Conventional monitoring of cardiac output requires an invasive procedure and an additional device, which can lead to increased risk and cost. Investigators developed an artificial intelligence algorithm to predict intraoperative changes in cardiac output using capnography in patients undergoing surgery under general anesthesia.

Anesthesiologists strive to maintain adequate cardiac output during surgery. However, conventional monitoring of cardiac output requires an invasive procedure (risk) and an additional device (cost).

Because most surgeries are performed without any invasive monitors, anesthesiologists must manage the patients without cardiac output information.

However, modern anesthesia machines usually provide capnography, and continuous capnography monitoring can help estimate changes in cardiac output. Therefore, investigators aim to develop an artificial intelligence algorithm to predict intraoperative changes in cardiac output using capnography in patients undergoing surgery under general anesthesia.

Investigators train a model using capnography data (5-minute duration) related to a 20% or greater decrease in cardiac output during the same period. The developed model can provide an alarm for a decrease in cardiac output based on the change in capnography.

Inclusion Criteria:

• Elective surgery under general anesthesia
• Adult patients (18 < age < 76)
• Patients who were monitored invasive arterial blood pressure (waveform) and capnography (numeric)

Exclusion Criteria:

• Emergency surgery
• Cardiovascular and thoracic surgery
• Known Asthma and Chronic obstructive pulmonary disease (COPD)
• Preoperative pulmonary function test (PFT) abnormality over moderate grade
• Intraoperative monitoring duration less than 30 minutes

Adult patient who underwent surgery under general anesthesia in Samsung Medical Center, Seoul, South Korea