Dementia risk analysis using temporal event modeling on a large real-world dataset

R. Andrew Taylor, Aidan Gilson, Ling Chi, Adrian D. Haimovich, Anna Crawford, Cynthia Brandt, Phillip Magidson, James M. Lai, Scott Levin, Adam P. Mecca, Ula Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.

Original languageEnglish
Article number22618
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023
Externally publishedYes

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