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.
Study Type
OBSERVATIONAL
Enrollment
2,005
No intervention
Samsung Medical Center
Seoul, South Korea
RECRUITINGPredictability of algorithm
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.
Time frame: Every time points with interval of 5 minutes during surgery
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