Perioperative Outcome Risk Assessment With Computer Learning Enhancement (ORACLE)

The Washington University

Status: Completed

Conditions: Acute Kidney Injury; Morality

Treatments: Other: Machine learning models predicting postoperative death and acute kidney injury

Study type: Interventional

Funder types: Other

Identifiers

NCT05042804

202108022

Details and patient eligibility

About

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.

Full 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.

Enrollment: 5,114 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Screening

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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5,114 participants in 2 patient groups

Machine Learning Assistance

Experimental group

Description:

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.

Treatment:

Other: Machine learning models predicting postoperative death and acute kidney injury

No Assistance

No Intervention group

Description:

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.