Deep Learning Model for Predicting a Peripheral Venous Waveform-based Pulse Pressure Variation
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: peripheral waveform collection.
- Who it may be relevant to
- Registry conditions: Peripheral Vein, Arterial Wave Reflections, Pulse Pressure Variation, Stroke Volume Variation. Basic parameters: 19 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- South Korea
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Development and Validation of a Peripheral Venous Waveform-based Pulse Pressure Variation Calculating Deep Learning Model
Overview
Pulse pressure variation is a monitoring index that indicates the response to fluid therapy in patients receiving mechanical ventilation, and is used as a reference for patients with unstable hemodynamic conditions. However, it is invasive because it requires arterial puncture to collect it. In a previous study by the investigators, the investigators developed and verified an artificial intelligence model that predicts stroke volume variation, in real time using only the central venous pressure waveform. However, since a large vein such as the jugular vein must be punctured to collect the central venous pressure waveform, it is still invasive, and its clinical utility is low. Therefore, in this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .
Detailed description
In this study, the investigators collected waveforms from peripheral veins that are less invasive and can be a wide range of applications because all surgical patients have them. The investigators aimed to develop and verify an artificial intelligence model that predicts pulse pressure variation obtained from peripheral venous waveforms .
Interventions
- Other peripheral waveform collection
The peripheral venous pressure waveform is collected by connecting a pressure transducer that is currently in use to the placed central venous line. In addition, the pulse pressure variation or stroke volume variation value that can be obtained from the arterial catheter. This extracts the medical records and bio-signal information of the subjects registered through the previously approved 'Establishment of a Bio-signal and Clinical Information Registry for the Development of Patient Monitoring
Primary outcome measures
- Pulse pressure variation [Time frame: intraoperative period]
Eligibility criteria
Inclusion criteria
- Patients who voluntarily agreed and signed the written informed consent form before participating in this study
- Adult aged 19 years or older
- American Society of Anesthesiologists physical class (ASA) 1-3
- Patients scheduled for elective hepatectomy under general anesthesia
- Patients who require arterial pressure monitoring and additional peripheral venous access for routine anesthesia preparation
- Non-smokers with normal pulmonary function
Exclusion criteria
- Patients with abnormal findings on electrocardiogram before surgery
- Patients who cannot undergo peripheral venous puncture
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
South Korea · 1 center
- Seoul National University Bundang Hospital — Seongnam-si
Identifiers
NCT: NCT06734650 · B-2411-936-303