Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007–2022)

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and management. Yet, little is known about how these multimorbidity patterns have evolved over time.ObjectiveTo identify temporal shifts in comorbidity-based phenotypes among older adults with AD visiting EDs between 2007 and 2022 using unsupervised clustering methods.MethodsWe analyzed ED visits for adults aged ≥60 with an AD diagnosis from the Nationwide Emergency Department Sample (NEDS) for the years 2007, 2012, 2017, and 2022. Using ICD-9/10 codes, we mapped diagnoses to 30 clinically relevant comorbidities per year and applied the k-means clustering method to identify subgroups based on diagnostic co-occurrence. Heatmaps summarized cluster compositions across timepoints.ResultsOver 15 years, four stable but evolving comorbidity clusters emerged in each year. Earlier cohorts (2007-2012) were dominated by cardiovascular and respiratory clusters (e.g., CHF, CAD, respiratory failure), while more recent cohorts (2017-2022) showed increased prevalence of nonspecific, frailty-related presentations (e.g., fatigue, GERD, general symptoms). Despite rising ED utilization among older adults, the proportion of visits documenting AD declined from 2.59% in 2007 to 1.34% in 2022, potentially reflecting shifts in coding, outpatient management, and diagnostic overshadowing by acute symptoms.ConclusionsThe comorbidity landscape of AD-related ED visits is changing, with a shift toward vaguer syndromes and complex multimorbidity. These findings underscore the need for dementia-aware triage strategies and dynamic phenotyping tools to improve emergency care for cognitively impaired older adults.