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.

NCT Number	NCT07601802
Lead Sponsor	University of California, San Francisco Collaborators: Conquer Cancer Foundation, National Cancer Institute (NCI)
Conditions	Cancer, Acute Care Service Utilization
Enrollment	4,740 participants
Start Date	2017-07-01
Primary Completion	2024-03-31 (estimated)
Study Completion	2024-03-31 (estimated)
Updated on ClinicalTrials.gov	2026-05-22