Machine Learning to Predict Acute Care During Cancer Therapy

Chemotherapeutic Toxicity Clinical Trial

— Chemo-SHIELD  

Official title:

Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05122247
• Other study ID #: Pro00109633
• Status: Completed
• 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
• 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:

-

Related Conditions & MeSH terms

• Chemotherapeutic Toxicity

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