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Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized
Bradley Fritz1, Christopher King1, Yixin Chen2
1Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, 63110, USA.
F1000Research
|August 7, 2023
Summary
Anesthesiology clinicians using machine learning tools can predict postoperative complications, such as death and acute kidney injury, more accurately than those without AI assistance. This study explores AI
Area of Science:
- Anesthesiology and Perioperative Medicine
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Millions of deaths and acute kidney injury (AKI) cases occur post-surgery annually.
- Early risk identification and intraoperative mitigation are crucial for preventing adverse outcomes.
- Intraoperative telemedicine is underutilized in anesthesiology, despite its success in critical care.
Purpose of the Study:
- To evaluate the accuracy of anesthesiology clinicians in predicting postoperative complications with and without machine learning (ML) assistance.
- To explore the potential of ML tools in leveraging electronic health data for perioperative risk assessment.
- To define the role of ML in intraoperative telemedicine and broader surgical risk assessment.
Main Methods:
- A sub-study nested within the TECTONICS randomized clinical trial (NCT03923699).
- Anesthesiology clinicians in a telemedicine setting reviewed surgical cases, assessing likelihood of 30-day in-hospital death or AKI.
- Case reviews were randomized to include access to ML predictions or not, with prediction accuracy compared between groups.
Main Results:
- The study aims to compare the accuracy of ML-assisted predictions versus standard clinical judgment.
- Results will quantify the improvement in predicting postoperative complications (death, AKI) with ML integration.
- This will provide data on the efficacy of ML tools in real-time clinical decision-making during surgery.
Conclusions:
- The study will elucidate the value of machine learning in enhancing the accuracy of anesthesiology clinicians' risk predictions.
- Findings will inform the integration of ML-powered telemedicine into surgical workflows.
- Successful outcomes will support the broader application of ML for perioperative risk assessment and mitigation.
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