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

NCT02914444 | Unknown

• 1 other identifier: VitalDB
• Study Type: observational
• Target: 200,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Anesthesia; Monitoring, Intraoperative

Outcome Measures

Primary Outcomes (1)

• Integrated patient monitoring data (*.vital fime) from multiple anesthesia devices during surgery

  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, plethysmography, and pressure waves) is usually 500 Hz. All the values of abovementioned variables are stored as a single file per patient case, with the extension of \*.vital that is created by the Vital Recorder program. 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.

  during surgery

Secondary Outcomes (4)

• Patient weight

  baseline

• Patient gender

  baseline

• Patient age

  baseline

• Diagnosis

  baseline

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing surgery in Seoul National University Hospital.

You may qualify if:

• Patients undergoing surgery

You may not qualify if:

• N/A

Sponsors & Collaborators

• Seoul National University Hospital: lead

Study Sites (1)

Seoul National University Hospital

Seoul, South Korea

RECRUITING

Related Publications (2)

• 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.

  PMID: 40404499 DERIVED

• 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.

  PMID: 34591024 DERIVED

Study Officials

• Chul-Woo Jung

  Seoul National University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Chul-Woo Jung

CONTACT

82-10-8984-4611; jungcwoo@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: August 30, 2016
• First Posted: September 26, 2016
• Study Start: June 1, 2016
• Primary Completion: June 1, 2025
• Study Completion: June 1, 2025
• Last Updated: December 1, 2023

Record last verified: 2023-11

Locations

KR (1)