Realtime Streaming Clinical Use Engine for Medical Escalation (ReSCUE-ME)

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.

Study Overview

• Status: Completed
• Conditions: Monitoring, Physiologic; Clinical Deterioration; Hospital Medicine
• Intervention / Treatment: Other: MEWS++ Monitoring; Other: Predictor Score

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: 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.

A silent pilot of the ReSCUE-ME alerts has been running on 10E and 10W since Feb 2019. The study team has continuously monitoring the alert performance via a real-time web-based dashboard. The results are summarized below:

• Median # of alerts to primary team, per floor, per day: 8
• Median # of alerts to RRT, per floor, per day: 4
• Sensitivity 0.76, Specificity 0.68, AUC 0.77
• Accuracy 0.69, Precision 0.3, F1 Score 0.43 This performance compares very favorably to the performance seen in the retrospective historical cohort used to develop the MEWS++ model:
• Sensitivity 0.82, Specificity 0.76, AUC 0.85
• Accuracy 0.76, Precision 0.12, F1 Score 0.19"

• Study Type: Interventional
• Enrollment (Actual): 2915
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • New York
    • New York, New York, United States, 10029
      • Mount Sinai Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: PREVENTION
• Allocation: NON_RANDOMIZED
• Interventional Model: PARALLEL
• Masking: NONE

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
ACTIVE_COMPARATOR: MEWS++ Monitoring; This consists of all the patients that will be receiving MEWS++ escalation monitoring and provider alerting.	Other: MEWS++ Monitoring; Patient's electronic medical record data will undergo processing by a machine learning algorithm (MEWS++).; Other: Predictor Score; 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.
PLACEBO_COMPARATOR: Standard of Care Monitoring; Patients in the control arm will have a score calculated but no alert will be sent.	Other: Predictor Score; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Overall rate of care escalation	The composite (sum) of the rate of escalation of care (from floor to Stepdown, Telemetry, ICU) and rate of RRT initiated therapy (including but not limited to blood pressure support, respiratory care support, anti-biotic augmentation, invasive monitoring).	30 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Number of participants requiring blood pressure support	Number of participants requiring blood pressure support agents such as initiation of vasopressor medication or administration of fluid bolus.	30 month
Number of participants requiring respiratory support	Number of participants requiring respiratory support intervention such as initiation of nasal cannula to high flow or frequency of intubation	30 month
Number of cardiac arrest episode	Frequency of cardiac arrest episode	30 month
Mortality Rate	Number of Mortalities	30 month
Notification Frequency	The average notifications per day per patient	30 month
Number of calls	The average number of calls per patient	30 month
Sensitivity and Specificity of the RRT alert	The performance of the alert will be evaluated by calculating the sensitivity, specificity, positive predictive value, negative predictive value, precision, recall, and F1-score. This will be done both for the overall escalation rate and if possible for individual escalations (ICU, step-down, telemetry) and death.	30 month

Collaborators and Investigators

• Sponsor: Icahn School of Medicine at Mount Sinai
• Investigators: Study Director: Matthew A Levin, MD, Icahn School of Medicine at Mount Sinai

Publications and helpful links

Helpful Links

• The public website for information regarding the ReSCUE-ME trial.

Study record dates

Study Major Dates

• Study Start (ACTUAL): June 18, 2019
• Primary Completion (ACTUAL): March 19, 2020
• Study Completion (ACTUAL): March 19, 2020

Study Registration Dates

• First Submitted: July 16, 2019
• First Submitted That Met QC Criteria: July 18, 2019
• First Posted (ACTUAL): July 19, 2019

Study Record Updates

• Last Update Posted (ACTUAL): June 25, 2020
• Last Update Submitted That Met QC Criteria: June 24, 2020
• Last Verified: June 1, 2020

More Information

Terms related to this study

• Keywords: Critical Care; Machine Learning; Electronic Health Record; Patient Monitoring; Big Data; Medical Early Warning Systems
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Disease Progression; Clinical Deterioration
• Other Study ID Numbers: GCO 19-0729

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: Individual participant data that underlie the results reported in this article, after deidentification (text, tables, figures, and appendices).
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No