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Prediction of Patient Deterioration Using Machine Learning

NCT05045742 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 526

Last updated 2026-03-17

No results posted yet for this study

Summary

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.

Conditions

Interventions

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

Sponsors & Collaborators

  • Biofourmis Inc.

    collaborator INDUSTRY

  • Brigham and Women's Hospital

    lead OTHER

Principal Investigators

  • David Levine, MD MPH MA · Associate Physician

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-03-20
Primary Completion
2025-03-20
Completion
2026-02-16

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05045742 on ClinicalTrials.gov