Clinical trial NCT06238180: Personalised Real-time Interoperable Sepsis Monitoring (PRISM)
Official title: Prediction of Sepsis in Patients Undergoing Abdominal Surgery: A Prospective, Observational Clinical Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Sepsis
Abdominal Sepsis
Infections
Clinical Deterioration
Hemodynamic Instability
Keywords: sepsis
surgical ward
artificial intelligence
decision-support
continuous monitoring
wearable devices
Lead sponsor: Aisthesis Medical P.C.
Sponsor class: INDUSTRY
Collaborators: name: Larissa University Hospital
name: Technical University of Crete
First posted: 2024-02-02
Enrollment: 55
Sex: ALL
Minimum age (years): 18
Maximum age (years): 120
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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. 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Accuracy of AI-Driven Sepsis Prediction in Postoperative Period
timeFrame: 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.
Secondary outcomes:
Other outcomes:
Locations:
city: Larissa
country: Greece
facility: General University Hospital of Larissa
state: Thessaly
Study officials:
affiliation: Larissa University Hospital
name: Eleni Arnaoutoglou, MD, PhD
role: STUDY_CHAIR
References: