Prediction of Expected Length of Hospital Stay Using Machine Learning

Status

Withdrawn

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04784351

Enrollment

Registered

2021-03-05

Start date

2021-03-20

Completion date

2026-12-01

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 length of stay 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
Length of Stay	From date of admission to date of discharge (1 to 24 days)	The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours

Countries

United States

Contacts

PRINCIPAL_INVESTIGATORDavid Levine, MD MPH MA

Associate Physician

Outcome results