Peter M. Schumacher

A new closed-loop control system for isoflurane using bispectral index outperforms manual control

Background: Automatic control of depth of hypnosis using the Bispectral Index (BIS) can help to reduce phases of inadequate control. Automated BIS control with propofol or isoflurane administration via an infusion system has recently been described, a comparable study with isoflurane administration via a vaporizer had not been conducted yet. Our hypothesis was that our new model based closed-loop control system can safely be applied clinically and maintains the BIS within a defined target range better than manual control. Methods: Twenty-three patients, American Society of Anesthesiologists risk class I–III, scheduled for decompressive spinal surgery were randomized into groups with either closed-loop or manual control of BIS using isoflurane. An alfentanil target-controlled infusion was adjusted according to standard clinical practice. The BIS target was set to 50 during the operation. The necessity of human intervention in the control system and events of inadequate sedation (BIS <40 or BIS >60) were counted. The number of phases of inadequate control, defined as BIS ≥65 for more than 3 min, were recorded. The performance of the controller was assessed by several indicators (mean absolute deviation and median absolute performance error) and measured during the skin incision phase, the subsequent low flow phase, and the wound closure phase. Recovery profiles of both groups were compared. Results: No human intervention was necessary in the closed-loop control group. The occurrence of inadequate BIS was quantified with the mean and median values of the area under the curve and amounted to 0.360 and 0.088 for the manual control group and 0.049 and 0.017 for the closed-loop control group, respectively. In the manual control group nine phases of inadequate control were recorded, compared with one in the closed-loop control group, 10.3% to 0.5% of all observed anesthesia time. During all phases the averages of the performance parameters (mean absolute deviation and median absolute performance error) were more than 30% smaller in closed-loop control than in manual control (P < 0.05 between groups). Conclusions: Closed-loop control with BIS using isoflurane can safely be applied clinically and performs significantly better than manual control, even in phases with abrupt changes of stimulation that cannot be foreseen by the control system.

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.

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 C50O Hierarchical, and Fixed C50O 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 C50O Hierarchical model performed slightly better than the Scaled C50O 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 C50O Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Anesthesia or sedation for gastroenterologic endoscopies

Purpose of review Because propofol is the sedative preferred by gastroenterologists, we focus this review on gastroenterologist-directed propofol sedation, provide simulations of the respiratory depressant effect of different dosing protocols and give a perspective on future developments in computer-assisted sedation techniques. Recent findings Propofol use by nonanesthesiologists remains a contraindication in the package insert of propofol in most countries. Sedation guidelines produced by the American Society of Gastroenterology partially contradict those produced by the American Society of Anesthesiologists for sedation by nonanesthesiologists, whereas the German guidelines were developed with anesthesiologists involved. The use of fospropofol, recently approved by the US Food and Drug Administration for sedation, is considered an alternative to propofol by some gastroenterologists. Methodological errors in earlier pharmacological studies have to be solved before widespread use of fospropofol is justified, however. Our simulations show that dosing protocols with small boluses administered at reasonable intervals induce less respiratory depression than large boluses. Interindividual variability of propofol-induced respiratory depression is illustrated by different pharmacokinetic and dynamic parameter sets used in the simulation. Two computer-assisted propofol infusion systems are currently being investigated. They not only incorporate the target effect but also the side effects, which may limit respiratory depression. Summary Propofol use by gastroenterologists may be well tolerated if appropriate patient selection, staff training, monitoring and low-dose sedation protocols are applied.

