Radiation Therapy ComplicationSHIELD-RTNCT04277650
System for High-Intensity Evaluation During Radiotherapy
A study testing Machine learning algorithm in people with Radiation Therapy Complication and Chemotherapeutic Toxicity, run by Duke University at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Radiation Therapy Complication, Chemotherapeutic Toxicity
- What is being tested?
- Machine learning algorithm
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 311 participants
- Where
- 1 siteUnited States
- Study dates
- 7 Sep 2018to 30 Jun 2019
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- started outpatient radiation therapy with or without concurrent systemic therapy at Duke Cancer Center
Who cannot join
- 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 locations and contacts
1 site in 1 country.
About this study
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
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.
- Being tested
- Machine learning algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning algorithmOther
machine learning directed identification of radiotherapy or chemoradiotherapy patients at high-risk for emergency department acute care and/or hospitalization
| Study group | Role | What they receive |
|---|---|---|
| Once weekly clinical evaluation | Active comparator | Outpatient participants evaluated as high risk by the machine learning algorithm and provided once weekly clinical evaluations Receives: Other: Machine learning algorithm |
| Twice weekly clinical evaluation | Experimental | Outpatient participants evaluated as high risk by the machine learning algorithm and provided twice weekly clinical evaluations Receives: Other: Machine learning algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of unplanned emergency department visits or hospital admissions
Measured: 6 months
Secondary outcomes (3)
Number of unplanned emergency department visits or hospital admissions up to 15 days post radiation treatment
Measured: up to 15 days post radiation treatment
Number of missed clinical evaluation visits
Measured: 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
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Feb 2020
- Study start7 Sep 2018
- Primary completion30 Jun 2019
- Study completion30 Jun 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Radiation Therapy ComplicationStudies of the same condition, open and under way.
- More trials run by Duke UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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)
- Enrolment
- 311 participants (actual)
Sponsor and investigators
- Lead sponsor
- Duke University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Manisha Palta, MD, Principal investigator, Duke Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04277650
- Sponsor's protocol ID
- Pro00100647
- Acronym
- SHIELD-RT
- Status last verified
- Jan 2021
- First submitted
- 18 Feb 2020
- Last updated
- 19 May 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: De-identified summary data in the form of publication data tables and figures will be shared. Individual level data will not be shared.
Publications
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.
PubMed 32886536James 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.
PubMed 38776517Natesan 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.
PubMed 38586278Hong 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.
PubMed 36180836
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04277650
- Acronym / sponsor ID
- SHIELD-RT / Pro00100647
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 311 participants (actual)
- Lead sponsor
- Duke UniversityUniversity, hospital or other
- Study start
- 7 Sep 2018 (actual)
- Primary completion
- 30 Jun 2019 (actual)
- Study completion
- 30 Jun 2019 (actual)
- First posted
- 20 Feb 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Supportive care
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Participants identified by the machine learning (ML) algorithm as high risk were randomized to either once weekly or twice weekly clinical evaluations
| Arm | Type | Receives |
|---|---|---|
| Once weekly clinical evaluation | Active comparator | Other: Machine learning algorithmOutpatient participants evaluated as high risk by the machine learning algorithm and provided once weekly clinical evaluations |
| Twice weekly clinical evaluation | Experimental | Other: Machine learning algorithmOutpatient participants evaluated as high risk by the machine learning algorithm and provided twice weekly clinical evaluations |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- started outpatient radiation therapy with or without concurrent systemic therapy at Duke Cancer Center
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning algorithmmachine learning directed identification of radiotherapy or chemoradiotherapy patients at high-risk for emergency department acute care and/or hospitalization | Other | Once weekly clinical evaluation, Twice weekly clinical evaluation |
Primary outcome
Number of unplanned emergency department visits or hospital admissions
Time frame: 6 months
3 secondary outcomes
Number of unplanned emergency department visits or hospital admissions up to 15 days post radiation treatment
Time frame: up to 15 days post radiation treatment
Number of missed clinical evaluation visits
Time frame: 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
Time frame: 6 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2021.
- Results publication: PubMed 32886536Hong 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.
- Derived from this study: PubMed 38776517James 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.
- Derived from this study: PubMed 38586278Natesan 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.
- Derived from this study: PubMed 36180836Hong 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.
- Individual participant dataSharing planned: No. De-identified summary data in the form of publication data tables and figures will be shared. Individual level data will not be shared.
Related trials for Radiation Therapy Complication
Not yet recruitingObservational study
Determinants of Radiation Dermatitis in Head and Neck Cancer Patients Receiving Preventive Photobiomodulation
Head and Neck Neoplasms, Radiodermatitis
Recruiting
A Study of Short-course Hypofractionated Online-adaptive Radiotherapy for the Post-operative Treatment of Endometrial Cancer: Tolerability and Feasibility
Endometrial Carcinoma (EC)
RecruitingPhase 1/2
E7 T-cell Receptor (TCR) -T Cell Induction Therapy for Locoregionally Advanced HPV-associated Cancers
HPV-Associated Cervical Carcinoma, HPV-Related Carcinoma
RecruitingPhase 1/2
Lead-212 PSV359 Therapy for Patients With Solid Tumors
Pancreatic Ductal Adenocarcinoma, Gastric Cancer
RecruitingPhase 3
A Study of Trastuzumab Deruxtecan Versus Standard of Care Chemotherapy for HER2-Expressing (IHC 3+/2+) Endometrial Cancer in Adjuvant Setting
Endometrial Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Manisha Palta, MD principal investigator
Duke Health
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine learning algorithm in people with Radiation Therapy Complication and Chemotherapeutic Toxicity, run by Duke University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 May 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of unplanned emergency department visits or hospital admissions (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04277650, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 May 2021.