Authors: Hyung-Chul Lee, M.D. et al
Anesthesiology 3 2018, Vol.128, 492-501. doi:10.1097/ALN.0000000000001892
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.
What We Already Know about This Topic
- The combined effects of propofol and remifentanil on the bispectral index have been characterized using isobole and response surface models
- Deep learning is a kind of machine learning based on a set of algorithms to model high-level abstractions in data using multiple linear and nonlinear transformations
What This Article Tells Us That Is New
- An empirical model was developed from propofol and remifentanil dosing histories and demographic data to predict bispectral index during total intravenous anesthesia target-controlled infusions using a deep learning approach
- The deep learning model had less error in predicting bispectral index during anesthesia induction, maintenance, and recovery periods than the response surface model
- The generalizability of the deep learning model is very dependent on the training data set