Response surface modeling of the interaction between propofol and sevoflurane

Background:Propofol and sevoflurane display additivity for γ-aminobutyric acid receptor activation, loss of consciousness, and tolerance of skin incision. Information about their interaction regarding electroencephalographic suppression is unavailable. This study examined this interaction as well as the interaction on the probability of tolerance of shake and shout and three noxious stimulations by using a response surface methodology. Methods:Sixty patients preoperatively received different combined concentrations of propofol (0–12 &mgr;g/ml) and sevoflurane (0–3.5 vol.%) according to a crisscross design (274 concentration pairs, 3 to 6 per patient). After having reached pseudo-steady state, the authors recorded bispectral index, state and response entropy and the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy. For the analysis of the probability of tolerance by logistic regression, a Greco interaction model was used. For the separate analysis of bispectral index, state and response entropy suppression, a fractional Emax Greco model was used. All calculations were performed with NONMEM V (GloboMax LLC, Hanover, MD). Results:Additivity was found for all endpoints, the Ce50, PROP/Ce50, SEVO for bispectral index suppression was 3.68 &mgr;g · ml−1/ 1.53 vol.%, for tolerance of shake and shout 2.34 &mgr;g · ml−1/ 1.03 vol.%, tetanic stimulation 5.34 &mgr;g · ml−1 / 2.11 vol.%, laryngeal mask airway insertion 5.92 &mgr;g · ml−1 / 2.55 vol.%, and laryngoscopy 6.55 &mgr;g · ml−1/2.83 vol.%. Conclusion:For both electroencephalographic suppression and tolerance to stimulation, the interaction of propofol and sevoflurane was identified as additive. The response surface data can be used for more rational dose finding in case of sequential and coadministration of propofol and sevoflurane.

Compartmental Pharmacokinetics of Dantrolene in Adults: Do Malignant Hyperthermia Association Dosing Guidelines Work?

Dantrolene is the only drug proven effective for prevention and treatment of malignant hyperthermia (MH). Current dosing recommendations are based on noncompartmental analyses and are largely empiric. They are also divergent, as evidenced by differing recommendations from the Malignant Hyperthermia Association of the United States (MHAUS) and European Sources. We determined the compartmental pharmacokinetics of dantrolene, simulated the concentration time course based on currently recommended dosing, and suggest an optimal regimen. Nine volunteers (55–89 kg) received IV infusions of dantrolene (5 mg/kg over 30 min followed by 0.05 mg · kg−1 · h−1 for 5 h). Venous blood samples were drawn for up to 60 h, and dantrolene plasma concentrations were determined by reverse phase, high-performance liquid chromatography. One, two, and three compartmental models were fitted to the data, and a covariate analysis was performed. All calculations were performed with NONMEM using the population approach. The data were adequately described by a two-compartment model with the following typical variable values (median ± se): volumes of distribution V1 = 3.24 ± 0.61 L; V2 = 22.9 ± 1.53 L; plasma clearance CLel = 0.03 ± 0.003 L/min; and distributional clearance CLdist = 1.24 ± 0.22 L/min. All parameters were scaled linearly with weight. Simulations of European recommendations for treatment of MH lead to plasma concentrations converging to 14–18 mg/L within 24 h. Simulating MHAUS guidelines (intermittent bolus administration) yielded peak and trough plasma concentrations ranging from 6.7–22.6 mg/L. Based on our findings, we propose an infusion regimen adjusted to the initial bolus dose(s) required to control symptoms. This strategy maintains the individualized therapeutic concentrations and improves stability of plasma concentrations.

Control of Drug Administration During Monitored Anesthesia Care

Monitored anesthesia care (MAC) is increasingly used to provide patient comfort for diagnostic and minor surgical procedures. The drugs used in this setting can cause profound respiratory depression even in the therapeutic concentration range. Titration to effect suffers from the difficulty to predict adequate analgesia prior to application of a stimulus, making titration to a continuously measurable side effect an attractive alternative. Exploiting the fact that respiratory depression and analgesia occur at similar drug concentrations, we suggest to administer opioids and propofol during MAC using a feedback control system with transcutaneously measured partial pressures of CO2(PtcCO2) as the controlled variable. To investigate this dosing paradigm, we developed a comprehensive model of human metabolism and cardiorespiratory regulation, including a compartmental pharmacokinetic and a pharmacodynamic model for the fast acting opioid remifentanil. Model simulations are in good agreement with ventilatory experimental data, both in presence and absence of drug. Closed-loop simulations show that the controller maintains a predefined CO2 target in the face of surgical stimulation and variable patient sensitivity. It prevents dangerous hypoventilation and delivers concentrations associated with analgosedation. The proposed control system for MAC could improve clinical practice titrating drug administration to a surrogate endpoint and actively limiting the occurrence of hypercapnia/hypoxia.

