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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.
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.
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Patient weight | Patient weight (kg) is gathered from the electronic medical recording system (EMR). | baseline |
| Patient gender | Patient gender (M/F) is gathered from the electronic medical recording system (EMR). |
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Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing surgery in Seoul National University Hospital.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chul-Woo Jung | Contact | 82-10-8984-4611 | jungcwoo@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Chul-Woo Jung | Seoul National University Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Seoul National University Hospital | Recruiting | Seoul | South Korea |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40404499 | Derived | 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. | |
| 34591024 |
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| baseline |
| Patient age | Patient age (years) is gathered from the electronic medical recording system (EMR). | baseline |
| Diagnosis | Patient's diagnosis is gathered from the electronic medical recording system (EMR). | baseline |
| 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. |