Trials / Withdrawn
WithdrawnNCT03974828
Telemedicine Notifications With Machine Learning for Postoperative Care
- Status
- Withdrawn
- Phase
- N/A
- Study type
- Interventional
- Enrollment
- 0 (actual)
- Sponsor
- Washington University School of Medicine · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
Summary
The ODIN-Report study will be a randomized controlled trial of the effect of providing machine learning risk forecasts to providers caring for patients immediately after surgery on serious complications. The complications studied will be ICU admission or death on wards, acute kidney injury, and hospital length of stay.
Detailed description
This will be a single center, randomized, controlled, pragmatic clinical trial. The investigators will screen surgical patients enrolled in TECTONICS (NCT03923699) and randomized to intraoperative contact. Near the end of the operation, the investigators will calculate the same machine learning risk forecasts of major complications as TECTONICS, and enroll patients if all of the following are true: (1) No ICU admission is intended (2) ML mortality risk forecast is in top 15% of historical PACU patients. Patients will be randomized 1:1:1 to no contact, brief contact, and full contact. The postoperative provider (PACU physician, anesthesiologist, ward clinician) will be notified before arrival of the risk forecast in the contact groups, and in the full contact group an additional set of explanatory ML outputs will be provided. The intention-to-treat principle will be followed for all analyses.
Conditions
- Surgery--Complications
- Perioperative/Postoperative Complications
- Acute Kidney Injury
- Hospital Mortality
Interventions
| Type | Name | Description |
|---|---|---|
| DEVICE | Anesthesia Control Tower Notification | Real-time data will be monitored through the AlertWatch system as well as the electronic health record. Risk forecasts of adverse events (30 day mortality, acute kidney injury, postoperative delirium, respiratory failure), PACU length of stay, and hospital length of stay will be generated by a machine learning algorithm. Additional outputs identifying the most important predictors and their effects will be combined with risk forecasts to form a report card. |
Timeline
- Start date
- 2025-07-01
- Primary completion
- 2028-07-01
- Completion
- 2028-07-01
- First posted
- 2019-06-05
- Last updated
- 2025-12-19
Regulatory
- FDA-regulated device study
Source: ClinicalTrials.gov record NCT03974828. Inclusion in this directory is not an endorsement.