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Personalised Real-time Interoperable Sepsis Monitoring (PRISM)
Sponsor: Aisthesis Medical P.C.
Summary
The goal of this prospective observational study is to develop and utilize an Artificial Intelligence (AI) model for the prediction of postoperative sepsis in patients undergoing abdominal surgery. The main questions it aims to answer are: 1. Can a remote AI-driven monitoring system accurately predict sepsis risk in postoperative patients? 2. How effectively can this system integrate and analyze multimodal data for early sepsis detection in the surgical ward? Participants are equipped with non-invasive PPG-based wearable devices to continuously monitor vital signs and collect high-quality clinical data. This data, along with demographic and laboratory information from the Electronic Health Record (EHR) of the hospital, are used for AI model development and validation.
Official title: Prediction of Sepsis in Patients Undergoing Abdominal Surgery: A Prospective, Observational Clinical Study
Key Details
Gender
All
Age Range
18 Years - 120 Years
Study Type
OBSERVATIONAL
Enrollment
0
Start Date
2023-11-29
Completion Date
2024-06-30
Last Updated
2026-04-09
Healthy Volunteers
Not specified
Interventions
PRISM Tool
The intervention in this study involves an AI-driven clinical decision-support system, PRISM Tool, designed for the early prediction of sepsis in patients undergoing abdominal surgery. PRISM Tool integrates data from PPG-based wearable wireless devices that monitor vital signs, electronic health records, and laboratory tests. The AI model analyzes this multimodal data to proactively identify signs of sepsis providing an early warning score to clinicians. The distinguishing feature of this intervention is its use of real-time data and advanced AI analytics to enhance early sepsis detection, aiming to improve patient outcomes in postoperative care.
Locations (1)
General University Hospital of Larissa
Larissa, Thessaly, Greece