From the Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Korea.
Anesthesiology. 2018 Mar;128(3):492-501. doi: 10.1097/ALN.0000000000001892.
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.
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.
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).
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.
在丙泊酚和瑞芬太尼靶控输注静脉麻醉中，预测效应部位浓度与双频谱指数之间存在差异是一个问题。我们假设，在全静脉麻醉中，深度学习方法将更准确地预测双频谱指数。
长短期记忆和前馈神经网络分别对经验模型的药代动力学和药效学部分进行模拟，以预测丙泊酚和瑞芬太尼联合使用时的术中双频谱指数。长短期记忆的输入是丙泊酚和瑞芬太尼的输注史，从靶控输注泵中以 10 秒的间隔提取 1800 秒的输注史。前馈网络的输入是长短期记忆的输出以及年龄、性别、体重和身高等人口统计学数据。前馈网络的最终输出是双频谱指数。将深度学习模型与之前报道的响应面模型的双频谱指数预测性能进行比较。
模型超参数包括长短期记忆层中的 8 个记忆单元和前馈网络中的 16 个节点。模型的训练和测试分别使用了 131 例和 100 例独立数据集。深度学习模型的一致性相关系数（95%置信区间）为 0.561（0.560 至 0.562），明显大于响应面模型的 0.265（0.263 至 0.266），P<0.001。
与传统模型相比，深度学习模型预测丙泊酚和瑞芬太尼靶控输注时的双频谱指数更准确。由于其出色的性能和可扩展性，麻醉药理学中的深度学习方法似乎很有前途。
