. Ten patients responded to somatostatin bolus, with a significant decrease in hepatic venous portal gradient (HVPG) and portal flow of -28.3% and -29.1%, respectively. At graft reperfusion, HVPG was lower in patients receiving somatostatin (-81.7% vs -58.8%; P = 0.0084), whereas no difference was observed in the portal flow (P = 0.4185). Somatostatin infusion counteracted the decrease in arterial flow (-10% vs -45%; P = 0.0431). There was no difference between the groups in the severity of IRI, incidence of adverse events, long-term complications, graft, and patient survival. CONCLUSIONS: Somatostatin infusion during LT in patients with CSPH is safe, reduces the HVPG, and preserves the arterial inflow to the graft. This study establishes the efficacy of somatostatin as a liver inflow modulator.

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based 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.

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-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model 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.

A response surface model approach for continuous measures of hypnotic and analgesic effect during sevoflurane-remifentanil interaction: quantifying the pharmacodynamic shift evoked by stimulation.

BACKGROUND: The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. METHODS: Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo-steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the "prestimulation" and "poststimulation" BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). RESULTS: The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. CONCLUSIONS: By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of "stimulation" on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index.

Interaction between nitrous oxide, sevoflurane, and opioids: a response surface approach.

BACKGROUND: The interaction of sevoflurane and opioids can be described by response surface modeling using the hierarchical model. We expanded this for combined administration of sevoflurane, opioids, and 66 vol.% nitrous oxide (N2O), using historical data on the motor and hemodynamic responsiveness to incision, the minimal alveolar concentration, and minimal alveolar concentration to block autonomic reflexes to nociceptive stimuli, respectively. METHODS: Four potential actions of 66 vol.% N2O were postulated: (1) N2O is equivalent to A ng/ml of fentanyl (additive); (2) N2O reduces C50 of fentanyl by factor B; (3) N2O is equivalent to X vol.% of sevoflurane (additive); (4) N2O reduces C50 of sevoflurane by factor Y. These four actions, and all combinations, were fitted on the data using NONMEM (version VI, Icon Development Solutions, Ellicott City, MD), assuming identical interaction parameters (A, B, X, Y) for movement and sympathetic responses. RESULTS: Sixty-six volume percentage nitrous oxide evokes an additive effect corresponding to 0.27 ng/ml fentanyl (A) with an additive effect corresponding to 0.54 vol.% sevoflurane (X). Parameters B and Y did not improve the fit. CONCLUSION: The effect of nitrous oxide can be incorporated into the hierarchical interaction model with a simple extension. The model can be used to predict the probability of movement and sympathetic responses during sevoflurane anesthesia taking into account interactions with opioids and 66 vol.% N2O.

Sevoflurane remifentanil interaction: comparison of different response surface models.

BACKGROUND: Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models. METHODS: Forty patients preoperatively received different combined concentrations of remifentanil (0-12 ng/ml) and sevoflurane (0.5-3.5 vol.%) according to a criss-cross design (160 concentration pairs, four per patient). After having reached pseudosteady state, the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy was recorded. For the analysis of the probability of tolerance, five different interaction models were tested: Greco, Reduced Greco, Minto, Scaled C50(O) Hierarchical, and Fixed C50(O) Hierarchical model. All calculations were performed with NONMEM VI (Icon Development Solutions, Ellicott City, MD). RESULTS: The pharmacodynamic interaction between sevoflurane and remifentanil was strongly synergistic for both the hypnotic and the analgesic components of anesthesia. The Greco model did not result in plausible parameter estimates. The Fixed C50(O) Hierarchical model performed slightly better than the Scaled C50(O) Hierarchical and Reduced Greco models, whereas the Minto model fitted less well. CONCLUSION: We showed the importance of exploring various surface model approaches when studying drug interactions. The Fixed C50(O) Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Propofol and remifentanil differentially modulate frontal electroencephalographic activity.

