Prediction of 30-Day Readmission Using Machine Learning
Sponsor
Brigham and Women's Hospital (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT04849312
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 the likelihood of 30-day readmission 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 30-Day Readmission Using Machine Learning
Anticipated Study Start Date
:
Mar 20, 2022
Anticipated Primary Completion Date
:
Dec 1, 2022
Anticipated Study Completion Date
:
Dec 1, 2022
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
- 30-Day Readmission [ yes / no ] [From date of admission to 30-days post-discharge (31 to 54 days)]
Unplanned hospital admission within 30 days of having been discharged
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
- 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.
- 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. Erratum in: J Thorac Cardiovasc Surg. 2020 Oct 17;:.
- Li Q, Yao X, Échevin 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.
- 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.
- 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.
- 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.
- 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.
Responsible Party:
David Levine,
Attending Physician,
Brigham and Women's Hospital
ClinicalTrials.gov Identifier:
NCT04849312
Other Study ID Numbers:
- 2017P002583a
First Posted:
Apr 19, 2021
Last Update Posted:
Feb 7, 2022
Last Verified:
Feb 1, 2022
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: