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Registry Construction of Intraoperative Vital Signs and Clinical Information in Surgical Patients

NCT ID: NCT02914444

Last Updated: 2023-12-01

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

200000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2016-06-01

Study Completion Date

2025-06-01

Brief Summary

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Monitoring data during anesthesia of surgical patient in the operation room will be collected and stored into the registry automatically. Patients' information and preoperative assessment from medical records will be included. Furthermore, intraoperative events will integrated and entered in the registry.

The purpose of the registry is to establish an automatic and accessible database of surgical patients for further retrospective studies.

Detailed Description

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Capture and storage of vital signs data are performed with our own developed data gathering program, Vital Recorder. The Vital Recorder program is a Windows® software and acquires time-synced patient monitoring data from the serial ports of multiple anesthesia devices like patient monitor, anesthesia machine, bispectral index monitor, cardiac output monitor, infusion pumps, etc. The retrieved data are stored on the local or network data storage.

Variables captured by the Vital Recorder program are as follows: heart rate, blood pressure, saturation, temperature, respiratory parameters, bispectral index, infusion history of target-controlled infusion pump, cardiac output, cerebral oxygen concentration, etc.

Time interval of the data is 1-2 sec for numeric variables. Resolution of waves (analog data such as ECG, plethysmogram, and pressure waves) is usually 500 Hz. Captured data of a patient is stored as a \*.vital file.

A laptop is connected to 4-6 anesthesia devices at the same time via serial connections. Patients enrolled (all of the surgical patients who undergo surgery at our hospital) receive routine anesthesia and surgery. Due to the program's automatic function, the program identifies the start and end of a case then automatically records the vital signs of every patient 24/365, once the program starts.

Patient information is additionally gathered from the electronic medical recording system (EMR). Data from the EMR are as follows: sex, age, weight, height, diagnosis, operation, anesthesia and surgery times, premedical history, perioperative lab data and medication.

List of vital files and patient information is integrated in an encrypted spreadsheet file.

Conditions

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Anesthesia Monitoring, Intraoperative

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Patients undergoing surgery

Exclusion Criteria

* N/A
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Seoul National University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Chul-Woo Jung

Associate Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Chul-Woo Jung

Role: PRINCIPAL_INVESTIGATOR

Seoul National University Hospital

Locations

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Seoul National University Hospital

Seoul, , South Korea

Site Status RECRUITING

Countries

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South Korea

Central Contacts

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Chul-Woo Jung

Role: CONTACT

Phone: 82-10-8984-4611

Email: [email protected]

Facility Contacts

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Chul-Woo Jung, MD. PhD

Role: primary

References

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Yang HL, Celi LA, Lee H, Park SA, Lee S, Jung CW, Lee HC. The effect of selection bias on the performance of a deep learning-based intraoperative hypotension prediction model using real-world samples from a publicly available database. Br J Anaesth. 2025 Sep;135(3):571-581. doi: 10.1016/j.bja.2025.03.024. Epub 2025 May 22.

Reference Type DERIVED
PMID: 40404499 (View on PubMed)

Choe S, Park E, Shin W, Koo B, Shin D, Jung C, Lee H, Kim J. Short-Term Event Prediction in the Operating Room (STEP-OP) of Five-Minute Intraoperative Hypotension Using Hybrid Deep Learning: Retrospective Observational Study and Model Development. JMIR Med Inform. 2021 Sep 30;9(9):e31311. doi: 10.2196/31311.

Reference Type DERIVED
PMID: 34591024 (View on PubMed)

Other Identifiers

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VitalDB

Identifier Type: -

Identifier Source: org_study_id