System for High-Intensity Evaluation During Radiotherapy

Study Overview
=================
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.

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

Conditions
-----------------
Radiation Therapy Complication, Chemotherapeutic Toxicity

Intervention / Treatment
-----------------
* Other: Machine learning algorithm


Participation Criteria
=================
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

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Supportive Care
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Participants identified by the machine learning (ML) algorithm as high risk were randomized to either once weekly or twice weekly clinical evaluations
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Once weekly clinical evaluation<br>Outpatient participants evaluated as high risk by the machine learning algorithm and provided once weekly clinical evaluations | Other: Machine learning algorithm<br>* machine learning directed identification of radiotherapy or chemoradiotherapy patients at high-risk for emergency department acute care and/or hospitalization<br>|
| Experimental: Twice weekly clinical evaluation<br>Outpatient participants evaluated as high risk by the machine learning algorithm and provided twice weekly clinical evaluations | Other: Machine learning algorithm<br>* machine learning directed identification of radiotherapy or chemoradiotherapy patients at high-risk for emergency department acute care and/or hospitalization<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of unplanned emergency department visits or hospital admissions |  | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 |