Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters

Sponsor

Samsung Medical Center (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05762237

Collaborator

(none)

5,064

Enrollment

Anticipated Duration (Days)

Study Details

Study Description

Brief Summary

The investigators aimed to investigate the deep learning model to predict intraoperative hypotension using non-invasive monitoring parameters.

Condition or Disease	Intervention/Treatment	Phase
Intraoperative Hypotension General Anesthesia

Detailed Description

Intraoperative hypotension is associated with various postoperative complications such as acute kidney injury. Therefore, precise prediction and prompt treatment of intraoperative hypotension are important. However, it is difficult to accurately predict intraoperative hypotension based on the anesthesiologists' experience and intuition. Recently, deep learning algorithms using invasive arterial pressure monitoring showed the good predictive ability of intraoperative hypotension. It can help the clinician's decisions. However, most patients undergoing general surgery are monitored by non-invasive parameters. Therefore, the investigators investigate the prediction model for intraoperative hypotension using non-invasive monitoring.

Study Design

Study Type:

Observational

Anticipated Enrollment :

5064 participants

Observational Model:

Cohort

Time Perspective:

Retrospective

Official Title:

Prediction of Intraoperative Hypotension Using Non-invasive Monitoring Devices: Development of Deep Learning Model

Anticipated Study Start Date :

Apr 1, 2023

Anticipated Primary Completion Date :

Apr 29, 2023

Anticipated Study Completion Date :

Apr 30, 2023

Arms and Interventions

Arm	Intervention/Treatment
Group In the open source database (VitalDB, https://vitaldb.net), the patients who underwent general anesthesia with non-invasive monitoring including blood pressure, electrocardiography, pulse oximetry, bispectral index, capnography, and minimal alveolar concentration of inhalation agent.

Outcome Measures

Primary Outcome Measures

Deep learning model's prediction ability on intraoperative hypotension event [through study completion, an average of 3 hour]
Area under the curve the receiver operating characteristic (AUROC) curve for the deep learning model to predict intraoperative hypotension.

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

The patients who are included in the open database, VtialDB.
The patients who underwent inhaled general anesthesia for non-cardiac surgery.
The patients who have non-invasive monitoring data including blood pressure, electrocardiography, pulse oximetry, bispectral index, capnography, and minimum alveolar concentration of inhalation agent.

Exclusion Criteria:

The patient with missing data.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

Samsung Medical Center

Investigators

Principal Investigator: Hyun Joo Ahn, MD, PhD, Samsung Medical Center

Study Documents (Full-Text)

None provided.

More Information

Publications

Responsible Party:

Hyun Joo Ahn, Professor, Samsung Medical Center

ClinicalTrials.gov Identifier:

NCT05762237

Other Study ID Numbers:

SMC 2022-09-096

First Posted:

Mar 9, 2023

Last Update Posted:

Mar 9, 2023

Last Verified:

Mar 1, 2023

Individual Participant Data (IPD) Sharing Statement:

Undecided

Plan to Share IPD:

Undecided

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by Hyun Joo Ahn, Professor, Samsung Medical Center

intraoperative hypotension

noninvasive monitor

deep learning algorithm

Additional relevant MeSH terms:

Hypotension

Study Results

No Results Posted as of Mar 9, 2023