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

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. Telemedicin...

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Autores principales: Fritz, Bradley, King, Christopher, Chen, Yixin, Kronzer, Alex, Abraham, Joanna, Ben Abdallah, Arbi, Kannampallil, Thomas, Budelier, Thaddeus, Montes de Oca, Arianna, McKinnon, Sherry, Tellor Pennington, Bethany, Wildes, Troy, Avidan, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397896/
https://www.ncbi.nlm.nih.gov/pubmed/37547785
http://dx.doi.org/10.12688/f1000research.122286.2
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author Fritz, Bradley
King, Christopher
Chen, Yixin
Kronzer, Alex
Abraham, Joanna
Ben Abdallah, Arbi
Kannampallil, Thomas
Budelier, Thaddeus
Montes de Oca, Arianna
McKinnon, Sherry
Tellor Pennington, Bethany
Wildes, Troy
Avidan, Michael
author_facet Fritz, Bradley
King, Christopher
Chen, Yixin
Kronzer, Alex
Abraham, Joanna
Ben Abdallah, Arbi
Kannampallil, Thomas
Budelier, Thaddeus
Montes de Oca, Arianna
McKinnon, Sherry
Tellor Pennington, Bethany
Wildes, Troy
Avidan, Michael
author_sort Fritz, Bradley
collection PubMed
description 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.
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spelling pubmed-103978962023-08-04 Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study Fritz, Bradley King, Christopher Chen, Yixin Kronzer, Alex Abraham, Joanna Ben Abdallah, Arbi Kannampallil, Thomas Budelier, Thaddeus Montes de Oca, Arianna McKinnon, Sherry Tellor Pennington, Bethany Wildes, Troy Avidan, Michael F1000Res Study Protocol 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. F1000 Research Limited 2022-09-29 /pmc/articles/PMC10397896/ /pubmed/37547785 http://dx.doi.org/10.12688/f1000research.122286.2 Text en Copyright: © 2022 Fritz B et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Study Protocol
Fritz, Bradley
King, Christopher
Chen, Yixin
Kronzer, Alex
Abraham, Joanna
Ben Abdallah, Arbi
Kannampallil, Thomas
Budelier, Thaddeus
Montes de Oca, Arianna
McKinnon, Sherry
Tellor Pennington, Bethany
Wildes, Troy
Avidan, Michael
Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title_full Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title_fullStr Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title_full_unstemmed Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title_short Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study
title_sort protocol for the perioperative outcome risk assessment with computer learning enhancement (periop oracle) randomized study
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10397896/
https://www.ncbi.nlm.nih.gov/pubmed/37547785
http://dx.doi.org/10.12688/f1000research.122286.2
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