Personalised Real-time Interoperable Sepsis Monitoring (PRISM)

NCT06238180 | Withdrawn

• 1 other identifier: 27681/13-06-23
• Study Type: observational
• Target: N/A
• Locations: 1 country
• Sites: 1

Brief 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. 1.Can a remote AI-driven monitoring system accurately predict sepsis risk in postoperative patients?
2. 2.How effectively can this system integrate and analyze multimodal data for early sepsis detection in the surgical ward?

Conditions

Sepsis; Infections; Clinical Deterioration; Hemodynamic Instability; Abdominal Sepsis

Keywords

sepsis; surgical ward; artificial intelligence; decision-support; continuous monitoring; wearable devices

Outcome Measures

Primary Outcomes (1)

• Accuracy of AI-Driven Sepsis Prediction in Postoperative Period

  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.

Interventions

PRISM Tool DEVICE

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.

Eligibility Criteria

• Age: 18 Years - 120 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• \<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.

Sponsors & Collaborators

• Aisthesis Medical P.C.: lead
• Larissa University Hospital: collaborator
• Technical University of Crete: collaborator

Study Sites (1)

General University Hospital of Larissa

Larissa, Thessaly, 41110, Greece

Location

MeSH Terms

Conditions

Sepsis; Intraabdominal Infections; Infections; Clinical Deterioration

Condition Hierarchy (Ancestors)

Systemic Inflammatory Response Syndrome; Inflammation; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Disease Progression; Disease Attributes

Study Officials

• Eleni Arnaoutoglou, MD, PhD

  Larissa University Hospital

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 18, 2024
• First Posted: February 2, 2024
• Study Start: November 29, 2023
• Primary Completion: June 30, 2024
• Study Completion: June 30, 2024
• Last Updated: April 9, 2026

Record last verified: 2026-04

Locations

GR (1)