Perioperative Outcome Risk Assessment With Computer Learning Enhancement (ORACLE)

This study will test whether anesthesiology clinicians working in a telemedicine setting can predict patient risk for postoperative complications (death and acute kidney injury) more accurately with access to a machine learning display than without it.

Study Overview

• Status: Completed
• Conditions: Acute Kidney Injury; Morality
• Intervention / Treatment: Other: Machine learning models predicting postoperative death and acute kidney injury

Detailed Description

The Perioperative Outcome Risk Assessment with Computer Learning Enhancement (Periop ORACLE) study will be a sub-study nested within the ongoing TECTONICS trial (NCT03923699). TECTONICS is a single-center randomized clinical trial assessing the impact of an anesthesiology control tower (ACT) on postoperative 30-day mortality, delirium, respiratory failure, and acute kidney injury. As part of the TECTONICS trial, investigators in the ACT perform medical record case reviews during the early part of surgery and document how likely they feel each patient is to experience postoperative death and acute kidney injury (AKI). In Periop ORACLE, these case reviews will be randomized to be performed with or without access to machine learning (ML) predictions.

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.

• Study Type: Interventional
• Enrollment (Actual): 5114
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Missouri
    • Saint Louis, Missouri, United States, 63110
      • Washington University School of Medicine

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Surgery in the main operating suite at Barnes-Jewish Hospital
• Surgery during hours of ACT operation (weekdays 7:00am-4:00pm)
• Enrolled in the TECTONICS randomized clinical trial (NCT03923699)

Exclusion Criteria:

• None

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: 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.	Other: 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 Intervention: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area under receiver-operating characteristic curve of clinician prediction for postoperative death	Clinicians will predict the likelihood of postoperative death for each case using a categorical scale. A logistic regression will be constructed using the clinician predictions as inputs, and the area under the receiver-operating characteristic curve will be determined.	30 days
Area under receiver-operating characteristic curve of clinician prediction for postoperative acute kidney injury	Clinicians will predict the likelihood of postoperative acute kidney injury for each case using a categorical scale. A logistic regression will be constructed using the clinician predictions as inputs, and the area under the receiver-operating characteristic curve will be determined.	7 days

Collaborators and Investigators

• Sponsor: Washington University School of Medicine
• Collaborators: Foundation for Anesthesia Education and Research
• Investigators: Principal Investigator: Bradley A Fritz, MD, Washington University School of Medicine

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2021
• Primary Completion (Actual): November 1, 2022
• Study Completion (Actual): November 1, 2022

Study Registration Dates

• First Submitted: September 1, 2021
• First Submitted That Met QC Criteria: September 4, 2021
• First Posted (Actual): September 13, 2021

Study Record Updates

• Last Update Posted (Actual): November 14, 2022
• Last Update Submitted That Met QC Criteria: November 11, 2022
• Last Verified: November 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Kidney Diseases; Urologic Diseases; Renal Insufficiency; Acute Kidney Injury
• Other Study ID Numbers: 202108022

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No