Machine Learning to Predict Acute Care During Cancer Therapy

NCT ID: NCT05122247

Last Updated: 2023-09-21

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 12000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-01-03
• Study Completion Date: 2023-09-19

Brief 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.

Detailed 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.

Conditions

Chemotherapeutic Toxicity

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

Machine learning algorithm

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

Intervention Type: OTHER

Eligibility Criteria

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

-

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of California, San Francisco

OTHER

Sponsor Role: collaborator

Duke University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Manisha Palta, MD

Role: PRINCIPAL_INVESTIGATOR

Duke Health

Locations

Duke University Health System

Durham, North Carolina, United States

Countries

United States

References

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.

Reference Type: BACKGROUND

PMID: 32886536 (View on PubMed)

Other Identifiers

Pro00109633

Identifier Type: -

Identifier Source: org_study_id