Realtime Streaming Clinical Use Engine for Medical Escalation
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Enrollment
- 2,780
- Locations
- 1
- Primary Endpoint
- Overall Rate of Escalation
Study Overview
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.
Detailed Description
Objectives:
Mount Sinai Hospital has developed a Rapid Response Team (RRT) system designed to give general floor care providers additional support for patients who may be requiring a higher level of care. This system enables both nurses and physicians to notify the RRT and have a critical care team evaluate the patients. During the period of 03/01/2018 to 09/17/2018, Mount Sinai Hospital floor units on 10W and 10E units made 357 rapid response team (RRT) calls with only 58 leading to an actual increase in the level of care (true positive rate ~ 16%). Similarly, the Electronic Health Record (EHR) generated 839 sepsis Best Practice Alerts (BPAs) yet only five led to escalations in care (true positive rate ~ 0.5%). The results above would imply that over 168 evaluations need to be made to identify a single case where the patient required an escalation in care. The goal of ReSCUE-ME is to evaluate prospective model performance and identify the best spot which the study team can incorporate MEWS++ into RRT and Primary providers workflow. The primary endpoint is rate of escalation of care on 10W and 10E during the study period.
Background:
In a prior study, the group has demonstrated that a machine learning model (MEWS++) significantly outperformed a standard, manually calculated MEWS score on a large retrospective cohort of hospitalized patients. To develop this model, the study team used a data set (Approved by the Program for Protection of Human Subjects Institutional Review Board (IRB) IRB-18-00581) of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements. The study team found that MEWS++ was superior to the standard MEWS model with a sensitivity of 81.6% vs. 44.6%, specificity of 75.5% vs. 64.5%, and area under the receiver operating curve of 0.85 vs. 0.71.
Encouraged by this prior result, the study team is seeking to evaluate the model in a prospective study.
Study Design
- Study Type
- Interventional
- Allocation
- Non Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Prevention
- Masking
- None
Masking Description
No masking is completed as the information/waiver of consent sheet for the two arms needed to be individualized.
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •All patients age 18 or greater who were admitted to a general care unit selected for each arm.
Exclusion Criteria
- •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.
Outcomes
Primary Outcomes
Overall Rate of Escalation
Time Frame: 10 months
Rate of escalation of care from floor to Stepdown, Telemetry, ICU, per 1,000 patient bed days.
Secondary Outcomes
- Number of Participants Who Experienced a Cardiac Arrest Episode(10 months)
- Number of Participants Requiring Blood Pressure Support(10 months)
- Number of Participants Requiring Respiratory Support(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)
- Number of Calls(10 months)
- Sensitivity and Specificity of the RRT Alert(10 months)
Investigators
Matthew Levin
Associate Professor, Department of Anesthesiology, Perioperative & Pain Medicine
Icahn School of Medicine at Mount Sinai