Abstract
BACKGROUND: Alzheimer's disease (AD) carries a high societal burden inequitably distributed across demographic groups. OBJECTIVE: To examine differences in readily ascertainable clinical outcomes of AD decline among demographic groups. METHODS: Leveraging the electronic health record (EHR) data (1994-2022) of patients with ≥1 diagnosis code for AD or related dementia from two large healthcare systems, we applied a knowledge graph-guided unsupervised phenotyping algorithm to predict AD diagnosis status and validated using gold-standard chart-reviewed and registry-derived diagnosis labels. After excluding patients with <24 months of data or who were admitted to nursing homes prior to AD diagnosis, we performed survival analyses at each healthcare system to assess the time to two readily ascertainable clinical outcomes of AD decline ( i.e., nursing home admission, death), stratified by demographic groups and accounting for baseline covariates ( e.g., age, gender, race, ethnicity, healthcare utilization, and comorbidities). We then performed a fixed-effects meta-analysis of the survival analysis data from both healthcare systems. RESULTS: The AD diagnosis phenotyping algorithm demonstrated high accuracy in identifying AD patients across both healthcare systems (AUROC score range: 0.835-0.923). Of the 34,181 AD patients in both healthcare systems (62% women, 90% non-Hispanic White, 80.39±9.28 years of age at AD diagnosis), 32% were admitted to nursing homes and 50% died during follow- up. In the fixed-effect meta-analysis, non-Hispanic White patients had a lower risk of nursing home admission (HR[95% CI]=0.825[0.776-0.877], p <0.001) and higher risk of death (HR[95% CI]=1.381[1.283-1.487], p <.0001) than racial and ethnic minorities. There was no difference between women and men in their risk of nursing home admission (HR[95% CI]=1.008[0.967-1.050], p =.762), but women had a lower risk of death (HR[95% CI]=0.873[0.837-0.910], p <.0001) than men. CONCLUSION: This study creates two large EHR-based AD cohorts and adds to the real-world evidence of demographic differences in clinical AD decline, which could potentially inform individual clinical management and future public health policies.