Personalised Real-time Interoperable Sepsis Monitoring (PRISM) (PRISM)

April 6, 2026 updated by: Aisthesis Medical P.C.

Prediction of Sepsis in Patients Undergoing Abdominal Surgery: A Prospective, Observational Clinical Study

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.

Study Overview

Status

Withdrawn

Conditions

Intervention / Treatment

Study Type

Observational

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Greece
    • Thessaly
      • Larissa, Thessaly, Greece, 41110
        • General University Hospital Of Larissa

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

The study population for the observational study on sepsis prediction are postoperative abdominal surgery patients >18 years of age, selected from a hospital setting, specifically targeting patients admitted for abdominal surgery. This includes a diverse demographic of adult patients undergoing various types of abdominal surgeries. The selection will focus on ensuring a representative sample of this patient group to accurately assess the efficacy and applicability of the AI-driven sepsis prediction system in a real-world clinical environment.

Description

Inclusion Criteria:

  • Patients undergoing elective abdominal surgery.
  • Postoperative admission to the surgical ward.
  • Age 18 years or older, who are able and willing to participate and have given written consent.
  • On admission, the primary investigator assess their risk to deteriorate during the first 72 hours after admission as reasonably high.

Exclusion Criteria:

  • <18 years of age Known allergy or contraindication to the monitoring devices.
  • Pre-existing conditions that could interfere with the study (e.g., chronic sepsis, immunodeficiency disorders).
  • Day case surgery.
  • Pregnancy.
  • Immediate transfer to ICU postoperatively.
  • Patient refusal or unable to give written consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of AI-Driven Sepsis Prediction in Postoperative Period
Time Frame: The accuracy of sepsis prediction will be assessed from the day of surgery, assessed daily for up to 7 days post-surgery or until hospital discharge.
This primary outcome measure evaluates the accuracy of an AI-driven monitoring system in predicting postoperative sepsis among patients undergoing abdominal surgery. The measure focuses on the system's ability to correctly identify sepsis, considering sensitivity, specificity, and predictive values.
The accuracy of sepsis prediction will be assessed from the day of surgery, assessed daily for up to 7 days post-surgery or until hospital discharge.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Aisthesis Medical P.C.

Collaborators

Larissa University Hospital
Technical University of Crete

Investigators

  • Study Chair: Eleni Arnaoutoglou, MD, PhD, Larissa University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

November 29, 2023

Primary Completion (Estimated)

June 30, 2024

Study Completion (Estimated)

June 30, 2024

Study Registration Dates

First Submitted

January 18, 2024

First Submitted That Met QC Criteria

January 25, 2024

First Posted (Actual)

February 2, 2024

Study Record Updates

Last Update Posted (Actual)

April 9, 2026

Last Update Submitted That Met QC Criteria

April 6, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 27681/13-06-23

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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