Anesthesia decisions during surgery involve controlling and maintaining a patient's anesthetic depth, blood pressures and heart rate, among many other conditions. This decision process relies on sound experience of estimating the impact of the drug inputs on the patient outcomes. Accurate estimation of such drug impacts is difficult due to several factors[1,2]: 1) typically, an anesthesia drug can affect multiple outcomes, 2) the same drug can have differing impacts on different patients, 3) the impacts can be altered by surgical types, procedures, stages, and patient conditions, 4) drug-to-drug interactions can influence patient outcomes. For anesthesiologists, managing such parameters on daily basis accurately and safely depends significantly on training and experience. Based on our increasing knowledge of drug interactions, it is essential to identify measurable relationships between drug administration and corresponding patient outcomes in a more accurate, objective, and reliable format.
Advanced information processing technology can be of great value in this pursuit. For instance, mathematical models can be developed and embedded into anesthesia monitoring systems so that in addition to "monitoring" the current status of a patient, they also can provide a prediction of the patient outcomes in the near-future. Artificial intelligence (AI) techniques and machine learning are also highly suitable in this application since they can use real-time observed data to modify models so that the models can become individualized to the specific patient, and the given type and stage of the surgery.
We report here an effort in developing such a new technology. The core of this technology is a data-based mathematics function model that relates multiple drugs and their interactions to several essential predictive outcomes of surgical patients in the near-future. This predictive capability can then be employed to display the patient's current status along with predictive near-future outcome trajectories. When a drug infusion rate is modified, its estimated impact can be immediately displayed. As a result, if a specifically targeted anesthesia depth or blood pressure level is required, this function can be used to display a computer-assisted "trial" on the system to ensure that the targeted factors can be achieved within required timeframes before the actual drug is administered to the patient.
It is noted that the importance of outcome prediction has been recognized in many procedures in anesthesiology and several scoring methods were employed, including Surgical Apgar, Risk Stratification Tools for predicting morbidity and mortality, and Preoperative Score for predicting postoperative mortality. Computer-assisted outcome prediction and decision assistance are more challenging, and have attracted more attention. Trauma resuscitation errors and their corrections were investigated with AI technology. General discussions on feasibility of AI technology for automated anesthesia drug delivery were reported. These studies have different focuses, use different methods, and report different results in this paper.