Presentation (CTI)

OBJECTIVES: As Alzheimer’s Disease (AD) management shifts toward earlier diagnostic utilization, effectively identifying preclinical AD (PCAD) populations has become a clinical priority. In these populations, risk-factor assessments combined with early cognitive symptomatology are critical for identifying patients at risk of progression to symptomatic AD and for guiding subsequent biomarker testing to confirm AD pathology. With the shift from research settings to real-world clinical practice, it reveals important limitations in current health economic and outcomes research (HEOR) frameworks for evaluating this population. Existing frameworks are largely anchored to cognitively impaired populations and fail to capture the increasing utilization of interventions in preclinical populations. This research proposes a framework to identify and incorporate preclinical AD populations into existing HEOR frameworks, capturing care utilization and economic impact across the entire AD continuum.
METHODS: To align HEOR with shifting clinical practice paradigms, diagnosis timing is modeled as a dynamic variable rather than a fixed entry state - i.e., diagnosis due to cognitive impairment. We introduce two probabilistic, real-world-derived inputs: (1) an NLP-based symptomatology assessment capturing prodromal signs from clinical text prior to objective impairment; and (2) a time-varying risk factor analysis predicting the probability of biomarker positivity (e.g., p-tau). Combined, these scores quantify the likelihood of progression across the early care journey from the asymptomatic population to subjective cognitive impairment and monitoring through biomarker confirmation and treatment initiation.
RESULTS: This proposed framework identifies novel economic value pathways associated with early identification. By shifting the temporal modeling of costs and benefits, the framework captures outcomes often omitted in traditional models, including resource utilization for proactive monitoring, the value of anticipatory planning, and the long-term impacts on survival and health-related quality of life.
CONCLUSIONS: This framework provides a foundation for evaluating the economic value of earlier AD identification within evolving real-world diagnostic and treatment pathways.