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

NCT06897514 | Recruiting

• 1 other identifier: SMC 2025-02-006
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

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?

Conditions

Hypotension During Surgery

Keywords

intraoperative hypotension; prediction algorithm

Outcome Measures

Primary Outcomes (1)

• Value of the Area Under the Receiver-Operating Characteristic curve analysis

  The area under the receiver operating characteristic curve is a measurement of how well a prediction model can predict intraoperative hypotension. It is used to assess the performance of algorithm.

  5 minutes before the occurrence of hypotension during general anesthesia

Study Arms (1)

Study group (single group)

All participants are enrolled in single group.

Diagnostic Test: Prediction algorithm for intraoperative hypotension

Interventions

Prediction algorithm for intraoperative hypotension 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.

Study group (single group)

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Samsung Medical Center: lead
• Samsung Medical Center, Sungkyunkwan University School of Medicine: collaborator

Study Sites (1)

Samsung Medical Center

Seoul, 06351, South Korea

RECRUITING

Related Publications (1)

• 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.

  PMID: 39158783 BACKGROUND

MeSH Terms

Interventions

Prediction Algorithms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Hyun Joo Ahn, MD PhD

  Samsung Medical Center, Sungkyunkwan University School of Medicine

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Hyun Joo Ahn, MD PhD

CONTACT

821099330784; hyunjooahn@skku.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 30 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Anesthesiologist

Study Record Dates

• First Submitted: March 25, 2025
• First Posted: March 27, 2025
• Study Start: April 11, 2025
• Primary Completion: December 1, 2025
• Study Completion: December 31, 2025
• Last Updated: May 15, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)