Ryanne C Buckley, Jacob N Stein, Alexis C Wardell, Tzy-Mey Kuo, Christopher Baggett, Hazel B Nichols, Erin E Hahn, Karen J Wernli, Lawrence H Kushi, Theresa H M Keegan, Andrew B Smitherman
This is the first validated risk model to predict ACEs in AYA patients with cancer during the early post-treatment period. The model, designed for seamless electronic health record integration, enables early identification of high-risk patients, presenting opportunities for targeted interventions to reduce acute care use. Further validation across different health care systems is planned and will expand clinical applicability.
PURPOSE: Adolescent and young adult (AYA) patients with cancer (ages 15-39 years at diagnosis) continue to have health care needs after completing treatment. Risk models to predict acute care events (ACEs) in this population may lead to earlier identification of high-risk populations. This study aimed to develop and validate a clinical risk model to predict ACEs in AYA patients with cancer during the early post-treatment period.
METHODS: Using the University of North Carolina Lineberger Cancer Information and Population Health Resource, we identified AYAs diagnosed between 2006 and 2018 who were 2-5 years after diagnosis. The primary outcome was any ACE, defined as either hospitalization or emergency department (ED) visit. Patients were randomly assigned to development (70%) and validation (30%) cohorts. Logistic regression models were developed using stepwise inclusion of predictive variables. Model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), and AUC.
RESULTS: The study cohort included 7,393 patients (development = 5,276, validation = 2,217) with an average follow-up of 1.9 years. The most common cancers were breast (17%), thyroid (14%), and gynecologic (10%). Patients with ACEs (n = 3,572, 48%) were more frequently female, Black, and publicly insured. Defining high risk as the top 20% of scores, in the validation cohort, the selected model achieved an AUC of 0.76, a specificity of 0.94, a sensitivity of 0.34, and a PPV of 0.84.
CONCLUSION: This is the first validated risk model to predict ACEs in AYA patients with cancer during the early post-treatment period. The model, designed for seamless electronic health record integration, enables early identification of high-risk patients, presenting opportunities for targeted interventions to reduce acute care use. Further validation across different health care systems is planned and will expand clinical applicability.