System for High-Intensity Evaluation During Radiotherapy

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

311 participants

Study Classification

INTERVENTIONAL

Study Start Date

2018-09-07

Study Completion Date

2019-06-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Radiation Therapy Complication Chemotherapeutic Toxicity

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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

Primary Study Purpose

SUPPORTIVE_CARE

Blinding Strategy

NONE

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.

Study Groups

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Once weekly clinical evaluation

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

Group Type ACTIVE_COMPARATOR

Machine learning algorithm

Intervention Type OTHER

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

Twice weekly clinical evaluation

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

Group Type EXPERIMENTAL

Machine learning algorithm

Intervention Type OTHER

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

Interventions

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Machine learning algorithm

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

Intervention Type OTHER

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No