Telemedicine Notifications With Machine Learning for Postoperative Care
Last updated on July 2021Recruitment
- Recruitment Status
- Not yet recruiting
- Estimated Enrollment
- 10000
Summary
- Conditions
- Acute Kidney Injury
- Hospital Mortality
- Perioperative/Postoperative Complications
- Surgery- Complications
- Type
- Interventional
- Phase
- Not Applicable
- Design
- Allocation: RandomizedIntervention Model: Parallel AssignmentIntervention Model Description: 1:1:1 randomization between standard of care (no contact), postoperative contact (brief), postoperative contact (long).Masking: Double (Participant, Outcomes Assessor)Primary Purpose: Other
Participation Requirements
- Age
- Between 18 years and 125 years
- Gender
- Both males and females
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...
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.
Tracking Information
- NCT #
- NCT03974828
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Christopher R King, MD, PhD Washington University School of Medicine
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