Acute Risk Monitoring for Oncology Therapy Regimen

Part of paid clinical trials in San Francisco, California.

Sponsor
University of California, San Francisco
Study ID
NCT07601802
Status
Completed

Conditions

  • Acute Care Service Utilization
  • Cancer

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Medical record review — OTHER
    Retrospective chart reviews for data collection will be conducted.

Study Details

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.

Key Dates

Start date
Jul 1, 2017
Status verified
May 2026
Primary completion
Mar 31, 2024
Completion
Mar 31, 2024

Study Design

Enrollment
4,740 participants (actual)

Arms

  • Arm: 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.

Primary Outcome Measure

Area under the receiver operating characteristic curve (AUROC) for OP-35 prediction model. [ Time Frame: Up to 6.75 years ]

Locations (1)

FacilityCityStateZIPSite coordinators
University of California, San FranciscoSan FranciscoCalifornia94143-

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