General Anesthesia Using Endotracheal Intubation
Conditions
Keywords
artificial intelligence, general anesthesia, capnography, cardiac output
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.
Interventions
OTHERNo Intervention: Observational Cohort
No intervention
Sponsors
Samsung Medical Center
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to 75 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
South Korea
Contacts
Primary ContactHeejoon Jeong, MD
Outcome results
None listed