Prediction of 30-Day Readmission Using Machine Learning
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Brigham and Women's Hospital
- Enrollment
- 372
- Locations
- 2
- Primary Endpoint
- 30-Day Readmission [ yes / no ]
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
Exclusion Criteria
- Not provided
Arms & Interventions
Training
A subset of patients that are used to train the machine learning algorithm.
Validation
A subset of patients that are "held back" and used to validate the algorithm's accuracy.
Outcomes
Primary Outcomes
30-Day Readmission [ yes / no ]
Time Frame: From date of admission to 30-days post-discharge (31 to 54 days)
Unplanned hospital admission within 30 days of having been discharged
Secondary Outcomes
No secondary outcomes reported
Investigators
David Levine
Attending Physician
Brigham and Women's Hospital