Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil

Abstract

Background The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Methods Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. Results The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). Conclusions The deep learning model–predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Development of a prediction model for hypotension after induction of anesthesia using machine learning

“…EHR and anesthesia data were retrieved from our department's web database. Briefly, intraoperative vital signs and pharmacological data from multiple anesthesia drug-delivery and monitoring devices were recorded in a time-synchronized fashion using a Vital Recorder [8]. Together with these data, EHR data were also stored in our departmental database.…”

Artificial Intelligence for Everyone

“…T HIS month's issue of A NESTHESIOLOGY contains a landmark article applying machine learning to model the interaction of remifentanil and propofol on processed electroencephalogram. 1 It's a big deal.…”

Artificial Intelligence and Machine Learning in Anesthesiology

“…The second paper, by Lee et al, 41 describes a neural network approach to predicting the Bispectral Index (BIS) based upon the infusion history of propofol and remifentanil. This paper is particularly noteworthy because a strongly theoretical approach to this question already exists in the target-controlled infusion literature.…”

“…51 A closedloop control system need not necessarily have any learning capability itself, but it provides the means to collect a large amount of physiologic data from many patients with high fidelity, and this is an essential precursor for machine learning. Access to large volumes of high-quality data will enable more machine-learning successes, such as the offline post hoc prediction of BIS 41 and hypotension 39 discussed above. For now, finding algorithms that provide good clinical predictions in real time should be emphasized.…”