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| Name | Class |
|---|---|
| Foundation for Anesthesia Education and Research | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine learning models predicting postoperative death and acute kidney injury | Other | The machine learning display uses data from the electronic health record to predict the likelihood of postoperative death and postoperative acute kidney injury. |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Bradley A Fritz, MD | Washington University School of Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Washington University School of Medicine | St Louis | Missouri | 63110 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39261226 | Derived | 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. | |
| 38826471 |
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| ID | Term |
|---|---|
| D058186 | Acute Kidney Injury |
| ID | Term |
|---|---|
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
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| Derived |
| 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. |
| 37547785 | Derived | 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. |
| D005261 |
| Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |