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Perioperative Outcome Risk Assessment With Computer Learning Enhancement

NCT05042804 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 5114

Last updated 2022-11-14

No results posted yet for this study

Summary

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.

Conditions

Interventions

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.

Sponsors & Collaborators

  • Foundation for Anesthesia Education and Research

    collaborator OTHER

  • Washington University School of Medicine

    lead OTHER

Principal Investigators

  • Bradley A Fritz, MD · Washington University School of Medicine

Study Design

Allocation
RANDOMIZED
Purpose
SCREENING
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-09-01
Primary Completion
2022-11-01
Completion
2022-11-01

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05042804 on ClinicalTrials.gov