Prediction of Expected Length of Hospital Stay Using Machine Learning

NCT04784351 | Withdrawn

• 1 other identifier: 2017P002583b
• Study Type: observational
• Target: N/A
• 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 length of stay throughout a patient's admission. This algorithm was then validated in a validation cohort.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• Length of Stay

  The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours

  From date of admission to date of discharge (1 to 24 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 (11)

• Lubelski D, Ehresman J, Feghali J, Tanenbaum J, Bydon A, Theodore N, Witham T, Sciubba DM. Prediction calculator for nonroutine discharge and length of stay after spine surgery. Spine J. 2020 Jul;20(7):1154-1158. doi: 10.1016/j.spinee.2020.02.022. Epub 2020 Mar 13.

  PMID: 32179154 BACKGROUND

• Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET, Ramkumar PN. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J Shoulder Elbow Surg. 2020 Nov;29(11):2385-2394. doi: 10.1016/j.jse.2020.04.009. Epub 2020 Jun 9.

  PMID: 32713541 BACKGROUND

• Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE. Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models. J Arthroplasty. 2019 Apr;34(4):632-637. doi: 10.1016/j.arth.2018.12.030. Epub 2018 Dec 27.

  PMID: 30665831 BACKGROUND

• Ma X, Si Y, Wang Z, Wang Y. Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Programs Biomed. 2020 Apr;186:105224. doi: 10.1016/j.cmpb.2019.105224. Epub 2019 Nov 20.

  PMID: 31765937 BACKGROUND

• Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. Int J Cardiol. 2019 Aug 1;288:140-147. doi: 10.1016/j.ijcard.2019.01.046. Epub 2019 Jan 19.

  PMID: 30685103 BACKGROUND

• Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning: A derivation study. J Clin Neurosci. 2020 Sep;79:100-103. doi: 10.1016/j.jocn.2020.07.046. Epub 2020 Aug 5.

  PMID: 33070874 BACKGROUND

• Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, Ramkumar PN. Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. J Arthroplasty. 2018 Dec;33(12):3617-3623. doi: 10.1016/j.arth.2018.08.028. Epub 2018 Sep 5.

  PMID: 30243882 BACKGROUND

• Young AJ, Hare A, Subramanian M, Weaver JL, Kaufman E, Sims C. Using Machine Learning to Make Predictions in Patients Who Fall. J Surg Res. 2021 Jan;257:118-127. doi: 10.1016/j.jss.2020.07.047. Epub 2020 Aug 18.

  PMID: 32823009 BACKGROUND

• Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4.

  PMID: 32376173 BACKGROUND

• 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

• Nemati M, Ansary J, Nemati N. Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns (N Y). 2020 Aug 14;1(5):100074. doi: 10.1016/j.patter.2020.100074. Epub 2020 Jul 4.

  PMID: 32835314 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: March 2, 2021
• First Posted: March 5, 2021
• Study Start: March 20, 2021
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: March 17, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (2)