Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with...

Prognostication for people with Alzheimer's disease (AD) at the point of care could improve clinical management.

In this retrospective cohort study using the electronic health record (EHR) data from a large healthcare system (2011-2022), we applied an unsupervised latent factor clustering approach guided by knowledge graph embeddings to stratify AD patients into two groups at diagnosis (baseline) using clinical features in the two years preceding diagnosis. We prognosticated the risk of AD-related outcomes (nursing home admission and mortality) for the clusters in survival analyses adjusted for baseline confounders (age, gender, race, ethnicity, healthcare utilization, and comorbidities). To reflect real-world evolution in clinical trajectories, we updated patient stratification for patients remaining at risk one year post-diagnosis and repeated prognostication.

We stratify 16,411 AD patients into two groups at baseline (41% Group 1, 59% Group 2). Baseline Group 2 has a significantly lower risk of nursing home admission (HR [95% CI] = 0.804 [0.765, 0.844], p < .001) but comparable mortality risk to baseline Group 1 (HR [95% CI] = 1.008 [0.963, 1.056], p = 0.733). We re-stratify the 12,606 patients remaining at risk one year post-diagnosis (46% Group 1, 54% Group 2). Consistent with baseline, the updated Group 2 has a lower risk of nursing home admission (HR [95% CI] = 0.815 [0.766, 0.868], p < .001) but comparable mortality risk (HR [95% CI] = 0.977 [0.922, 1.035], p = .430) to Group 1.

Patient stratification enables outcome prognosis for AD patients. While baseline prognostication can guide early treatment and tailored management, dynamic prognostication may inform more timely interventions to improve long-term outcomes.