Trials / Completed
CompletedNCT05045742
Prediction of Patient Deterioration Using Machine Learning
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 526 (actual)
- Sponsor
- Brigham and Women's Hospital · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
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
- Infection
- Heart Failure
- Chronic Obstructive Pulmonary Disease
- Asthma
- Gout Flare
- Chronic Kidney Diseases
- Hypertensive Urgency
- Atrial Fibrillation Rapid
- Anticoagulants; Increased
Interventions
| Type | Name | Description |
|---|---|---|
| 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 |
Timeline
- Start date
- 2021-03-20
- Primary completion
- 2025-03-20
- Completion
- 2026-02-16
- First posted
- 2021-09-16
- Last updated
- 2026-03-17
Locations
2 sites across 1 country: United States
Source: ClinicalTrials.gov record NCT05045742. Inclusion in this directory is not an endorsement.