Acute Risk Monitoring for Oncology Therapy Regimen

NCT07601802 | Completed

• 2 other identifiers: 20721R01CA277782
• Study Type: observational
• Target: 4,740
• Locations: 1 country
• Sites: 1

Brief Summary

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

Cancer; Acute Care Service Utilization

Keywords

Radiation therapy; Chemoradiation; Acute Care; Chemotherapy; Risk Cancer Care Delivery; Machine Learning; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

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

  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.

  Up to 6.75 years

Study Arms (1)

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.

Other: Medical record review

Interventions

Medical record review OTHER

Retrospective chart reviews for data collection will be conducted.

Patients receiving cancer therapy at University of California, San Francisco (UCSF)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients receiving care for cancer

You may qualify if:

• Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations.

You may not qualify if:

• Patients under the age of 18.
• Patients receiving care as part of a clinical trial.

Sponsors & Collaborators

• University of California, San Francisco: lead
• Conquer Cancer Foundation: collaborator
• National Cancer Institute (NCI): collaborator

Study Sites (1)

University of California, San Francisco

San Francisco, California, 94143, United States

Location

MeSH Terms

Conditions

Neoplasms

Study Officials

• Julian Hong, MD, MS

  University of California, San Francisco

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 15, 2026
• First Posted: May 22, 2026
• Study Start: July 1, 2017
• Primary Completion: March 31, 2024
• Study Completion: March 31, 2024
• Last Updated: May 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will share

Locations

US (1)