CancerARMORNCT07601802
Acute Risk Monitoring for Oncology Therapy Regimen
An observational study of Cancer and Acute Care Service Utilization, run by University of California, San Francisco at 1 site in United States.
Trial summary
Patients undergoing outpatient infusion systemic therapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospitalizations. These events impact patient outcomes, treatment decisions, and healthcare costs. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cancer, Acute Care Service Utilization
- What is being tested?
- Medical record review
- Type of treatment
- Injection or infusion
- How many people
- 4,740 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Jul 2017to 31 Mar 2024
- 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: Adult patients receiving care for cancer
Who may be able to join
- Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations.
Who cannot join
- Patients under the age of 18.
- Patients receiving care as part of a clinical trial.
Trial locations and contacts
1 site in 1 country.
United States1 site
University of California, San Francisco
San Francisco, California, 94143
About this study
Official title: Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model
Patients undergoing outpatient infusion systemic therapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospitalizations. These events impact patient outcomes, treatment decisions, and healthcare costs. To address this need, the Centers for Medicare \& Medicaid Services developed the chemotherapy measure (OP-35). Recent randomized controlled studies indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. This study aims to develop and prospectively validate ML approaches to predict the risk of OP-35 qualifying, potentially preventable, acute care events within 30 days of infusion systemic therapy.
Read the detailed description
OBJECTIVES:
- Develop and retrospectively validate electronic health record-based machine learning models using routinely collected clinical data from patients receiving systemic therapy to predict risk of potentially preventable OP-35 qualifying acute care events. (Phase 1: Retrospective)
II. Prospectively validate machine learning models across distinct time periods. (Phase 2: Prospective)
III. Understand patterns of care by stratifying and analyzing model performance by treatment type, cancer diagnosis, and race/ethnicity to assess bias and disparities in outcomes.
OUTLINE:
Retrospective and prospective clinical data obtained from medical records will be used to develop and validate predictive machine learning models. Prospective data will be divided into 2 phases: Prospective validation (PV) 1 and PV 2.
- Conditions
- Cancer, Acute Care Service Utilization
- Being tested
- Medical record review
- Keywords
- Radiation therapy, Chemoradiation, Acute Care, Chemotherapy, Risk Cancer Care Delivery, Machine Learning, Artificial Intelligence
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Medical record reviewOther
Retrospective chart reviews for data collection will be conducted.
| Study group | Role | What they receive |
|---|---|---|
| Patients receiving cancer therapy at University of California, San Francisco (UCSF) | All adults undergoing systemic cancer-related therapy from July 2017 to March 2024 at any UCSF outpatient, infusion center with available OP-35 data. Receives: Other: Medical record review |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model.
UCSF patients receiving infusion systemic therapy had clinical data incorporated into machine learning (ML) models to predict risk of Centers for Medicare \& Medicaid Services Chemotherapy Measure (OP-35) qualifying acute care events within 30 days of infusion. Models included variables such as cancer diagnosis, therapeutic agents, and laboratory values. Three ML approaches were employed to train models in predicting OP-35 events. Models were trained and retrospectively validated on data from July 7, 2017, to February 11, 2021, and prospectively validated on 2 cohorts: April 17, 2023, to October 29, 2023 (PV1) and February 19, 2024, to March 31, 2024 (PV2) to generate a validation AUROC. The initial prospective validation occurred over a pre-planned period with the assumption of a 2% event rate, based on the model development data, with an alpha of 0.05 and 84% power to detect an AUROC of 0.75, requiring a sample size of at least 8000 infusions.
Measured: Up to 6.75 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start1 Jul 2017
- Primary completion31 Mar 2024
- Study completion31 Mar 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- More trials run by University of California, San FranciscoThe 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
- Case only
- Time perspective
- Other
- Enrolment
- 4,740 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of California, San Francisco
- Sponsor type
- University, hospital or other
- Collaborators
- Conquer Cancer Foundation, National Cancer Institute (NCI)
- Responsible party
- Sponsor
- Investigators
- Julian Hong, MD, MS, Principal investigator, University of California, San Francisco
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07601802
- Sponsor's protocol ID
- 20721
- Other IDs
- R01CA277782
- Acronym
- ARMOR
- Status last verified
- May 2026
- First submitted
- 15 May 2026
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified data may be shared with study collaborators during the course of the study.