Model-based control of neuromuscular block using mivacurium: design and clinical verification.

Background: Short‐acting agents for neuromuscular block (NMB) require frequent dosing adjustments for individual patients needs. In this study, we verified a new closed‐loop controller for mivacurium dosing in clinical trials. Methods: Fifteen patients were studied. T1% measured with electromyography was used as input signal for the model‐based controller. After induction of propofol/opiate anaesthesia, stabilization of baseline electromyography signal was awaited and a bolus of 0.3 mg kg−1 mivacurium was then administered to facilitate endotracheal intubation. Closed‐loop infusion was started thereafter, targeting a neuromuscular block of 90%. Setpoint deviation, the number of manual interventions and surgeons complaints were recorded. Drug use and its variability between and within patients were evaluated. Results: Median time of closed‐loop control for the 11 patients included in the data processing was 135 [89–336] min (median [range]). Four patients had to be excluded because of sensor problems. Mean absolute deviation from setpoint was 1.8 ± 0.9 T1%. Neither manual interventions nor complaints from the surgeons were recorded. Mean necessary mivacurium infusion rate was 7.0 ± 2.2 μg kg−1 min−1. Intrapatient variability of mean infusion rates over 30‐min interval showed high differences up to a factor of 1.8 between highest and lowest requirement in the same patient. Conclusions: Neuromuscular block can precisely be controlled with mivacurium using our model‐based controller. The amount of mivacurium needed to maintain T1% at defined constant levels differed largely between and within patients. Closed‐loop control seems therefore advantageous to automatically maintain neuromuscular block at constant levels.

The well-being model: a new drug interaction model for positive and negative effects

Background: Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account. Methods: By extension of a previously published interaction model, the authors propose a method to study drug interactions considering also their side effects. A general outcome parameter identified as patients well-being is defined by superposition of positive and negative effects. Well-being response surfaces are computed and analyzed for varying drugs pharmacodynamics and interaction types. In particular, the existence of multiple maxima and of optimal drug combinations is investigated for the combination of two drugs. Results: Both drug pharmacodynamics and interaction type affect the well-being surface and the deriving optimal combinations. The effect of the interaction parameters can be explained in terms of synergy and antagonism and remains unchanged for varying pharmacodynamics. For all simulations performed for the combination of two drugs, the presence of more than one maximum was never observed. Conclusions: The model is consistent with clinical knowledge and supports previously published experimental results on optimal drug combinations. This new framework improves understanding of the characteristics of drug combinations used in clinical practice and can be used in clinical research to identify optimal drug dosing.

Automatic Algorithm for Monitoring Systolic Pressure Variation and Difference in Pulse Pressure

BACKGROUND: Difference in pulse pressure (dPP) reliably predicts fluid responsiveness in patients. We have developed a respiratory variation (RV) monitoring device (RV monitor), which continuously records both airway pressure and arterial blood pressure (ABP). We compared the RV monitor measurements with manual dPP measurements. METHODS: ABP and airway pressure (PAW) from 24 patients were recorded. Data were fed to the RV monitor to calculate dPP and systolic pressure variation in two different ways: (a) considering both ABP and PAW (RV algorithm) and (b) ABP only (RVslim algorithm). Additionally, ABP and PAW were recorded intraoperatively in 10-min intervals for later calculation of dPP by manual assessment. Interobserver variability was determined. Manual dPP assessments were used for comparison with automated measurements. To estimate the importance of the PAW signal, RVslim measurements were compared with RV measurements. RESULTS: For the 24 patients, 174 measurements (6–10 per patient) were recorded. Six observers assessed dPP manually in the first 8 patients (10-min interval, 53 measurements); no interobserver variability occurred using a computer-assisted method. Bland-Altman analysis showed acceptable bias and limits of agreement of the 2 automated methods compared with the manual method (RV: −0.33% ± 8.72% and RVslim: −1.74% ± 7.97%). The difference between RV measurements and RVslim measurements is small (bias −1.05%, limits of agreement 5.67%). CONCLUSIONS: Measurements of the automated device are comparable with measurements obtained by human observers, who use a computer-assisted method. The importance of the PAW signal is questionable.