Prediction of Patient Deterioration Using Machine Learning

March 16, 2026 updated by: David Levine, Brigham and Women's Hospital
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration throughout a patient's admission. This algorithm was then validated in a validation cohort.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

526

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Massachusetts
      • Boston, Massachusetts, United States, 02115
        • Brigham and Women's Hospital
      • Boston, Massachusetts, United States, 02130
        • Brigham and Women's Faulkner Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and geographic residence requirements and are enrolled in home hospital.

Description

Inclusion Criteria:

Cared for in the Brigham and Women's Home Hospital study

Exclusion Criteria:

Incomplete continuous monitoring data

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training
A subset of patients that are used to train the machine learning algorithm.
Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2
We will retrospectively compare the alarms produced from traditional vital sign alarms (thresholds set by clinicians) versus the BioVitals Index vs the National Early Warning Score 2
Validation
A subset of patients that are "held back" and used to validate the algorithm's accuracy.
Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2
We will retrospectively compare the alarms produced from traditional vital sign alarms (thresholds set by clinicians) versus the BioVitals Index vs the National Early Warning Score 2

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Alarm burden
Time Frame: From admission to discharge, measured in hours, on average 5 days
The number of alarms fired per patient per hour
From admission to discharge, measured in hours, on average 5 days

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity for recognition of a safety composite
Time Frame: From admission to discharge, on average 5 days
The sensitivity (true positives divided by condition positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
From admission to discharge, on average 5 days
Specificity for recognition of a safety composite
Time Frame: From admission to discharge, on average 5 days
The specificity (true negatives divided by condition negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
From admission to discharge, on average 5 days
Positive predictive value for recognition of a safety composite
Time Frame: From admission to discharge, on average 5 days
The positive predictive value (true positives divided by the sum of true positives plus false positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
From admission to discharge, on average 5 days
Negative predictive value for recognition of a safety composite
Time Frame: From admission to discharge, on average 5 days
The negative predictive value (true negatives divided by the sum of true negatives plus false negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
From admission to discharge, on average 5 days
Rate of alarms with clinical utility
Time Frame: From admission to discharge, on average 5 days
We will use general estimating equations (GEE) with three outcomes per patient (the number of clinically important alarms for BioVitals, NEWS2, and traditional vital signs); the GEE will account for the clustering between the three outcomes on a patient. The GEE will use a negative binomial marginal model with a log-link for the number of alarms with clinical utility and an offset for log length-of stay (in hours); with this model, we model the rate per hour of number of alarms with clinical utility with BI, NEWS2, and traditional vital signs. The main covariate in the negative binomial model will be a three-level covariate for method: BI vs NEWS2 vs traditional vital signs, and the exponential of the effect of this covariate will be a pair-wise rate ratio for BI vs NEWS2 vs traditional vital signs.
From admission to discharge, on average 5 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Brigham and Women's Hospital

Collaborators

Biofourmis Inc.

Investigators

  • Principal Investigator: David Levine, MD MPH MA, Associate Physician

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 20, 2021

Primary Completion (Actual)

March 20, 2025

Study Completion (Actual)

February 16, 2026

Study Registration Dates

First Submitted

April 14, 2021

First Submitted That Met QC Criteria

September 15, 2021

First Posted (Actual)

September 16, 2021

Study Record Updates

Last Update Posted (Actual)

March 17, 2026

Last Update Submitted That Met QC Criteria

March 16, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2017P002583d

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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