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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05122247
Other study ID # Pro00109633
Secondary ID
Status Completed
Phase
First received
Last updated
Start date January 3, 2022
Est. completion date September 19, 2023

Study information

Verified date September 2023
Source Duke University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.


Description:

A previously described machine learning (ML)-based model accurately predicted ED visits or hospitalizations for cancer patients undergoing radiation therapy or chemoradiation. An IRB approved prospective randomized trial, SHIELD-RT (NCT04277650) found that preemptive intervention for patients undergoing radiation and chemoradiation based on the ML model's risk stratification decreased the relative risk of acute care visits by 50%, showing that ML-guided escalation of care improved personalized supportive care and treatment compliance while decreasing healthcare costs. The objective of this study is to apply this validated ML based model to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Once validated, this study will add to the previously published body of evidence supporting a randomized trial evaluating the ML algorithm's ability to assign intervention for patients receiving systemic therapy at highest risk for acute care encounters.


Recruitment information / eligibility

Status Completed
Enrollment 12000
Est. completion date September 19, 2023
Est. primary completion date September 19, 2023
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - had treatment encounter in the Duke Medical Oncology department from January 7th, 2019 to June 30th, 2019 - DUHS medical record available Exclusion Criteria: -

Study Design


Related Conditions & MeSH terms


Intervention

Other:
Machine learning algorithm
machine learning directed identification of chemotherapy patients at high-risk for emergency department acute care and/or hospitalization

Locations

Country Name City State
United States Duke University Health System Durham North Carolina

Sponsors (2)

Lead Sponsor Collaborator
Duke University University of California, San Francisco

Country where clinical trial is conducted

United States, 

References & Publications (1)

Hong JC, Eclov NCW, Dalal NH, Thomas SM, Stephens SJ, Malicki M, Shields S, Cobb A, Mowery YM, Niedzwiecki D, Tenenbaum JD, Palta M. System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation. J Clin Oncol. 2020 Nov 1;38(31):3652-3661. doi: 10.1200/JCO.20.01688. Epub 2020 Sep 4. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary number of unplanned of hospital admission or emergency department visits during systemic therapy 12 months
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