ArticlemedRxiv : the preprint server for health sciences2025
Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's Disease.
Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAlzheimer's disease (AD) carries a high societal burden inequitably distributed across demographic groups.
objectiveTo examine differences in readily ascertainable clinical outcomes of AD decline among demographic groups.
methodsLeveraging 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 (
resultsThe 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],
conclusionThis 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.
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