Clinical trial NCT06734650: Deep Learning Model for Predicting a Peripheral Venous Waveform-based Pulse Pressure Variation
Official title: Development and Validation of a Peripheral Venous Waveform-based Pulse Pressure Variation Calculating Deep Learning Model
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Peripheral Vein
Arterial Wave Reflections
Pulse Pressure Variation
Stroke Volume Variation
Deep Learning Model
Lead sponsor: Seoul National University Bundang Hospital
Sponsor class: OTHER
First posted: 2024-12-16
Enrollment: 150
Sex: ALL
Minimum age (years): 19
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients scheduled for elective hepatectomy under general anesthesia

Brief summary: 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 .

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

Primary outcomes:
measure: Pulse pressure variation
timeFrame: intraoperative period
Secondary outcomes:
Other outcomes:
Locations:
city: Seongnam-si
country: Korea, Republic of
facility: Seoul National University Bundang Hospital
state: Gyunggi-do
Study officials:
affiliation: assistant professor
name: Insun Park, M.D./Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: