Clinical trial NCT04277650: System for High-Intensity Evaluation During Radiotherapy
Official title: System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-directed Clinical Evaluations During Outpatient Cancer Radiation and Chemoradiation
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Radiation Therapy Complication
Chemotherapeutic Toxicity
Lead sponsor: Duke University
Sponsor class: OTHER
First posted: 2020-02-20
Enrollment: 311
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Participants identified by the machine learning (ML) algorithm as high risk were randomized to either once weekly or twice weekly clinical evaluations
Primary purpose: SUPPORTIVE_CARE

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.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Number of unplanned emergency department visits or hospital admissions
timeFrame: 6 months
Secondary outcomes:
measure: Number of unplanned emergency department visits or hospital admissions up to 15 days post radiation treatment
timeFrame: up to 15 days post radiation treatment
measure: Number of missed clinical evaluation visits
timeFrame: 6 months
measure: 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
timeFrame: 6 months
Other outcomes:
Locations:
city: Durham
country: United States
facility: Duke Cancer Center
state: North Carolina
Study officials:
affiliation: Duke Health
name: Manisha Palta, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.