BACKGROUND: The purpose of this study was to evaluate a new, physiologically inspired method for the analysis of the electroencephalogram during propofol-remifentanil anesthesia. Based on fixed-order autoregressive moving-average modeling, this method was hypothesized to be capable of dissociating the effects that hypnotic and analgesic agents have on brain electrical activity. METHODS: Raw electroencephalographic waves from a previously published study were reanalyzed. In this study, 45 American Society of Anesthesiologists status I patients were randomly allocated to one of three groups according to a specific target effect-site remifentanil concentration (0, 2, and 4 ng/ml). All patients received stepwise-increased targeted effect-site concentrations of propofol (CePROP). At each step change in target CePROP, the Observer's Assessment of Alertness/Sedation score was evaluated. Raw electroencephalograph was continuously acquired from frontal electrodes. Electroencephalography traces were analyzed using a fixed-order autoregressive moving average model to give derived measures of Cortical State and Cortical Input. Response surfaces were visualized and modeled using Hierarchical Linear Modeling. RESULTS: Cortical State (a measure of cortical responsiveness) and Cortical Input (a measure of the magnitude of cortical input) were shown to respond differently to CePROP and effect-site remifentanil concentration. Cortical Input decreased significantly with increasing effect-site remifentanil concentration, whereas Cortical State remained unchanged with increasing effect-site remifentanil concentration but decreased with increasing CePROP. CONCLUSION: Because Cortical State responds principally to variations in CePROP, it is a potential measure of hypnosis, whereas the dependence of Cortical Input on effect-site remifentanil concentration suggests that it may be useful as a measure of analgesic efficacy and the nociceptive-antinociceptive balance.

Noxious stimulation response index: a novel anesthetic state index based on hypnotic-opioid interaction.

BACKGROUND: The noxious stimulation response index (NSRI) is a novel anesthetic depth index ranging between 100 and 0, computed from hypnotic and opioid effect-site concentrations using a hierarchical interaction model. The authors validated the NSRI on previously published data. METHODS: The data encompassed 44 women, American Society of Anesthesiology class I, randomly allocated to three groups receiving remifentanil infusions targeting 0, 2, and 4 ng/ml. Propofol was given at stepwise increasing effect-site target concentrations. At each concentration, the observer assessment of alertness and sedation score, the response to eyelash and tetanic stimulation of the forearm, the bispectral index (BIS), and the acoustic evoked potential index (AAI) were recorded. The authors computed the NSRI for each stimulation and calculated the prediction probabilities (PKs) using a bootstrap technique. The PKs of the different predictors were compared with multiple pairwise comparisons with Bonferroni correction. RESULTS: The median (95% CI) PK of the NSRI, BIS, and AAI for loss of response to tetanic stimulation was 0.87 (0.75-0.96), 0.73 (0.58-0.85), and 0.70 (0.54-0.84), respectively. The PK of effect-site propofol concentration, BIS, and AAI for observer assessment of alertness and sedation score and loss of eyelash reflex were between 0.86 (0.80-0.92) and 0.92 (0.83-0.99), whereas the PKs of NSRI were 0.77 (0.68-0.85) and 0.82 (0.68-0.92). The PK of the NSRI for BIS and AAI was 0.66 (0.58-0.73) and 0.63 (0.55-0.70), respectively. CONCLUSION: The NSRI conveys information that better predicts the analgesic component of anesthesia than AAI, BIS, or predicted propofol or remifentanil concentrations. Prospective validation studies in the clinical setting are needed.

Behavior of entropy/complexity measures of the electroencephalogram during propofol-induced sedation: dose-dependent effects of remifentanil.

BACKGROUND: Several new measures based on the regularity of the electroencephalogram signal for the assessment of depth of anesthesia/sedation have been proposed recently. In this study we analyze the influence of remifentanil and electroencephalogram frequency content of the performance of a set of such measures. METHODS: Forty-five patients with American Society of Anesthesiologists physical status I were randomly allocated to one of three groups according to the received dose of predicted effect compartment-controlled remifentanil (0, 2, and 4 ng/ml). All 45 patients received stepwise increased effect site concentration-controlled dose of propofol. At every step of propofol increase, the Observer's Assessment of Alertness/Sedation score was assessed. The following measures were calculated from the electroencephalographic signal: spectral entropy, approximate entropy, Higuchi fractal dimension, Lempel-Ziv complexity, relative beta ratio, and SyncFastSlow measure. RESULTS: The behavior of the electroencephalogram-based measures is highly sensitive to the frequency content of the signal and the dose of remifentanil. The prediction probability with respect to the Observer's Assessment of Alertness/Sedation score of the most discriminative measure, the Higuchi fractal dimension, dropped from 0.90 (electroencephalographic frequency band 6-47 Hz, no remifentanil) to 0.55 when the frequency band was changed to 0.5-19 Hz and to 0.83 when remifentanil concentration was increased to 4 ng/ml. The coeffect of remifentanil on electroencephalographic regularity is bimodal depending on the frequency band of the signal. CONCLUSIONS: Cutting off high frequencies from the electroencephalogram and increased remifentanil concentration deteriorate the performance of the electroencephalogram-based entropy/complexity measures as indicators of the depth of propofol sedation.