Machine Learning to Predict Acute Care During Cancer Therapy

Overview

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.

Full Title of Study: “Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Retrospective
  • Study Primary Completion Date: September 19, 2023

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.

Interventions

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

Clinical Trial Outcome Measures

Primary Measures

  • number of unplanned of hospital admission or emergency department visits during systemic therapy
    • Time Frame: 12 months

Participating in This Clinical Trial

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:

-

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Duke University
  • Collaborator
    • University of California, San Francisco
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Manisha Palta, MD, Principal Investigator, Duke Health

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.

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