Identifying common disease trajectories of Alzheimer's disease with electronic health records.

ABSTRACT:

Background

Alzheimer's disease (AD) is a leading cause of dementia and an escalating public health concern. Although recent research has identified multiple AD risk factors, most studies examine isolated comorbidities rather than complex, sequential progressions. In this study, we sought to identify multi-step trajectories culminating in AD by analysing longitudinal electronic health records (EHRs).

Methods

We analysed data from 24,473 patients in the University of California Health Data Warehouse (UCHDW). A Fine-Gray subdistribution hazard model identified temporally associated diagnoses, from which we constructed diagnostic trajectories. We employed dynamic time warping and k-means clustering to group similar trajectories, and network analyses to characterize their common

SUBMITTER: Fu M 

PROVIDER: S-EPMC12266379 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature