Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study

Author:

Fritz BradleyORCID,King Christopher,Chen Yixin,Kronzer Alex,Abraham Joanna,Ben Abdallah Arbi,Kannampallil Thomas,Budelier ThaddeusORCID,Montes de Oca Arianna,McKinnon Sherry,Tellor Pennington Bethany,Wildes TroyORCID,Avidan MichaelORCID

Abstract

Background: More than four million people die each year in the month following surgery, and many more experience complications such as acute kidney injury. Some of these outcomes may be prevented through early identification of at-risk patients and through intraoperative risk mitigation. Telemedicine has revolutionized the way at-risk patients are identified in critical care, but intraoperative telemedicine services are not widely used in anesthesiology. Clinicians in telemedicine settings may assist with risk stratification and brainstorm risk mitigation strategies while clinicians in the operating room are busy performing other patient care tasks. Machine learning tools may help clinicians in telemedicine settings leverage the abundant electronic health data available in the perioperative period. The primary hypothesis for this study is that anesthesiology clinicians can predict postoperative complications more accurately with machine learning assistance than without machine learning assistance. Methods: This investigation is a sub-study nested within the TECTONICS randomized clinical trial (NCT03923699). As part of TECTONICS, study team members who are anesthesiology clinicians working in a telemedicine setting are currently reviewing ongoing surgical cases and documenting how likely they feel the patient is to experience 30-day in-hospital death or acute kidney injury. For patients who are included in this sub-study, these case reviews will be randomized to be performed with access to a display showing machine learning predictions for the postoperative complications or without access to the display. The accuracy of the predictions will be compared across these two groups. Conclusion: Successful completion of this study will help define the role of machine learning not only for intraoperative telemedicine, but for other risk assessment tasks before, during, and after surgery. Registration: ORACLE is registered on ClinicalTrials.gov: NCT05042804; registered September 13, 2021.

Funder

Foundation for Anesthesia Education and Research

National Center for Advancing Translational Sciences

Agency for Healthcare Research and Quality

National Science Foundation

National Institute of Nursing Research

Publisher

F1000 Research Ltd

Subject

General Pharmacology, Toxicology and Pharmaceutics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

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