System for High-Intensity Evaluation During Radiotherapy (SHIELD-RT)

Duke University

Status: Completed

Conditions: Radiation Therapy Complication; Chemotherapeutic Toxicity

Treatments: Other: Machine learning algorithm

Study type: Interventional

Funder types: Other

Identifiers

NCT04277650

Pro00100647

Details and patient eligibility

About

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.

Enrollment: 311 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Supportive Care

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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311 participants in 2 patient groups

Once weekly clinical evaluation

Active Comparator group

Description:

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

Treatment:

Other: Machine learning algorithm

Twice weekly clinical evaluation

Experimental group

Description:

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

Treatment:

Other: Machine learning algorithm