Temporal Subtyping of Alzheimer's Disease Using Medical Conditions Preceding Alzheimer's Disease Onset in Electronic Health Records
This work addresses subtyping Alzheimer's disease to potentially improve early detection and personalized treatment, though it is incremental as it applies an existing method to new data.
The study used spectral clustering on longitudinal EHR data from 29,922 Alzheimer's disease patients to identify four subtypes based on patterns of other conditions before diagnosis, which also differed significantly in demographics, mortality, and medications after diagnosis.
Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment, prognosis and disease management. It can also support the testing of new prevention and treatment strategies through clinical trials. In this study, we employed spectral clustering to cluster 29,922 AD patients in the OneFlorida Data Trust using their longitudinal EHR data of diagnosis and conditions into four subtypes. These subtypes exhibit different patterns of progression of other conditions prior to the first AD diagnosis. In addition, according to the results of various statistical tests, these subtypes are also significantly different with respect to demographics, mortality, and prescription medications after the AD diagnosis. This study could potentially facilitate early detection and personalized treatment of AD as well as data-driven generalizability assessment of clinical trials for AD.