An observational study in Cancer and Acute Care Service Utilization, sponsored by University of California, San Francisco. Completed at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-05-22.
Sponsored by University of California, San Francisco · Observational
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.
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.
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Adult patients receiving care for cancer
Exclusion Criteria:
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
Retrospective chart reviews for data collection will be conducted.
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.
Time frame: Up to 6.75 years
Plan to share: Yes — De-identified data may be shared with study collaborators during the course of the study.
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This study is completed, as verified in May 2026. You cannot join it, but the record below documents what was studied.
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University of California, San Francisco