Predicting the Best Fit

Antinociceptive Agents as General Anesthetic Adjuncts: Supra-additive and Infra-additive Interactions

The hypothesis "General anesthesia consists of producing both loss of consciousness and the inhibition of noxious stimuli reaching the brain and causing arousal" was used as a basis for the review of published data on general anesthetic interactions with antinociceptive agents: opioids, α 2 adrenergic agonists, and systemic sodium channel blockers. This review is focused on a specific type of anesthetic interaction-the transformation of antinociceptive agents into general anesthetic adjuncts. The primary aim is to answer 2 questions. First, how does an antinociceptive agent transform the effect of an anesthetic in providing a certain component of anesthesia-hypnosis, immobility, or hemodynamic response to noxious stimulation? Second, does a combination of an anesthetic with an adjunct result in a simple summation of their respective effects or in a supra-additive or infra-additive interaction? The Medline database was searched for data describing the interactions of antinociceptive agents and general anesthetics. The following classes of antinociceptive agents were considered: opioids, α 2 adrenergic agonists, and systemic sodium channel blockers. Drugs used in combination with antinociceptive agents were general anesthetics and benzodiazepines. The following terms related to drug interactions were used: anesthetic interactions, synergy, antagonism, isobolographic analysis, response surface analysis, and fractional analysis. The interactions of antinociceptive agents with general anesthetics result in a decrease of general anesthetic requirements, which differ for each of the components of general anesthesia: hypnosis, immobility, and hemodynamic response to noxious stimulation. Most studies of the nature of anesthetic interactions are related to opioid-general anesthetic combinations, and their conclusions usually confirm supra-additivity.

Simulation-Based Gastrointestinal Endoscopy Sedations: A Novel Validation to Multidrug Pharmacodynamic Modeling

Pharmacodynamic models have described the interactions between anesthetics. Applying the models to clinical practice is still problematic due to inherent limitations: 1. modeling conditions are different from practice. 2. One model can only describe one endpoint. To tackle these, we propose a new method of model validation for recovery and intraprocedural sedation adequacy with a three-drug pharmacodynamic model using six published clinical studies that contain midazolam, opioid, and propofol. Mean drug dose, intraprocedural sedation level, procedure, and recovery time are extracted from each study. Simulated drug regimens are designed to best approximate study conditions. A published deep sedation model is used for simulation. Model-predicted recovery time and intraprocedural sedation scores are compared with the original clinical study outcomes. The model successfully predicted recovery times in eight out of nine regimens. Lower doses of midazolam are associated with faster recovery. Model prediction of intraprocedural sedation level was compatible with the clinical studies in five out of seven regimens. The three-drug pharmacodynamic model describes the course of gastrointestinal endoscopy sedations from clinical studies well. Model predictions are consistent with the results from clinical studies. The approach implies that large scale validation can be performed repeatedly.

Response surface model comparison and combinations for remifentanil and propofol in describing response to esophageal instrumentation and adverse respiratory events

BACKGROUND

The primary aim of this essay was to explore the best fitting model in remifentanil-propofol combined administrations during esophageal instrumentation (EI) from five distinct response surface models. The secondary aim was to combine the models to give appropriate effect-site drug concentrations (Ces) range with maximal comfort and safety.

METHODS

The Greco, reduced Greco, Minto, Scaled C50 Hierarchy and Fixed C50 Hierarchy models were constructed to fit four drug effects: loss of response to esophageal instrumentation (LREI), loss of response to esophageal instrumentation revised (LREIR), intolerable ventilatory depression (IVD) and respiratory compromise (RC). Models were tested by chi-square statistical test and evaluated with Akaike Information Criterion (AIC). Model prediction performance were measured by successful prediction rate (SPR) and three prediction errors.

RESULTS

The reduced Greco model was the best fitting model for LREI and RC, and the Minto model was the best fitting model for LREIR and IVD. The SPRs of reduced Greco model for LREI and RC were 81.76% and 79.81%. The SPRs of Minto model for LREIR and IVD were 80.32% and 80.12%. Overlay of the reduced Greco model for LREI and Minto model for IVD offered visual aid for guidance in drug administration.

CONCLUSIONS

Using proper response surface model to fit different drug effects will describe the interactions between anesthetic drugs better. Combining response surface models to select the more reliable effect-site drug concentrations range can be used to guide clinical drug administration with greater safety and provide an improvement of anesthesia precision.

