Algorithm Predicting Intraoperative Changes in Cardiac Output Using Capnography

Recruiting

• Conditions: General Anesthesia Using Endotracheal Intubation

• Registration Number: NCT07061548
• Lead Sponsor: Samsung Medical Center

Brief Summary

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.

Detailed Description

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.

Study Timeline

• Status Verified: 2025-07
• Study First Submitted: 2025-07-02
• Study First Submitted QC: 2025-07-02
• Study Start: 2025-07-03
• Study First Posted: 2025-07-11
• Last Update Submitted: 2025-07-15
• Last Update Posted: 2025-07-18
• Primary Completion: 2025-12-31
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2005

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Predictability of algorithm	Every time points with interval of 5 minutes during surgery	The performance of the algorithm to predict whether cardiac output has decreased by more than 20% compared to 5 minutes ago. Predictability is estimated by area under the receiver-operating characteristic curve analysis.

Trial Locations

Locations (1)

Samsung Medical Center; 🇰🇷 Seoul, Korea, Republic of