AMICAS Project

The Adaptive Multi-drug Infusion Control system for general Anesthesia in major Surgery project integrates human expertise with computer optimization to create a successful solution for breakthrough into clinical practice


General anesthesia is a complex control problem present in various medical applications of drug management systems.
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Key challenges addressed.

The ERC AMICAS project proposes an Adaptive Multi-drug Infusion Control system for general Anesthesia in major Surgery. A major challenge in anesthesia is to adapt the drug infusion rates from observed patient response to surgical actions. The patient models are based on nominal population characteristic response and lack specific surgical effects. In major surgery (for instance, cardiac, transplant, bariatric surgery) modelling uncertainty arises from significant blood losses, anomalous drug diffusion, drug effect synergy/antagonism, anesthetic-hemodynamic interactions, etc. This complex interplay requires superhuman abilities of the anesthesiologist, acquired along many years of training and practice. How can we mimic this large amount of expertise? How to provide support for their critical decisions? .

Computer controlled anesthesia holds the answer to be the game changer for best surgery outcomes. Although few, clinical studies report that computer-based anesthesia for one or two drugs outperforms manual management. In reality, clinical practice performs a multi-drug optimization problem while mitigating large patient model uncertainty. The anesthesiologist makes decisions based on future surgeon actions and expected patient response. This is a predictive control strategy, a mature methodology in systems and control engineering with great potential to induce faster recovery times and lower the risk of post-surgery complications.

The interdisciplinary team of AMICAS aims to advance the scope and clinical use of computer based constrained optimization of multi-drug infusion rates for anesthesia with strong effects on hemodynamics. In doing so, we identify multivariable models and minimize the large uncertainties in patient response. With adaptation mechanisms from nominal to individual patient models, we design multivariable optimal predictive control methodologies to manage strongly coupled dynamics occurring in patient’s vital signs during major surgery. Ultimately, to maximize the performance of the closed loop, we model the surgical stimulus as a known disturbance signal and additional bolus infusions from anesthesiologist as known inputs.


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