CompletedNCT07601802

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

Sponsor: University of California, San Francisco

Trial Details

Condition	Cancer, Acute Care Service Utilization
Intervention	Medical record review
Status	Completed
Participants	4,740
Est. Completion	31 Mar 2024
Last Updated	22 May 2026

Primary Outcome

Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model.

Eligibility Criteria

Inclusion Criteria

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.

Trial Sites

Countries

United States

View Registry Entry

Related Analysis

ARMOR Trial Validates Machine Learning Model for Predicting Acute Care Events in Cancer Patients — Real-World Evidence from 4,740 Participants

The completed ARMOR trial successfully validated a machine learning model to predict acute care events in 4,740 cancer patients receiving outpatient systemic therapy. This landmark study offers new insights into preventing potentially avoidable emergency department visits and hospitalizations.

Read full article →

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