MoralityORACLENCT05042804
Perioperative Outcome Risk Assessment With Computer Learning Enhancement
A study testing Machine learning models predicting postoperative death and acute kidney injury in people with Morality and Acute Kidney Injury, run by Washington University School of Medicine at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Morality, Acute Kidney Injury
- What is being tested?
- Machine learning models predicting postoperative death and acute kidney injury
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 5,114 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Sep 2021to 1 Nov 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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)
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
United States1 site
Washington University School of Medicine
St Louis, Missouri, 63110
About this study
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.
Read the 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.
- Conditions
- Morality, Acute Kidney Injury
- Being tested
- Machine learning models predicting postoperative death and acute kidney injury
- Medical subject headings
- Acute Kidney Injury
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning models predicting postoperative death and acute kidney injuryOther
The machine learning display uses data from the electronic health record to predict the likelihood of postoperative death and postoperative acute kidney injury.
| Study group | Role | What they receive |
|---|---|---|
| Machine Learning Assistance | Experimental | 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. Receives: Other: Machine learning models predicting postoperative death and acute kidney injury |
| No Assistance | No intervention | 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 7 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2021
- Study start1 Sep 2021
- Primary completion1 Nov 2022
- Study completion1 Nov 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for MoralityStudies of the same condition, open and under way.
- More trials run by Washington University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 5,114 participants (actual)
Sponsor and investigators
- Lead sponsor
- Washington University School of Medicine
- Sponsor type
- University, hospital or other
- Collaborators
- Foundation for Anesthesia Education and Research
- Responsible party
- Principal investigator, Bradley Fritz, Washington University School of Medicine
- Investigators
- Bradley A Fritz, MD, Principal investigator, Washington University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05042804
- Sponsor's protocol ID
- 202108022
- Acronym
- ORACLE
- Status last verified
- Nov 2022
- First submitted
- 1 Sep 2021
- Last updated
- 14 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 39261226Fritz 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.
PubMed 38826471Fritz 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.
PubMed 37547785
Study at a glance
From the registry record, last updated by the sponsor on 14 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Perioperative Outcome Risk Assessment With Computer Learning Enhancement
- ClinicalTrials.gov ID
- NCT05042804
- Acronym / sponsor ID
- ORACLE / 202108022
- Conditions
- Morality, Acute Kidney Injury
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 5,114 participants (actual)
- Lead sponsor
- Washington University School of MedicineUniversity, hospital or other
- Collaborators
- Foundation for Anesthesia Education and Research
- Study start
- 1 Sep 2021 (actual)
- Primary completion
- 1 Nov 2022 (actual)
- Study completion
- 1 Nov 2022 (actual)
- First posted
- 13 Sep 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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 design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Machine Learning Assistance | Experimental | Other: Machine learning models predicting postoperative death and acute kidney injuryClinicians 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. |
| No Assistance | No intervention | –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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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)
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning models predicting postoperative death and acute kidney injuryThe machine learning display uses data from the electronic health record to predict the likelihood of postoperative death and postoperative acute kidney injury. | Other | Machine Learning Assistance |
Primary outcome
Area under receiver-operating characteristic curve of clinician prediction for postoperative death
Time frame: 30 days
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.
Area under receiver-operating characteristic curve of clinician prediction for postoperative acute kidney injury
Time frame: 7 days
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Nov 2022.
- Derived from this study: PubMed 39261226Fritz 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.
- Derived from this study: PubMed 38826471Fritz 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.
- Derived from this study: PubMed 37547785Fritz 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Bradley A Fritz, MD principal investigator
Washington University School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine learning models predicting postoperative death and acute kidney injury in people with Morality and Acute Kidney Injury, run by Washington University School of Medicine at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 14 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under receiver-operating characteristic curve of clinician prediction for postoperative death (time frame: 30 days). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05042804, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Nov 2022.