Clinical trial NCT04849312: Prediction of 30-Day Readmission Using Machine Learning
Official title: Prediction of 30-Day Readmission Using Machine Learning
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Infection
Heart Failure
Chronic Obstructive Pulmonary Disease
Asthma
Gout Flare
Chronic Kidney Diseases
Hypertensive Urgency
Atrial Fibrillation Rapid
Anticoagulants; Increased
Lead sponsor: Brigham and Women's Hospital
Sponsor class: OTHER
Collaborators: name: Biofourmis Inc.
First posted: 2021-04-19
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home 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.


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

Primary outcomes:
measure: 30-Day Readmission [ yes / no ]
timeFrame: From date of admission to 30-days post-discharge (31 to 54 days)
Secondary outcomes:
Other outcomes:
Locations:
city: Boston
country: United States
facility: Brigham and Women's Hospital
state: Massachusetts
city: Boston
country: United States
facility: Brigham and Women's Faulkner Hospital
state: Massachusetts
Study officials:
affiliation: Associate Physician
name: David Levine, MD MPH MA
role: PRINCIPAL_INVESTIGATOR
References:
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