Prediction of Patient Deterioration Using Machine Learning

Completed

Conditions studied: Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases, Hypertensive Urgency, Atrial Fibrillation Rapid, Anticoagulants; Increased

In brief

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.

Key facts

Study ID

NCT05045742

Run by

Brigham and Women's Hospital

People needed

526

Starts

2021-03-20

Expected to finish

2026-02-16

Last updated by the study team

2026-03-17

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

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

You may not qualify if…

• Incomplete continuous monitoring data

Where it is running

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

Full record on ClinicalTrials.gov

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