ArticleJCO oncology practice2023
Predicting Acute Care Events Among Patients Initiating Chemotherapy: A Practice-Based Validation and Adaptation of the PROACCT Model.
Article in JCO oncology practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Acute Care Events During Systemic Cancer Treatment: Moving From Risk Prediction to Clinical Decision Support Using a Two-Model Approach.JCO oncology practice · 2026Article
- Development of Machine Learning Systems to Predict Cancer-Related Symptoms With Validation Across a Health Care System.JCO clinical cancer informatics · 2025Article
- Predicting the Future by Studying the Past for Patients With Cancer Diagnosed in the Emergency Department.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
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6 authors.
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Abstract
purposeAcute care events (ACEs), comprising emergency department visits and hospitalizations, are a priority area for reduction in oncology. Prognostic models are a compelling strategy to identify high-risk patients and target preventive services, but have yet to be broadly implemented, partly because of challenges with electronic health record (EHR) integration. To facilitate EHR integration, we adapted and validated the previously published PRediction Of Acute Care use during Cancer Treatment (PROACCT) model to identify patients at highest risk for ACEs after systemic anticancer treatment.
methodsA retrospective cohort of adults with a cancer diagnosis starting systemic therapy at a single center between July and November 2021 was divided into development (70%) and validation (30%) sets. Clinical and demographic variables were extracted, limited to those in structured format in the EHR, including cancer diagnosis, age, drug category, and ACE in prior year. Three logistic regression models of increasing complexity were developed to predict risk of ACEs.
resultsFive thousand one hundred fifty-three patients were evaluated (3,603 development and 1,550 validation). Several factors were predictive of ACEs: age (in decades), receipt of cytotoxic chemotherapy or immunotherapy, thoracic, GI or hematologic malignancy, and ACE in the prior year. We defined high-risk as the top 10% of risk scores; this population had 33.6% ACE rate compared with 8.3% for the remaining 90% in the low-risk group. The simplest Adapted PROACCT model had a C-statistic of 0.79, sensitivity of 0.28, and specificity of 0.93.
conclusionWe present three models designed for EHR integration that effectively identify oncology patients at highest risk for ACE after initiation of systemic anticancer treatment. By limiting predictors to structured data fields and including all cancer types, these models offer broad applicability for cancer care organizations and may offer a safety net to identify and target resources to this high risk.
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