Prediction of 30-Day Readmission Using Machine Learning

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

• Registration Number: NCT04849312
• Lead Sponsor: Brigham and Women's Hospital

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

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-04-14
• Study First Submitted QC: 2021-04-14
• Study First Posted: 2021-04-19
• Status Verified: 2022-02
• Last Update Submitted: 2022-02-03
• Last Update Posted: 2022-02-07
• Study Start: 2022-03-20
• Primary Completion: 2022-12-01
• Study Completion: 2022-12-01

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 500

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
30-Day Readmission [ yes / no ]	From date of admission to 30-days post-discharge (31 to 54 days)	Unplanned hospital admission within 30 days of having been discharged

Trial Locations

Locations (2)

Brigham and Women's Hospital; 🇺🇸 Boston, Massachusetts, United States

Brigham and Women's Faulkner Hospital; 🇺🇸 Boston, Massachusetts, United States