Intraoperative hypotension is associated with various postoperative complications such as acute kidney injury. Therefore, precise prediction and prompt treatment of intraoperative hypotension are important. However, it is difficult to accurately predict intraoperative hypotension based on the anesthesiologists' experience and intuition. Recently, deep learning algorithms using invasive arterial pressure monitoring showed the good predictive ability of intraoperative hypotension. It can help the clinician's decisions. However, most patients undergoing general surgery are monitored by non-invasive parameters. Therefore, the investigators investigate the prediction model for intraoperative hypotension using non-invasive monitoring.
The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.