Work

Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer’s disease

Year: 2026

Type: article

Abstract: Alzheimer’s disease (AD) carries a high societal burden inequitably distributed across demographic groups. Using real-world electronic health record (EHR) data with accurate population identification, we examine demographic differences and potentially modifiable drivers of AD decline. Leveraging EHR data (1994–2022) from two large independent healthcare systems, we applied an unsupervised phenotyping algorithm to predict AD diagnosis and validated using gold-standard chart-reviewed and registry-derived diagnosis labels. Among patients with ≥24 months of EHR data not living in nursing homes pre-AD diagnosis, we estimated the time-to-decline (nursing home admission, death) in healthcare system-specific covariate-adjusted competing risk survival analyses stratified by demographic groups. We then performed covariate-adjusted fixed-effects meta-analyses using inverse variance weighting. The algorithm demonstrates robust performance in identifying AD populations across healthcare systems and demographic groups (AUROC score range: 0.835-0.923). Of the 29,262 AD patients in both healthcare systems (61% women, 90% non-Hispanic White, 79.52 ± 9.39 years of age at AD diagnosis), 49% transition to nursing homes and 52% die during follow-up. In covariate-adjusted fixed-effects meta-analysis, women have higher nursing home admission risk (HR [95% CI] = 1.061 [1.024-1.100], p = 1.203×10-3) but lower death risk (HR [95% CI] = 0.856 [0.811-0.904], p = 2.434×10-8) than men. Non-Hispanic White individuals have similar nursing home risk (HR [95% CI] = 1.006 [0.952-1.063], p = 8.306×10-1) but higher death risk (HR [95% CI] = 1.376 [1.245-1.521], p = 4.084×10-10) than racial and ethnic minorities. Older age at AD diagnosis and greater comorbidity burden increase both nursing home admission and death risk. We provide real-world evidence of drivers of demographic differences in AD decline that could inform individual clinical management and public health policies. People with Alzheimer’s disease have memory loss and behavior changes. They experience varying rates of decline, with some facing higher risks of entering nursing homes or dying. In this study, we examined which individuals were at greater risk of decline by analyzing medical records. We identified 29,262 people with Alzheimer’s disease using an accurate patient identification tool. We found that women were more likely to enter nursing homes, while men and non-Hispanic White individuals were more likely to die. Patients diagnosed at an older age and those with additional health conditions faced increased risks of both poor outcomes. These insights from real-world clinical data may help clinicians tailor individualized care and promote more equitable Alzheimer’s disease management. Venkatesh, Wang et al. identify Alzheimer’s disease populations from electronic health records and examine demographic differences and modifiable drivers of nursing home admission and death risk. Women have higher nursing home admission risk while non-Hispanic white individuals and men have higher death risk, with comorbidities as key drivers.

Source: Communications Medicine

DOI: 10.1038/s43856-026-01443-7