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model
- ClinicalTrials.gov ID
- NCT07601802
- Acronym / sponsor ID
- ARMOR / 20721
- Conditions
- Cancer, Acute Care Service Utilization
- Keywords
- Radiation therapy, Chemoradiation, Acute Care, Chemotherapy, Risk Cancer Care Delivery, Machine Learning, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 4,740 participants (actual)
- Lead sponsor
- University of California, San FranciscoUniversity, hospital or other
- Collaborators
- Conquer Cancer Foundation, National Cancer Institute (NCI)
- Study start
- 1 Jul 2017 (actual)
- Primary completion
- 31 Mar 2024 (actual)
- Study completion
- 31 Mar 2024 (actual)
- First posted
- 22 May 2026 (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.
Patients undergoing outpatient infusion systemic therapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospitalizations. These events impact patient outcomes, treatment decisions, and healthcare costs. To address this need, the Centers for Medicare \& Medicaid Services developed the chemotherapy measure (OP-35). Recent randomized controlled studies indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. This study aims to develop and prospectively validate ML approaches to predict the risk of OP-35 qualifying, potentially preventable, acute care events within 30 days of infusion systemic therapy.
Detailed description
OBJECTIVES:
- Develop and retrospectively validate electronic health record-based machine learning models using routinely collected clinical data from patients receiving systemic therapy to predict risk of potentially preventable OP-35 qualifying acute care events. (Phase 1: Retrospective)
II. Prospectively validate machine learning models across distinct time periods. (Phase 2: Prospective)
III. Understand patterns of care by stratifying and analyzing model performance by treatment type, cancer diagnosis, and race/ethnicity to assess bias and disparities in outcomes.
OUTLINE:
Retrospective and prospective clinical data obtained from medical records will be used to develop and validate predictive machine learning models. Prospective data will be divided into 2 phases: Prospective validation (PV) 1 and PV 2.
Study design
- Observational model
- Case only
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients receiving cancer therapy at University of California, San Francisco (UCSF) | – | Other: Medical record reviewAll adults undergoing systemic cancer-related therapy from July 2017 to March 2024 at any UCSF outpatient, infusion center with available OP-35 data. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients receiving care for cancer
Key inclusion criteria (1 listed)
- Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations.
Key exclusion criteria (2 listed)
- Patients under the age of 18.
- Patients receiving care as part of a clinical trial.
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 |
|---|---|---|
| Medical record reviewRetrospective chart reviews for data collection will be conducted. | Other | Patients receiving cancer therapy at University of California, San Francisco (UCSF) |
Primary outcome
Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model.
Time frame: Up to 6.75 years
UCSF patients receiving infusion systemic therapy had clinical data incorporated into machine learning (ML) models to predict risk of Centers for Medicare \& Medicaid Services Chemotherapy Measure (OP-35) qualifying acute care events within 30 days of infusion. Models included variables such as cancer diagnosis, therapeutic agents, and laboratory values. Three ML approaches were employed to train models in predicting OP-35 events. Models were trained and retrospectively validated on data from July 7, 2017, to February 11, 2021, and prospectively validated on 2 cohorts: April 17, 2023, to October 29, 2023 (PV1) and February 19, 2024, to March 31, 2024 (PV2) to generate a validation AUROC. The initial prospective validation occurred over a pre-planned period with the assumption of a 2% event rate, based on the model development data, with an alpha of 0.05 and 84% power to detect an AUROC of 0.75, requiring a sample size of at least 8000 infusions.
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 22 May 2026.
- Individual participant dataSharing planned: Yes. De-identified data may be shared with study collaborators during the course of the study.
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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.
Julian Hong, MD, MS principal investigator
University of California, San Francisco
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 Cancer and Acute Care Service Utilization, run by University of California, San Francisco 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 2 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 22 May 2026. 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?
Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model. (time frame: Up to 6.75 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 22 May 2026.