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

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?

Study Overview

• Status: Recruiting
• Conditions: Hypotension During Surgery
• Intervention / Treatment: Diagnostic test: Prediction algorithm for intraoperative hypotension

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.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Hyun Joo Ahn, MD PhD; Phone Number: 821099330784; Email: hyunjooahn@skku.edu

Study Locations

• Korea, Republic of
  • Seoul, Korea, Republic of, 06351
    • Recruiting
    • Samsung Medical Center
    • Contact: Heejoon Jeong, MD; Phone Number: +82-2-3410-0841; Email: heejoonjeong@skku.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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)

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Study group (single group); All participants are enrolled in single group.	Diagnostic test: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Samsung Medical Center
• Collaborators: Samsung Medical Center, Sungkyunkwan University School of Medicine
• Investigators: Principal Investigator: Hyun Joo Ahn, MD PhD, Samsung Medical Center, Sungkyunkwan University School of Medicine

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): April 11, 2025
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: March 25, 2025
• First Submitted That Met QC Criteria: March 25, 2025
• First Posted (Actual): March 27, 2025

Study Record Updates

• Last Update Posted (Actual): May 15, 2025
• Last Update Submitted That Met QC Criteria: May 14, 2025
• Last Verified: May 1, 2025

More Information

Terms related to this study

• Keywords: intraoperative hypotension; prediction algorithm
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Hypotension
• Other Study ID Numbers: SMC 2025-02-006

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No