External Validation of Prediction Algorithm Using Non-invasive Monitoring Device for Intraoperative Hypotension

NCT ID: NCT06897514

Last Updated: 2025-05-15

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-04-11
• Study Completion Date: 2025-12-31

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.

Conditions

Hypotension During Surgery

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Study group (single group)

All participants are enrolled in single group.

Prediction algorithm for intraoperative hypotension

Intervention Type: DIAGNOSTIC_TEST

All participants will receive five non-invasive monitoring during their surgery. Data from these monitoring device will be put into the prediction algorithm.

Interventions

Prediction algorithm for intraoperative hypotension

All participants will receive five non-invasive monitoring during their surgery. Data from these monitoring device will be put into the prediction algorithm.

Intervention Type: DIAGNOSTIC_TEST

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

• Minimum Eligible Age: 19 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Samsung Medical Center, Sungkyunkwan University School of Medicine

UNKNOWN

Sponsor Role: collaborator

Samsung Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Hyun Joo Ahn

Professor, Anesthesiologist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Hyun Joo Ahn, MD PhD

Role: PRINCIPAL_INVESTIGATOR

Samsung Medical Center, Sungkyunkwan University School of Medicine

Locations

Samsung Medical Center

Seoul, , South Korea

Site Status: RECRUITING

Countries

South Korea

Central Contacts

Hyun Joo Ahn, MD PhD

Role: CONTACT

hyunjooahn@skku.edu

821099330784

Facility Contacts

Heejoon Jeong, MD

Role: primary

heejoonjeong@skku.edu

+82-2-3410-0841

References

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.

Reference Type: BACKGROUND

PMID: 39158783 (View on PubMed)

Other Identifiers

SMC 2025-02-006

Identifier Type: -

Identifier Source: org_study_id