Clinical trial · Observational
Acute Risk Monitoring for Oncology Therapy Regimen
Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Acute Care Service Utilization | — | UNRESOLVED | — |
| Cancer | Malignant Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Medical record review | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients receiving cancer therapy at University of California, San Francisco (UCSF)
- description
- All adults undergoing systemic cancer-related therapy from July 2017 to March 2024 at any UCSF outpatient, infusion center with available OP-35 data.
- interventionNames
- Other: Medical record review
Primary outcomes (1)
- measure
- Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model.
- timeFrame
- Up to 6.75 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations. Exclusion Criteria: * Patients under the age of 18. * Patients receiving care as part of a clinical trial.
References
Publications (0)
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