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

Bradley Fritz, Christopher King, Yixin Chen, Alex Kronzer, Joanna Abraham, Arbi Ben Abdallah, Thomas Kannampallil, Thaddeus Budelier, Arianna Montes de Oca, Sherry McKinnon, Bethany Tellor Pennington, Troy Wildes, Michael Avidan

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)653
Number of pages1
JournalF1000Research
Volume11
DOIs
StatePublished - 2022
Externally publishedYes

Keywords

  • Anesthesiology
  • Machine Learning
  • Postoperative Complications
  • Protocol
  • Surgery

ASJC Scopus subject areas

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

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