Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
Department of Artificial Intelligence, Sungkyunkwan University College of Computing and Informatics, Suwon-si, Gyeonggi, South Korea.
J Clin Monit Comput. 2024 Dec;38(6):1357-1365. doi: 10.1007/s10877-024-01206-6. Epub 2024 Aug 19.
Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms.
An open-source database of non-cardiac surgery patients ( https://vitadb.net/dataset ) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC).
Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5 min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915-0.918) for the original data and 0.833 (95% CI, 0.830-0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension.
A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.
术中低血压与不良结局相关。预测并主动管理低血压可以降低其发生率。此前，使用人工智能的术中低血压预测算法已开发用于有创动脉血压监测仪。本研究测试了常规的非侵入性监测仪是否也可以使用深度学习算法来预测术中低血压。
使用非心脏手术患者的开源数据库（https://vitadb.net/dataset）来开发深度学习算法。该算法使用来自韩国一家三级医院的外部数据进行验证。术中低血压定义为收缩压低于 90mmHg。输入数据包括五个监测仪：非侵入性血压、心电图、光电容积脉搏波、二氧化碳图和脑电双频指数。主要结局是通过接收者操作特征曲线下面积（AUROC）评估深度学习模型的性能。
分别使用 4754 名和 421 名患者的数据进行算法开发和外部验证。多头注意力架构的全连接模型和具有焦点损失函数的全局注意力局部递归模型能够在发生术中低血压前 5 分钟预测其发生。该算法在原始数据中的 AUROC 为 0.917（95%置信区间[CI]，0.915-0.918），在外部验证数据中的 AUROC 为 0.833（95%CI，0.830-0.836）。注意力图量化了每个监测仪的贡献，表明我们的算法利用了来自每个监测仪的数据，权重范围为 8%至 22%，用于确定低血压。
利用多通道非侵入性监测仪的深度学习模型可以高精度预测术中低血压。需要进行未来的前瞻性研究，以确定该模型是否可以帮助临床医生在使用非侵入性监测的手术患者中预防低血压。
