The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.

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.

Inclusion Criteria:

• The patients who are included in the open database, VtialDB.
• The patients who underwent inhaled general anesthesia for non-cardiac surgery.
• The patients who have non-invasive monitoring data including blood pressure, electrocardiography, pulse oximetry, bispectral index, and capnography.

Exclusion Criteria:

• The patient with missing data.

The study population included patients who underwent inhaled general anesthesia for non-cardiac surgery between June 2016 and August 2017 at Seoul National University Hospital, Seoul, South Korea.