Acute Risk Monitoring for Oncology Therapy Regimen

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.

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Key information

Conditions

Cancer Acute Care Service Utilization Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

University of California, San Francisco

San Francisco, California, 94143, United States

About this study

OBJECTIVES:

I. 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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Medical Record Review

Other

Retrospective chart reviews for data collection will be conducted.

Primary outcomes

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.

Sponsors and collaborators

Lead sponsor

University of California, San Francisco

Other

Collaborators

Conquer Cancer Foundation
National Cancer Institute (NCI)

Registry information

Official study title

Acute Risk Monitoring for Oncology Therapy Regimens (ARMOR): A Silent Prospective Validation of a Machine Learning Model

Acronym: ARMOR

Important dates

Study start: 2017
Primary completion: 2024
Study completion: 2024
First posted: May 22, 2026
Registry last updated: May 22, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed