Ignite Creation Date:
2025-12-24 @ 5:23 PM
Ignite Modification Date:
2026-01-01 @ 8:06 AM
Study NCT ID:
NCT04277650
Status:
COMPLETED
Last Update Posted:
2021-05-19
First Post:
2020-02-18
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
System for High-Intensity Evaluation During Radiotherapy
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'interventionBrowseModule': {'meshes': [{'id': 'D000098435', 'term': 'Machine Learning Algorithms'}], 'ancestors': [{'id': 'D000465', 'term': 'Algorithms'}, {'id': 'D055641', 'term': 'Mathematical Concepts'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE', 'maskingDescription': '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.'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Participants identified by the machine learning (ML) algorithm as high risk were randomized to either once weekly or twice weekly clinical evaluations'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 311}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2018-09-07', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2021-01', 'completionDateStruct': {'date': '2019-06-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-05-17', 'studyFirstSubmitDate': '2020-02-18', 'studyFirstSubmitQcDate': '2020-02-19', 'lastUpdatePostDateStruct': {'date': '2021-05-19', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-02-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-06-30', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number of unplanned emergency department visits or hospital admissions', 'timeFrame': '6 months'}], 'secondaryOutcomes': [{'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'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Radiation Therapy Complication', 'Chemotherapeutic Toxicity']}, 'referencesModule': {'references': [{'pmid': '32886536', 'type': 'RESULT', '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.'}, {'pmid': '38776517', 'type': 'DERIVED', 'citation': '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': '38586278', 'type': 'DERIVED', 'citation': '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': '36180836', 'type': 'DERIVED', '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.'}]}, 'descriptionModule': {'briefSummary': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n• started outpatient radiation therapy with or without concurrent systemic therapy at Duke Cancer Center\n\nExclusion Criteria:\n\n* undergoing total body radiation therapy for hematopoetic stem cell transplantation\n* undergoing therapy as inpatient\n* treating physician who opted out of randomization\n* completed radiation therapy prior to algorithm execution'}, 'identificationModule': {'nctId': 'NCT04277650', 'acronym': 'SHIELD-RT', 'briefTitle': 'System for High-Intensity Evaluation During Radiotherapy', 'organization': {'class': 'OTHER', 'fullName': 'Duke University'}, 'officialTitle': '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', 'orgStudyIdInfo': {'id': 'Pro00100647'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Once weekly clinical evaluation', 'description': 'Outpatient participants evaluated as high risk by the machine learning algorithm and provided once weekly clinical evaluations', 'interventionNames': ['Other: Machine learning algorithm']}, {'type': 'EXPERIMENTAL', 'label': 'Twice weekly clinical evaluation', 'description': 'Outpatient participants evaluated as high risk by the machine learning algorithm and provided twice weekly clinical evaluations', 'interventionNames': ['Other: Machine learning algorithm']}], 'interventions': [{'name': 'Machine learning algorithm', 'type': 'OTHER', 'description': 'machine learning directed identification of radiotherapy or chemoradiotherapy patients at high-risk for emergency department acute care and/or hospitalization', 'armGroupLabels': ['Once weekly clinical evaluation', 'Twice weekly clinical evaluation']}]}, 'contactsLocationsModule': {'locations': [{'zip': '27710', 'city': 'Durham', 'state': 'North Carolina', 'country': 'United States', 'facility': 'Duke Cancer Center', 'geoPoint': {'lat': 35.99403, 'lon': -78.89862}}], 'overallOfficials': [{'name': 'Manisha Palta, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Duke Health'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'De-identified summary data in the form of publication data tables and figures will be shared. Individual level data will not be shared.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Duke University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}