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INTRODUCTION: Delirium has heterogeneous etiologies and clinical presentations and is often associated with poor outcomes. Its pathophysiological mechanisms remain largely hypothetical and without targeted pharmacological treatment. This work investigates subphenotypes of patients undergoing delirium assessment based on clinical features and fluid biomarkers. METHODS: We performed latent class analysis of an observational cohort of older adults undergoing elective surgery. RESULTS: Two classes were identified, both containing individuals experiencing delirium symptoms, with a higher number in Class 1 (p 

Original publication

DOI

10.1002/alz.70516

Type

Journal article

Journal

Alzheimers Dement

Publication Date

07/2025

Volume

21

Keywords

altered consciousness, biomarkers, cognition, cognitive change, delirium, endotypes, glial fibrillary acidic protein, inattention, latent class analysis, machine learning, neurofilament light chain, phenotyping, post‐operative, post‐operative delirium, subphenotypes, unsupervised clustering, Humans, Male, Female, Delirium, Aged, Biomarkers, Phenotype, Postoperative Complications, Cohort Studies, Latent Class Analysis, Aged, 80 and over, tau Proteins, Amyloid beta-Peptides