Telemedicine Notifications With Machine Learning for Postoperative Care

NCT ID: NCT03974828

Last Updated: 2025-12-19

Basic Information

• Recruitment Status: WITHDRAWN
• Clinical Phase: NA
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-07-01
• Study Completion Date: 2028-07-01

Brief 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

Keywords

Telemedicine; Anesthesia Control Tower; Machine Learning; Forecasting Algorithms; Randomized Controlled Trial; PACU

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: DOUBLE

Study Groups

Non-Contact

Participants in the non-contact group will be monitored by anesthesia control tower clinicians who will utilize AlertWatch and integrating machine-learning forecasting algorithms for adverse outcomes predictions, but who will not contact the postoperative provider unless it is clinically necessary for patient safety purposes.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Brief contact

PACU and ward providers caring for participants in the brief contact group will be notified by Anesthesia Control Tower clinicians before arrival if the patient's forecast for mortality is in the top 15% of historical PACU patients. The notification will contain a brief summary of the patient's forecast risk of major adverse events.

Group Type: EXPERIMENTAL

Anesthesia Control Tower Notification

Intervention Type: DEVICE

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.

Full contact

PACU and ward providers caring for participants in the full contact group will be notified by Anesthesia Control Tower clinicians before arrival if the patient's forecast for mortality is in the top 15% of historical PACU patients. The notification will contain a report card of the patient's forecast risk of major adverse events, explanatory machine-learning outputs, most influential pre- and intraoperative data, and predicted treatments.

Group Type: EXPERIMENTAL

Anesthesia Control Tower Notification

Intervention Type: DEVICE

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.

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Enrolled in TECTONICS Study (ID 201903026, NCT03923699), in OR randomized to contact
• workweek hours
• preoperative assessment completed
• estimated risk of mortality in top 15% of historical PACU patients

Exclusion Criteria

• Not enrolled in TECTONICS Study
• Operating room randomized to non-contact in TECTONICS
• Planned ICU admission

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Washington University School of Medicine

OTHER

Sponsor Role: lead

Responsible Party

Christopher King

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Christopher R King, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Washington University School of Medicine

Other Identifiers

201905127

Identifier Type: -

Identifier Source: org_study_id