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Telemedicine Notifications With Machine Learning for Postoperative Care (ODIN-Report)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03974828
Recruitment Status : Not yet recruiting
First Posted : June 5, 2019
Last Update Posted : May 7, 2020
Sponsor:
Information provided by (Responsible Party):
Christopher King, Washington University School of Medicine

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.

Condition or disease Intervention/treatment Phase
Surgery--Complications Perioperative/Postoperative Complications Acute Kidney Injury Hospital Mortality Device: Anesthesia Control Tower Notification Not Applicable

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.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 3375 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Intervention 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
Official Title: Telemedicine Notifications With Machine Learning for Postoperative Care
Estimated Study Start Date : September 1, 2020
Estimated Primary Completion Date : January 1, 2024
Estimated Study Completion Date : January 1, 2024

Resource links provided by the National Library of Medicine


Arm Intervention/treatment
No Intervention: 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.
Experimental: 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.
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.

Experimental: 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.
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.




Primary Outcome Measures :
  1. Unplanned ICU admission [ Time Frame: 7 days post-op ]
    Admission to a "critical care" bed regardless of rationale or duration at any point in the follow up time frame. Patients who expire without transfer to ICU will be marked as positive.


Secondary Outcome Measures :
  1. Acute Kidney Injury [ Time Frame: 7 days post-op ]
    Postoperative laboratory values and urine output will be used to calculate Kidney Disease Improving Global Outcomes grades of acute kidney injury. Where unavailable, baseline Glomerular filtration rate will be assumed to be age, sex, and body size normal.

  2. Hospital length of stay [ Time Frame: 30 days post-op ]
    The duration in days between end of anesthesia care and discharge from the performing hospital.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03974828


Contacts
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Contact: Sherry McKinnon, BS 314-221-7764 smckinnon@wustl.edu

Sponsors and Collaborators
Washington University School of Medicine
Investigators
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Principal Investigator: Christopher R King, MD, PhD Washington University School of Medicine
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Responsible Party: Christopher King, Principal Investigator, Washington University School of Medicine
ClinicalTrials.gov Identifier: NCT03974828    
Other Study ID Numbers: 201905127
First Posted: June 5, 2019    Key Record Dates
Last Update Posted: May 7, 2020
Last Verified: May 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: Data are a subset of TECTONICS and will be have the same sharing plan / restrictions.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Product Manufactured in and Exported from the U.S.: Yes
Keywords provided by Christopher King, Washington University School of Medicine:
Telemedicine
Anesthesia Control Tower
Machine Learning
Forecasting Algorithms
Randomized Controlled Trial
PACU
Additional relevant MeSH terms:
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Acute Kidney Injury
Postoperative Complications
Renal Insufficiency
Kidney Diseases
Urologic Diseases
Pathologic Processes
Anesthetics
Central Nervous System Depressants
Physiological Effects of Drugs