A Response Surface Analysis of the Combination of Dexmedetomidine and Sufentanil for Attenuating the Haemodynamic Response to Endotracheal Intubation

Purpose

Dexmedetomidine combined with opioids has been extensively used to blunt cardiovascular responses to endotracheal intubation. To determine their interaction, we aimed to develop a response surface model between dexmedetomidine and sufentanil.

Methods

One hundred and twenty patients undergoing scheduled gynaecological surgery were recruited. According to a simulation of slice design, patients received different dose pairs of dexmedetomidine (0 to 1.1 μg/kg) and sufentanil (.1 to .5 μg/kg). The mean arterial blood pressure and heart rate of patients were recorded just before endotracheal intubation, immediately after intubation, and during the first 3 min after intubation. The primary outcomes were haemodynamic changes. The full dose–response relationship between dexmedetomidine and sufentanil was analysed using a logit model.

Results

This response surface model revealed that the interaction between dexmedetomidine and sufentanil was additive. The dose pairs that could effectively attenuate the haemodynamic response to endotracheal intubation primarily ranged from .3 to .4 μg/kg and .5 to 1.1 μg/kg for sufentanil and dexmedetomidine, respectively.

Conclusion

When used propofol as the main hypnotic drug during anaesthesia induction, dexmedetomidine could effectively reduce the requirement of sufentanil in an additive manner. However, it is not an effective drug for ablating the cardiovascular response to endotracheal intubation when used alone. The clinical trial registry. The trial registry name: Chinese Clinical Trial Registry. Registration number: ChiCTR1800015273. URL: http://www.chictr.org.cn

Opioid and propofol pharmacodynamics modeling during brain mapping in awake craniotomy

BACKGROUND

Awake craniotomy (AC) is performed to identify cerebral language center. The challenge of anesthesia is to maintain a calm, comfortable, and cooperative patient during the mapping phase. Response surface models (RSMs) are multidrug modeling algorithms. In this pharmacodynamic study, we investigate the first use of RSM with bispectral index (BIS) to predict patient's response to name calling (RNC) and wakefulness (complete neurological tests) during AC.

METHODS

The study is performed in two phases. We prospectively enrolled 40 patients who received video-assisted thoracoscopic surgery (VATS) using propofol and fentanyl as the modeling group. Effect-site concentrations (Ce) and BIS values were recorded and a RSM is built from the data set. We verified the RSM retrospectively in AC patients, designated as the validation group. Corresponding BIS values were analyzed for RNC and wakefulness.

RESULTS

A total of 155 data sets of propofol Ce, fentanyl Ce, and BIS pairs were available for modeling. The range of propofol and fentanyl Ce were 0 to 9.95 μg/mL and 0 to 3.69 ng/mL, respectively. Observed BIS ranged from 21 to 98. The model identified an additive interaction between propofol and an opioid. RNC at BIS 64 is predicted by the model and 70 is required for wakefulness.

CONCLUSION

RSM built from VATS patients is verified with a separate group of AC patient. The BIS target advised for RSM-predicted wakefulness is 70. The model illustrates the timeline to wakefulness during AC under propofol and an opioid. It has implications in guiding, dosing, and estimation of time to wakefulness with propofol and an opioid.

Previously published drug interaction models predict loss of response for transoesophageal echocardiography sedation well but not response to oesophageal instrumentation

Response surface models (RSMs) were used to predict effects of multiple drugs interactions. Our study was aimed to validate accuracy of the previous published volunteer models during transoesophageal echocardiography (TEE). This is a cross-sectional study with 20 patients scheduled for transesophageal echocardiography in Taipei Veterans General Hospital, Taiwan. Effect-site concentration pairs of alfentanil and propofol were recorded and converted to equivalent remifentanil and propofol effect-site concentrations. Observer’s Assessment of Alertness/Sedation (OAA/S) scores were assessed every 2 minutes. Using these data, previous published models of loss of response (LOR), intolerable ventilatory depression (IVD), and loss of response to esophageal instrumentation (LREI) were then estimated. Accuracy of prediction is assessed by calculating the difference between the true response and the model-predicted probability. Clinical events such as interruption of TEE were recorded. The average procedure time was 11 minutes. Accuracy for prediction of LOR and LREI is 63.6% and 38.5%, respectively. There were four patients experienced desaturation for less than 1 minute, which were not predicted by IVD model, and one interruption of TEE due to involuntary movement. The previous published drug-interaction RSMs predict LOR well but not LREI for TEE sedation. Further studies using response surface methodology are needed to improve quality for TEE sedation and clinical implementation.