TY - JOUR
T1 - Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
AU - Fritz, Bradley
AU - King, Christopher
AU - Chen, Yixin
AU - Kronzer, Alex
AU - Abraham, Joanna
AU - Ben Abdallah, Arbi
AU - Kannampallil, Thomas
AU - Budelier, Thaddeus
AU - Montes de Oca, Arianna
AU - McKinnon, Sherry
AU - Tellor Pennington, Bethany
AU - Wildes, Troy
AU - Avidan, Michael
N1 - Publisher Copyright:
Copyright: © 2022 Fritz B et al.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Anesthesiology
KW - Machine Learning
KW - Postoperative Complications
KW - Protocol
KW - Surgery
UR - http://www.scopus.com/inward/record.url?scp=85166777797&partnerID=8YFLogxK
U2 - 10.12688/f1000research.122286.2
DO - 10.12688/f1000research.122286.2
M3 - Article
C2 - 37547785
AN - SCOPUS:85166777797
SN - 2046-1402
VL - 11
SP - 653
JO - F1000Research
JF - F1000Research
ER -