Prediction of Expected Length of Hospital Stay Using Machine Learning

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

• Registration Number: NCT04784351
• 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 length of stay throughout a patient's admission. This algorithm was then validated in a validation cohort.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2021-03-02
• Study First Submitted QC: 2021-03-02
• Study First Posted: 2021-03-05
• Study Start: 2021-03-20
• Status Verified: 2021-04
• Last Update Submitted: 2021-04-14
• Last Update Posted: 2021-04-19
• Primary Completion: 2021-08-01
• Study Completion: 2021-12-01

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
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

Trial Locations

Locations (2)

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

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