Prediction of 30-Day Readmission Using Machine Learning

NCT04849312 | Completed

• 1 other identifier: 2017P002583a
• Study Type: observational
• Target: 372
• Locations: 1 country
• Sites: 2

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.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• 30-Day Readmission [ yes / no ]

  Unplanned hospital admission within 30 days of having been discharged

  From date of admission to 30-days post-discharge (31 to 54 days)

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

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

Contact the study team to discuss eligibility requirements. They can help determine if this study is right for you.

Sponsors & Collaborators

• Brigham and Women's Hospital: lead
• Biofourmis Inc.: collaborator

Study Sites (2)

Brigham and Women's Hospital

Boston, Massachusetts, 02115, United States

Location

Brigham and Women's Faulkner Hospital

Boston, Massachusetts, 02130, United States

Location

Related Publications (7)

• Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A. Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures. J Foot Ankle Surg. 2019 May;58(3):410-416. doi: 10.1053/j.jfas.2018.09.004. Epub 2019 Feb 23.

  PMID: 30803914 BACKGROUND

• Li Q, Yao X, Echevin D. How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data. Value Health. 2020 Oct;23(10):1307-1315. doi: 10.1016/j.jval.2020.06.009. Epub 2020 Sep 7.

  PMID: 33032774 BACKGROUND

• Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019 Apr;95:27-37. doi: 10.1016/j.artmed.2018.08.004. Epub 2018 Sep 10.

  PMID: 30213670 BACKGROUND

• Morel D, Yu KC, Liu-Ferrara A, Caceres-Suriel AJ, Kurtz SG, Tabak YP. Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach. Int J Med Inform. 2020 Jul;139:104136. doi: 10.1016/j.ijmedinf.2020.104136. Epub 2020 Apr 18.

  PMID: 32353752 BACKGROUND

• Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med. 2020 Mar;118:103636. doi: 10.1016/j.compbiomed.2020.103636. Epub 2020 Feb 1.

  PMID: 32174313 BACKGROUND

• Bolourani S, Tayebi MA, Diao L, Wang P, Patel V, Manetta F, Lee PC. Using machine learning to predict early readmission following esophagectomy. J Thorac Cardiovasc Surg. 2021 Jun;161(6):1926-1939.e8. doi: 10.1016/j.jtcvs.2020.04.172. Epub 2020 May 29.

  PMID: 32711985 BACKGROUND

• Arvind V, London DA, Cirino C, Keswani A, Cagle PJ. Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty. J Shoulder Elbow Surg. 2021 Feb;30(2):e50-e59. doi: 10.1016/j.jse.2020.05.013. Epub 2020 Jun 9.

  PMID: 32868011 BACKGROUND

MeSH Terms

Conditions

Infections; Heart Failure; Pulmonary Disease, Chronic Obstructive; Asthma; Renal Insufficiency, Chronic; Hypertensive Crisis

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Bronchial Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Hypertension; Vascular Diseases

Study Officials

• David Levine, MD MPH MA

  Associate Physician

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Attending Physician

Study Record Dates

• First Submitted: April 14, 2021
• First Posted: April 19, 2021
• Study Start: June 1, 2017
• Primary Completion: October 31, 2019
• Study Completion: November 30, 2019
• Last Updated: March 17, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (2)