Literature DB >> 31859230

Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

Thomas H McCoy1, Larry Han2, Amelia M Pellegrini1, Rudolph E Tanzi3, Sabina Berretta4, Roy H Perlis1.   

Abstract

INTRODUCTION: Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high-risk individuals.
METHODS: We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow-up.
RESULTS: Among 267,855 hospitalized patients with 1,251,858 patient years of follow-up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54-1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients. DISCUSSION: A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.
© 2019 the Alzheimer's Association.

Entities:  

Keywords:  Alzheimer's disease; Cognition; Data mining; Dementia; Electronic health record; Machine learning; Natural language processing; Phenotype; Research domain criteria

Mesh:

Year:  2020        PMID: 31859230      PMCID: PMC7067642          DOI: 10.1016/j.jalz.2019.09.084

Source DB:  PubMed          Journal:  Alzheimers Dement        ISSN: 1552-5260            Impact factor:   21.566


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