Prediction of 30-Day Readmission Using Machine Learning

Brigham and Women's Hospital500 enrolled

Overview

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 the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.

Study Type

OBSERVATIONAL

Enrollment

500

Conditions

Infection Heart Failure Chronic Obstructive Pulmonary Disease Asthma Gout Flare Chronic Kidney Diseases Hypertensive Urgency Atrial Fibrillation Rapid

Locations (2)

Brigham and Women's Hospital

Boston, Massachusetts, United States

Brigham and Women's Faulkner Hospital

Boston, Massachusetts, United States

Outcomes

Primary Outcomes

30-Day Readmission [ yes / no ]

Unplanned hospital admission within 30 days of having been discharged

Time frame: From date of admission to 30-days post-discharge (31 to 54 days)

Data from ClinicalTrials.gov

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