Personalised Real-time Interoperable Sepsis Monitoring (PRISM)

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.

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.