System for High-Intensity Evaluation During Radiotherapy

NCT04277650 | Completed

• 1 other identifier: Pro00100647
• Study Type: interventional
• Target: 311
• Locations: 1 country
• Sites: 1

Brief Summary

This quality improvement project will evaluate the implementation of a previously described intervention (twice per week on-treatment clinical evaluations) in a feasible fashion using a previously described machine learning algorithm identifying patients identified at high risk for an emergency visit or hospitalization during radiation therapy.

Conditions

Radiation Therapy Complication; Chemotherapeutic Toxicity

Outcome Measures

Primary Outcomes (1)

• Number of unplanned emergency department visits or hospital admissions

  6 months

Secondary Outcomes (3)

• Number of unplanned emergency department visits or hospital admissions up to 15 days post radiation treatment

  up to 15 days post radiation treatment

• Number of missed clinical evaluation visits

  6 months

• Number of acute care visits with listed reason as anemia, nutrition (including dehydration), diarrhea, emesis, infectious (including fever, pneumonia, and sepsis), nausea, neutropenia, pain category

  6 months

Study Arms (2)

Once weekly clinical evaluation

ACTIVE COMPARATOR

Outpatient participants evaluated as high risk by the machine learning algorithm and provided once weekly clinical evaluations

Other: Machine learning algorithm

Twice weekly clinical evaluation

EXPERIMENTAL

Outpatient participants evaluated as high risk by the machine learning algorithm and provided twice weekly clinical evaluations

Other: Machine learning algorithm

Interventions

Machine learning algorithm OTHER

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

Once weekly clinical evaluation; Twice weekly clinical evaluation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• started outpatient radiation therapy with or without concurrent systemic therapy at Duke Cancer Center

You may not qualify if:

• undergoing total body radiation therapy for hematopoetic stem cell transplantation
• undergoing therapy as inpatient
• treating physician who opted out of randomization
• completed radiation therapy prior to algorithm execution

Sponsors & Collaborators

• Duke University: lead

Study Sites (1)

Duke Cancer Center

Durham, North Carolina, 27710, United States

Location

Related Publications (4)

• 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.

  PMID: 32886536 RESULT

• James B Yu Md Mhs Fastro, Hong JC. AI Use in Prostate Cancer: Potential Improvements in Treatments and Patient Care. Oncology (Williston Park). 2024 May 13;38(5):208-209. doi: 10.46883/2024.25921021.

  PMID: 38776517 DERIVED

• Natesan D, Eisenstein EL, Thomas SM, Eclov NCW, Dalal NH, Stephens SJ, Malicki M, Shields S, Cobb A, Mowery YM, Niedzwiecki D, Tenenbaum JD, Palta M, Hong JC. Health Care Cost Reductions with Machine Learning-Directed Evaluations during Radiation Therapy - An Economic Analysis of a Randomized Controlled Study. NEJM AI. 2024 Apr;1(4):10.1056/aioa2300118. doi: 10.1056/aioa2300118. Epub 2024 Mar 15.

  PMID: 38586278 DERIVED

• Hong JC, Eclov NCW, Stephens SJ, Mowery YM, Palta M. Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study. BMC Bioinformatics. 2022 Sep 30;23(Suppl 12):408. doi: 10.1186/s12859-022-04940-3.

  PMID: 36180836 DERIVED

MeSH Terms

Interventions

Machine Learning Algorithms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Manisha Palta, MD

  Duke Health

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: The ML directed twice-weekly evaluation arm was unblinded. Participants and providers were blinded to ML identification of high risk participants in the once weekly evaluation (standard of care) arm.
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Participants identified by the machine learning (ML) algorithm as high risk were randomized to either once weekly or twice weekly clinical evaluations

Study Record Dates

• First Submitted: February 18, 2020
• First Posted: February 20, 2020
• Study Start: September 7, 2018
• Primary Completion: June 30, 2019
• Study Completion: June 30, 2019
• Last Updated: May 19, 2021

Record last verified: 2021-01

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)