Clinical trial NCT06897514: External Validation of Prediction Algorithm Using Non-invasive Monitoring Device for Intraoperative Hypotension
Official title: External Validation of Prediction Algorithm Using Non-invasive Monitoring Device for Intraoperative Hypotension
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hypotension During Surgery
Keywords: intraoperative hypotension
prediction algorithm
Lead sponsor: Samsung Medical Center
Sponsor class: OTHER
Collaborators: name: Samsung Medical Center, Sungkyunkwan University School of Medicine
First posted: 2025-03-27
Enrollment: 200
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The patients who undergoing general anesthesia with five non-invasive monitoring device (non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and Bispectral Index)

Brief summary: The goal of this prospective observational study is to externally validate the prediction algorithm using non-invasive monitoring device for intraoperative hypotension. The main question it aims to answer is: Does the prediction algorithm predict intraoperative hypotension effectively?

Detailed description: The hypotension that occurs during surgery is associated with the poor prognosis of patients after surgery. Previous studies have reported that even a short period of time of hypotension increases the risk of postoperative complications such as kidney injury. If anesthesiologists can predict intraoperative hypotension in advance, they can prevent or minimize the damage.

Recently, there are many reports on medical artificial intelligence models that predict the intraoperative hypotension. Among them, the Hypotension Prediction Index (HPI) model has already been commercialized and used in clinical practice. However, HPI has limitations in that it is necessary to perform invasive techniques (arterial cannulation) or to use dedicated equipment at high cost. However, since many of the general anesthesia are performed without invasive monitoring devices, the use of HPI medical devices is subject to considerable restrictions.

The investigators have reported the prediction algorithm for intraoperative hypotension using five non-invasive monitoring devices commonly used in general anesthesia: 1) blood pressure (NBP, number), 2) electrocardiogram (ECG, waveform), 3) end-oxygen saturation waveform (PPG, waveform), 4) end-stage carbon dioxide waveform (ETCO2, waveform), and 5) an anesthesia depth (BIS, number) By conducting a retrospective external validation process using public clinical data from other institutions (tertiary hospital in Korea), the final model was able to have good predictability with an Area Under the Receiver-Operating Characteristic Curve (AUROC) value of 0.917.

However, investigators did not externally validate that algorithm through a prospective designed study. This study intends to externally validate the "hypertension prediction model during surgery using non-invasive monitoring device", which has already reported It is expected that the usefulness and limitations of the prediction model can be evaluated again, and the model can be advanced based on the results.

Eligibility criteria: Inclusion Criteria:

* Adults patients aged 19 or more
* Elective surgery under general anesthesia
* American Society of Anesthesiologists physical status I - III

Exclusion Criteria:

* Vasopressor/Inotrope usage before surgery
* Patients who needs invasive arterial cannulation
* Emergency surgery
* Pregnant or lactating women

Primary outcomes:
measure: Value of the Area Under the Receiver-Operating Characteristic curve analysis
timeFrame: 5 minutes before the occurrence of hypotension during general anesthesia
Secondary outcomes:
Other outcomes:
Locations:
city: Seoul
country: Korea, Republic of
facility: Samsung Medical Center
Study officials:
affiliation: Samsung Medical Center, Sungkyunkwan University School of Medicine
name: Hyun Joo Ahn, MD PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Jeong H, Kim D, Kim DW, Baek S, Lee HC, Kim Y, Ahn HJ. Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices. J Clin Monit Comput. 2024 Dec;38(6):1357-1365. doi: 10.1007/s10877-024-01206-6. Epub 2024 Aug 19.