Andrea Gentilini

Response surface model for anesthetic drug interactions

Background Anesthetic drug interactions traditionally have been characterized using isobolographic analysis or multiple logistic regression. Both approaches have significant limitations. The authors propose a model based on response-surface methodology. This model can characterize the entire dose–response relation between combinations of anesthetic drugs and is mathematically consistent with models of the concentration–response relation of single drugs. Methods The authors defined a parameter, &thgr;, that describes the concentration ratio of two potentially interacting drugs. The classic sigmoid Emax model was extended by making the model parameters dependent on &thgr;. A computer program was used to estimate response surfaces for the hypnotic interaction between midazolam, propofol, and alfentanil, based on previously published data. The predicted time course of effect was simulated after maximally synergistic bolus dose combinations. Results The parameters of the response surface were identifiable. With the test data, each of the paired combinations showed significant synergy. Computer simulations based on interactions at the effect site predicted that the maximally synergistic three-drug combination tripled the duration of effect compared with propofol alone. Conclusions Response surfaces can describe anesthetic interactions, even those between agonists, partial agonists, competitive antagonists, and inverse agonists. Application of response-surface methodology permits characterization of the full concentration–response relation and therefore can be used to develop practical guidelines for optimal drug dosing.

Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane

A model-based closed-loop control system is presented to regulate hypnosis with the volatile anesthetic isoflurane. Hypnosis is assessed by means of the bispectral index (BIS), a processed parameter derived from the electroencephalogram. Isoflurane is administered through a closed-circuit respiratory system. The model for control was identified on a population of 20 healthy volunteers. It consists of three parts: a model for the respiratory system, a pharmacokinetic model and a pharmacodynamic model to predict BIS at the effect compartment. A cascaded internal model controller is employed. The master controller compares the actual BIS and the reference value set by the anesthesiologist and provides expired isoflurane concentration references to the slave controller. The slave controller maneuvers the fresh gas anesthetic concentration entering the respiratory system. The controller is designed to adapt to different respiratory conditions. Anti-windup measures protect against performance degradation in the event of saturation of the input signal. Fault detection schemes in the controller cope with BIS and expired concentration measurement artifacts. The results of clinical studies on humans are presented.

A new paradigm for the closed-loop intraoperative administration of analgesics in humans

We present a new paradigm for the closed-loop administration of analgesics during general anesthesia. The manipulated variable in the control system is the infusion rate of the opiate alfentanil, administered intravenously through a computer-controlled infusion pump (CCIP). The outputs to be controlled are the patients mean arterial pressure (MAP) and the drug concentration in the plasma. Maintaining MAP within appropriate ranges provides optimal treatment of the patients reactions to surgical stimuli. Maintaining plasma drug concentrations close to a reference value specified by the anesthesiologist allows to titrate analgesic administration to qualitative clinical end-points of insufficient analgesia. MAP is acquired invasively through a catheter cannula. Since plasma drug concentrations cannot be measured on-line, they are estimated via a pharmacokinetic model. We describe an explicit model-predictive controller which achieves the above-mentioned objectives. An upper constraint on drug concentrations is maintained to avoid overdosing. Constraints on the MAP are introduced to trigger a prompt controller reaction during hypertensive and hypotensive periods. Measurement artifacts in the MAP signal are rejected to prevent harmful misbehavior of the controller. We discuss the results of the clinical validation of the controller on humans.

Robust adaptive control of hypnosis during anesthesia

A closed-loop controller for hypnosis was designed and validated. The controller aims at regulating the bispectral index (BIS) with the volatile anesthetic isoflurane administered with a closed-circuit breathing system. The control algorithm consists of a cascaded internal model controller (IMC) where the master loop aims at regulating BIS. The slave loop tracks endtidal concentration references provided by the master controller. In the paper, a tuning method is presented. First, a robust design procedure which guarantees stability of the slave controller despite parametric uncertainties is described. Then, we demonstrate how the estimation of the drugs equilibration constant k/sub e0/ greatly improves performance if the estimated value is used to update the models in the control scheme. In order to do so, an identification scheme for k/sub e0/ is proposed, which requires estimation of the drugs time to peak effect t/sub peak/. The identification algorithm requires few modeling assumptions and guarantees convergence. Simulation results are presented, which quantify both the performance of the identification scheme and the improvement of the closed-loop control performance.

Perioperative closed-loop control of analgesia in humans

Summary form only given. We present a new paradigm for intraoperative closed-loop control of analgesia in humans. The infusion rate of the opiate alfentanil is the manipulated variable which is administered intravenously through a Computer Controlled Infusion Pump (CCIP). The two regulated outputs are the patients Mean Arterial Pressure (MAP) and the drug concentration in the plasma. Maintaining MAP within acceptable ranges improves the patients reactions to surgical stimulation. Tracking plasma concentrations enables anesthesiologists to titrate analgesic administration to other qualitative signs of inadequate analgesia. An explicit Model Predictive Controller (MPC) was designed to achieve the above mentioned goals. The results of clinical tests of the controller on humans are presented and discussed.