A new approach to the modeling and control of postoperative pain

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous non-linearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

Modeling of pain using artificial neural networks

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.

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 patient's mean arterial pressure (MAP) and the drug concentration in the plasma. Maintaining MAP within appropriate ranges provides optimal treatment of the patient's 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.

A rule-based postoperative pain controller: simulation results

We describe a system for controlling postoperative pain, a phenomenon that is difficult to treat by conventional control methods due to interpatient variability, interferences, non-linearity and the lack of a plausible, well-defined mathematical model. The system consists of two phases. In Phase 1 a closed-loop fuzzy controller implementing a suitable control strategy brings the patient to a zero-pain state. In Phase 2, an open-loop computer-assisted continuous infusion controller maintains a constant concentration of the analgesic (alfentanil) in plasma, subject to an upper safety limit on infusion rate; the set-point of this controller is periodically revised (either maintained or reduced) on the basis of feedback on the duration of zero pain (set-point reduction is necessary because the open-loop system has no means of knowing whether analgesic is accumulating in the patient). Pain is quantified by the patient on a numerical scale of 1 to 10 at 1.5-min intervals during Phase 1 and 9-min intervals during Phase 2. In simulation trials in which a fixed approximate model was used for the effect of sedation on pain while the pharmacokinetics of alfentanil were varied from one simulated patient to another, zero pain was achieved in under 15 min with minimal overshoot in plasma drug concentration and was maintained, with only minor deviation, by means of low drug concentrations.