Prediction of 30-Day Readmission Using Machine Learning

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04849312

Enrollment

372

Registered

2021-04-19

Start date

2017-06-01

Completion date

2019-11-30

Last updated

2026-03-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.

Design outcomes

Primary

Measure	Time frame	Description
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

Countries

United States

Contacts

PRINCIPAL_INVESTIGATORDavid Levine, MD MPH MA

Associate Physician

Outcome results