Perioperative Outcome Risk Assessment With Computer Learning Enhancement

NCT ID: NCT05042804

Last Updated: 2022-11-14

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

5114 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-09-01

Study Completion Date

2022-11-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Morality Acute Kidney Injury

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SCREENING

Blinding Strategy

SINGLE

Participants

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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.

Group Type EXPERIMENTAL

Machine learning models predicting postoperative death and acute kidney injury

Intervention Type OTHER

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.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

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.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Foundation for Anesthesia Education and Research

OTHER

Sponsor Role collaborator

Washington University School of Medicine

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Bradley Fritz

Instructor in Anesthesiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Bradley A Fritz, MD

Role: PRINCIPAL_INVESTIGATOR

Washington University School of Medicine

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Washington University School of Medicine

St Louis, Missouri, United States

Site Status

Countries

Review the countries where the study has at least one active or historical site.

United States

References

Explore related publications, articles, or registry entries linked to this study.

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.

Reference Type DERIVED
PMID: 39261226 (View on PubMed)

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.

Reference Type DERIVED
PMID: 38826471 (View on PubMed)

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.

Reference Type DERIVED
PMID: 37547785 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

202108022

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.