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Data collection issues.
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| Name | Class |
|---|---|
| Larissa University Hospital | OTHER |
| Technical University of Crete | UNKNOWN |
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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:
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.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Exclusion Criteria:
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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.
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| Name | Affiliation | Role |
|---|---|---|
| Eleni Arnaoutoglou, MD, PhD | Larissa University Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| General University Hospital of Larissa | Larissa | Thessaly | 41110 | Greece |
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| ID | Term |
|---|---|
| D018805 | Sepsis |
| D059413 | Intraabdominal Infections |
| D007239 | Infections |
| D000075902 | Clinical Deterioration |
| ID | Term |
|---|---|
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| D018450 | Disease Progression |
| D020969 | Disease Attributes |