Realtime Streaming Clinical Use Engine for Medical Escalation

NCT04026555 | Completed Results

• 1 other identifier: GCO 19-0729
• Study Type: interventional
• Target: 2,780
• Locations: 1 country
• Sites: 1

Brief Summary

The escalation of care for patients in a hospitalized setting between nurse practitioner managed services, teaching services, step-down units, and intensive care units is critical for appropriate care for any patient. Often such "triggers" for escalation are initiated based on the nursing evaluation of the patient, followed by physician history and physical exam, then augmented based on laboratory values. These "triggers" can enhance the care of patients without increasing the workload of responder teams. One of the goals in hospital medicine is the earlier identification of patients that require an escalation of care. The study team developed a model through a retrospective analysis of the historical data from the Mount Sinai Data Warehouse (MSDW), which can provide machine learning based triggers for escalation of care (Approved by: IRB-18-00581). This model is called "Medical Early Warning Score ++" (MEWS ++). This IRB seeks to prospectively validate the developed model through a pragmatic clinical trial of using these alerts to trigger an evaluation for appropriateness of escalation of care on two general inpatients wards, one medical and one surgical. These alerts will not change the standard of care. They will simply suggest to the care team that the patient should be further evaluated without specifying a subsequent specific course of action. In other words, these alerts in themselves does not designate any change to the care provider's clinical standard of care. The study team estimates that this study would require the evaluation of \~ 18380 bed movements and approximately 30 months to complete, based on the rate of escalation of care and rate of bed movements in the selected units.

Conditions

Clinical Deterioration; Hospital Medicine; Monitoring, Physiologic

Keywords

Medical Early Warning Systems; Patient Monitoring; Electronic Health Record; Big Data; Critical Care; Machine Learning

Outcome Measures

Primary Outcomes (1)

• Overall Rate of Escalation

  Rate of escalation of care from floor to Stepdown, Telemetry, ICU, per 1,000 patient bed days.

  10 months

Secondary Outcomes (7)

• Number of Participants Requiring Blood Pressure Support

  10 months

• Number of Participants Requiring Respiratory Support

  10 months

• Number of Participants Who Experienced a Cardiac Arrest Episode

  10 months

• Mortality Rate

  Duration of hospital stay, until discharge, regardless of stay length for patients who died in hospital, or 30 days after admission, starting from date of admission, up to 6 weeks.

• Notification Frequency - Number of Alerts Sent Per Day to Providers

  10 months

Study Arms (2)

MEWS++ Monitoring

ACTIVE COMPARATOR

This consists of all the patients that will be receiving MEWS++ escalation monitoring and provider alerting.

Other: MEWS++ Monitoring; Other: Predictor Score

Standard of Care Monitoring

PLACEBO COMPARATOR

Patients in the control arm will have a score calculated but no alert will be sent.

Other: Predictor Score

Interventions

MEWS++ Monitoring OTHER

Patient's electronic medical record data will undergo processing by a machine learning algorithm (MEWS++).

MEWS++ Monitoring

Predictor Score OTHER

A score predicting the likelihood that the patient will experience a deterioration in their clinical condition within six hours will be generated. If the prediction score exceeds a predetermined threshold, an alert will be sent to the provider. The alerting protocol is tiered, with both a low and high threshold. If the score is above the low threshold, nursing will be notified. If the score is above the high threshold, RRT will be notified.

MEWS++ Monitoring; Standard of Care Monitoring

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• All patients age 18 or greater who were admitted to a general care unit selected for each arm.

You may not qualify if:

• Any admitted patient who has a "Do Not Resuscitate (DNR)" and/or a "Do Not Intubate (DNI)" order in the EHR,
• any patient made "level of care" by RRT as documented in REDCap.

Sponsors & Collaborators

• Icahn School of Medicine at Mount Sinai: lead

Study Sites (1)

Mount Sinai Hospital

New York, New York, 10029, United States

Location

Related Publications (1)

• Levin MA, Kia A, Timsina P, Cheng FY, Nguyen KA, Kohli-Seth R, Lin HM, Ouyang Y, Freeman R, Reich DL. Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial. Crit Care Med. 2024 Jul 1;52(7):1007-1020. doi: 10.1097/CCM.0000000000006243. Epub 2024 Feb 21.

  PMID: 38380992 DERIVED

MeSH Terms

Conditions

Clinical Deterioration

Condition Hierarchy (Ancestors)

Disease Progression; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Results Point of Contact

• Title: Dr. Matthew Levin
• Organization: Icahn School of Medicine at Mount Sinai

matthew.levin@mssm.edu

212-241-8382

Study Officials

• Matthew A Levin, MD

  Icahn School of Medicine at Mount Sinai

  STUDY DIRECTOR

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Masking Details: No masking is completed as the information/waiver of consent sheet for the two arms needed to be individualized.
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor, Department of Anesthesiology, Perioperative & Pain Medicine

Model Details: For each patient, real-time data from clinical and administrative systems will be used by ReSCUE-ME to produce a MEWS++ score predicting the likelihood that the patient will require escalation of care within the next 6 hours. Upon the patient being admitted to the unit, the patient will be evaluated based on any update in the EHR. If the prediction score exceeds a "high" threshold, the RRT team will be notified directly. If the score is between a "low" threshold and the high threshold , the nursing team will be notified and increased nursing monitoring will be initiated. If the patient has met criteria for increased nursing monitoring, a refractory 8-hour refractory window will be applied during which no nursing alerts will be sent. However if the score exceeds the high threshold, the RRT team will be notified. Throughout the trial, the performance of the alerts will be monitored via web-based dashboards. If the performance is poor, the "high" and "low" thresholds will be adjusted.

Study Record Dates

• First Submitted: July 16, 2019
• First Posted: July 19, 2019
• Study Start: June 18, 2019
• Primary Completion: March 19, 2020
• Study Completion: March 19, 2020
• Last Updated: January 14, 2025
• Results First Posted: January 14, 2025

Record last verified: 2025-01

Locations

US (1)