Highly Correlated Multimorbidity in Alzheimer’s Disease: An EHR Analysis
Nai-Ching Chi, Kathleen Buckwalter, Pui Ying Yew, Nanle Gusen, Hua-Chin Chi, Chih-Lin Chi, Scott Larson

TL;DR
This study finds that Alzheimer’s patients often have multiple related chronic conditions, especially cardiovascular, metabolic, and mental health issues, which can help improve treatment strategies.
Contribution
The study identifies highly correlated multimorbidity patterns in Alzheimer’s patients using EHR data, revealing novel disease clusters for targeted management.
Findings
The hypertension-lipoprotein metabolism disorders dyad was the most frequent co-occurring condition in Alzheimer’s patients.
Cardiovascular, metabolic, and psychiatric conditions showed strong clustering in multimorbidity patterns.
The hypertension-dyslipidemia-depression-anxiety tetrad had the strongest correlation among four-condition clusters.
Abstract
Most multimorbidity studies focus on the prevalence of individual chronic conditions, neglecting the crucial aspect of correlated disease combinations. This study addressed this gap by investigating highly correlated multimorbidity patterns in 2,629 Alzheimer’s Disease (AD) patients using de-identified electronic health records from the University of Iowa Hospitals and Clinics. Diagnoses, coded to the first three levels of ICD-10, were analyzed using the Apriori algorithm to identify significant co-occurring chronic conditions (dyads, triads, and tetrads). Leverage metrics quantified the strength of these correlations. The hypertension-lipoprotein metabolism disorders dyad (I10-E78) was most prominent, occurring 7.8% more frequently than expected (21.4% total). Other significant dyads included depression-anxiety (F33-F41; 4.5% leverage), dyslipidemia-ischemic heart disease (E78-I25;…
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Taxonomy
TopicsChronic Disease Management Strategies · Machine Learning in Healthcare · Diabetes Treatment and Management
