Perioperative Outcome Risk Assessment With Computer Learning Enhancement
NCT ID: NCT05042804
Last Updated: 2022-11-14
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
5114 participants
INTERVENTIONAL
2021-09-01
2022-11-01
Brief Summary
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Detailed Description
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Investigators in the ACT will conduct all case reviews by viewing the patient's records in AlertWatch (AlertWatch, Ann Arbor, MI) and Epic (Epic, Verona, WI). AlertWatch is an FDA-approved patient monitoring system designed for use in the operating room. The version of AlertWatch used in this study has been customized for use in a telemedicine setting. Epic is the electronic health record system utilized at Barnes-Jewish Hospital. Each case review will be randomized in a 1:1 fashion to be completed with or without ML assistance. If the case review is randomized to ML assistance, the investigator will access a display interface (currently deployed as a web application on a secure server) that shows real-time ML predicted likelihood for postoperative death and postoperative AKI. If the case review is not randomized to ML assistance, the investigator will not access this display. After viewing the patient's data, the investigator will predict how likely the patient is to experience postoperative death and postoperative AKI and will document this prediction. The area under the receiver operating characteristic curves for predictions made with ML assistance and without ML assistance will be compared.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
SCREENING
SINGLE
Study Groups
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Machine Learning Assistance
Clinicians in the Anesthesia Control Tower will review patient data using the electronic health record and using AlertWatch, and they will also view the machine learning display. They will then predict how likely the patient is to experience postoperative death and postoperative acute kidney injury.
Machine learning models predicting postoperative death and acute kidney injury
The machine learning display uses data from the electronic health record to predict the likelihood of postoperative death and postoperative acute kidney injury.
No Assistance
Clinicians in the Anesthesia Control Tower will review patient data using the electronic health record and using AlertWatch, but they will not view the machine learning display. They will then predict how likely the patient is to experience postoperative death and postoperative acute kidney injury.
No interventions assigned to this group
Interventions
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Machine learning models predicting postoperative death and acute kidney injury
The machine learning display uses data from the electronic health record to predict the likelihood of postoperative death and postoperative acute kidney injury.
Eligibility Criteria
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Inclusion Criteria
* Surgery during hours of ACT operation (weekdays 7:00am-4:00pm)
* Enrolled in the TECTONICS randomized clinical trial (NCT03923699)
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Foundation for Anesthesia Education and Research
OTHER
Washington University School of Medicine
OTHER
Responsible Party
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Bradley Fritz
Instructor in Anesthesiology
Principal Investigators
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Bradley A Fritz, MD
Role: PRINCIPAL_INVESTIGATOR
Washington University School of Medicine
Locations
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Washington University School of Medicine
St Louis, Missouri, United States
Countries
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References
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Fritz BA, King CR, Abdelhack M, Chen Y, Kronzer A, Abraham J, Tripathi S, Ben Abdallah A, Kannampallil T, Budelier TP, Helsten D, Montes de Oca A, Mehta D, Sontha P, Higo O, Kerby P, Gregory SH, Wildes TS, Avidan MS. Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial. Br J Anaesth. 2024 Nov;133(5):1042-1050. doi: 10.1016/j.bja.2024.08.004. Epub 2024 Sep 10.
Fritz BA, King CR, Abdelhack M, Chen Y, Kronzer A, Abraham J, Tripathi S, Abdallah AB, Kannampallil T, Budelier TP, Helsten D, Montes de Oca A, Mehta D, Sontha P, Higo O, Kerby P, Gregory SH, Wildes TS, Avidan MS. Effect of Machine Learning on Anaesthesiology Clinician Prediction of Postoperative Complications: The Perioperative ORACLE Randomised Clinical Trial. medRxiv [Preprint]. 2024 May 23:2024.05.22.24307754. doi: 10.1101/2024.05.22.24307754.
Fritz B, King C, Chen Y, Kronzer A, Abraham J, Ben Abdallah A, Kannampallil T, Budelier T, Montes de Oca A, McKinnon S, Tellor Pennington B, Wildes T, Avidan M. Protocol for the perioperative outcome risk assessment with computer learning enhancement (Periop ORACLE) randomized study. F1000Res. 2022 Sep 29;11:653. doi: 10.12688/f1000research.122286.2. eCollection 2022.
Other Identifiers
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202108022
Identifier Type: -
Identifier Source: org_study_id
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