Algorithm Predicting Intraoperative Changes in Cardiac Output Using Capnography
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age19-75
SponsorSamsung Medical Center
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Elective surgery under general anesthesia
Adult patients (18 < age < 76)
Patients who were monitored invasive arterial blood pressure (waveform) and capnography (numeric)
Disqualifiers
Emergency surgery
Cardiovascular and thoracic surgery
Known Asthma and Chronic obstructive pulmonary disease (COPD)
Preoperative pulmonary function test (PFT) abnormality over moderate grade
Trial population
Adult patient who underwent surgery under general anesthesia in Samsung Medical Center, Seoul, South Korea
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
No Intervention: Observational Cohort
Other interventionNo intervention
Treatment groups
2,005 Participants
are divided into 1 treatment groupGroup A: capnography-cardiac output cohort1 intervention
Trial outcomes
Primary outcomes
1
Predictability 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 surgerySponsors and contacts
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Source ClinicalTrials.gov