Prediction of Expected Length of Hospital Stay Using Machine Learning
Sponsor
Brigham and Women's Hospital (Other)
Overall Status
Active, not recruiting
CT.gov ID
NCT04784351
Collaborator
Biofourmis Inc. (Industry)
500
2
8.4
250
29.7
Study Details
Study Description
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.
Condition or Disease | Intervention/Treatment | Phase |
---|---|---|
|
Study Design
Study Type:
Observational
Anticipated Enrollment
:
500 participants
Observational Model:
Cohort
Time Perspective:
Retrospective
Official Title:
Prediction of Expected Length of Hospital Stay Using Machine Learning
Actual Study Start Date
:
Mar 20, 2021
Anticipated Primary Completion Date
:
Aug 1, 2021
Anticipated Study Completion Date
:
Dec 1, 2021
Arms and Interventions
Arm | Intervention/Treatment |
---|---|
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. |
Outcome Measures
Primary Outcome Measures
- 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
Eligibility Criteria
Criteria
Ages Eligible for Study:
18 Years
and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
---|---|---|---|---|---|
1 | Brigham and Women's Hospital | Boston | Massachusetts | United States | 02115 |
2 | Brigham and Women's Faulkner Hospital | Boston | Massachusetts | United States | 02130 |
Sponsors and Collaborators
- Brigham and Women's Hospital
- Biofourmis Inc.
Investigators
- Principal Investigator: David Levine, MD MPH MA, Associate Physician
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Responsible Party:
David Levine,
Attending Physician,
Brigham and Women's Hospital
ClinicalTrials.gov Identifier:
NCT04784351
Other Study ID Numbers:
- 2017P002583b
First Posted:
Mar 5, 2021
Last Update Posted:
Apr 19, 2021
Last Verified:
Apr 1, 2021
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Additional relevant MeSH terms: