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Personalised Real-time Interoperable Sepsis Monitoring (PRISM)

A

Aisthesis Medical

Status

Completed

Conditions

Hemodynamic Instability
Abdominal Sepsis
Infections
Clinical Deterioration
Sepsis

Treatments

Device: VIOSync-SPI

Study type

Observational

Funder types

Other
Industry

Identifiers

NCT06238180
27681/13-06-23

Details and patient eligibility

About

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.

Enrollment

55 patients

Sex

All

Ages

18 to 120 years old

Volunteers

No Healthy Volunteers

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.

Trial contacts and locations

1

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Central trial contact

Ioannis Gkouzionis, PhD; Eleni Arnaoutoglou, MD, PhD

Data sourced from clinicaltrials.gov

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