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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT04849312
Other study ID # 2017P002583a
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date March 20, 2022
Est. completion date December 1, 2022

Study information

Verified date February 2022
Source Brigham and Women's Hospital
Contact David Levine, MD MPH MA
Phone 617 732 7063
Email dmlevine@partners.org
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 500
Est. completion date December 1, 2022
Est. primary completion date December 1, 2022
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.

Study Design


Locations

Country Name City State
United States Brigham and Women's Faulkner Hospital Boston Massachusetts
United States Brigham and Women's Hospital Boston Massachusetts

Sponsors (2)

Lead Sponsor Collaborator
Brigham and Women's Hospital Biofourmis Inc.

Country where clinical trial is conducted

United States, 

References & Publications (7)

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. — View Citation

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;:. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary 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)
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