Chemotherapeutic ToxicityChemo-SHIELDNCT05122247
Machine Learning to Predict Acute Care During Cancer Therapy
An observational study of Chemotherapeutic Toxicity, run by Duke University at 1 site in United States.
Trial summary
The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chemotherapeutic Toxicity
- What is being tested?
- Machine learning algorithm
- How many people
- 12,000 participants
- Where
- 1 siteUnited States
- Study dates
- 3 Jan 2022to 19 Sep 2023
- Phase
- Observational
- 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
Study population: Duke patients undergoing chemotherapy who had at least one treatment encounter between 1/7/2019 and 6/30/2019
Who may be able to join
- had treatment encounter in the Duke Medical Oncology department from January 7th, 2019 to June 30th, 2019
- DUHS medical record available
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
United States1 site
Duke University Health System
Durham, North Carolina, 27710
About this study
Official title: Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer
The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.
Read the detailed description
A previously described machine learning (ML)-based model accurately predicted ED visits or hospitalizations for cancer patients undergoing radiation therapy or chemoradiation. An IRB approved prospective randomized trial, SHIELD-RT (NCT04277650) found that preemptive intervention for patients undergoing radiation and chemoradiation based on the ML model's risk stratification decreased the relative risk of acute care visits by 50%, showing that ML-guided escalation of care improved personalized supportive care and treatment compliance while decreasing healthcare costs.
The objective of this study is to apply this validated ML based model to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Once validated, this study will add to the previously published body of evidence supporting a randomized trial evaluating the ML algorithm's ability to assign intervention for patients receiving systemic therapy at highest risk for acute care encounters.
- Conditions
- Chemotherapeutic Toxicity
- Being tested
- Machine learning algorithm
- Keywords
- machine learning, algorithm, outpatient
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 chemotherapy patients at high-risk for emergency department acute care and/or hospitalization
What is being measured
The primary outcome is the main question the study is designed to answer.
number of unplanned of hospital admission or emergency department visits during systemic therapy
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Nov 2021
- Study start3 Jan 2022
- Primary completion19 Sep 2023
- Study completion19 Sep 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Chemotherapeutic ToxicityStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 12,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Duke University
- Sponsor type
- University, hospital or other
- Collaborators
- University of California, San Francisco
- Responsible party
- Sponsor
- Investigators
- Manisha Palta, MD, Principal investigator, Duke Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05122247
- Sponsor's protocol ID
- Pro00109633
- Acronym
- Chemo-SHIELD
- Status last verified
- Sep 2023
- First submitted
- 5 Nov 2021
- Last updated
- 21 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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 32886536
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Generalizable Machine Learning to Predict Acute Care During Outpatient Systemic Cancer
- ClinicalTrials.gov ID
- NCT05122247
- Acronym / sponsor ID
- Chemo-SHIELD / Pro00109633
- Conditions
- Chemotherapeutic Toxicity
- Keywords
- machine learning, algorithm, outpatient
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 12,000 participants (actual)
- Lead sponsor
- Duke UniversityUniversity, hospital or other
- Collaborators
- University of California, San Francisco
- Study start
- 3 Jan 2022 (actual)
- Primary completion
- 19 Sep 2023 (actual)
- Study completion
- 19 Sep 2023 (actual)
- First posted
- 16 Nov 2021 (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.
The objective of this study is to apply a validated machine-learning based model (SHIELD-RT, NCT04277650) to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters.
Detailed description
A previously described machine learning (ML)-based model accurately predicted ED visits or hospitalizations for cancer patients undergoing radiation therapy or chemoradiation. An IRB approved prospective randomized trial, SHIELD-RT (NCT04277650) found that preemptive intervention for patients undergoing radiation and chemoradiation based on the ML model's risk stratification decreased the relative risk of acute care visits by 50%, showing that ML-guided escalation of care improved personalized supportive care and treatment compliance while decreasing healthcare costs.
The objective of this study is to apply this validated ML based model to a cohort of patients undergoing systemic therapy as outpatient cancer treatment to generate an automatic system for the prediction of unplanned hospital admission rates and emergency department encounters. Once validated, this study will add to the previously published body of evidence supporting a randomized trial evaluating the ML algorithm's ability to assign intervention for patients receiving systemic therapy at highest risk for acute care encounters.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Duke patients undergoing chemotherapy who had at least one treatment encounter between 1/7/2019 and 6/30/2019
Key inclusion criteria (2 listed)
- had treatment encounter in the Duke Medical Oncology department from January 7th, 2019 to June 30th, 2019
- DUHS medical record available
Key exclusion criteria (1 listed)
- -
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 chemotherapy patients at high-risk for emergency department acute care and/or hospitalization | Other | – |
Primary outcome
number of unplanned of hospital admission or emergency department visits during systemic therapy
Time frame: 12 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 21 Sep 2023.
- Background: 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.
- Individual participant dataSharing planned: No
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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?
An observational study of 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 2 inclusion and 1 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 21 Sep 2023. 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 of hospital admission or emergency department visits during systemic therapy (time frame: 12 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 NCT05122247, which also lists 1 linked publication.
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 21 Sep 2023.