A data‐driven examination of clustering of apathy and depression symptoms in people with dementia

Abstract Background Apathy is one of the most common Neuropsychiatric Symptom (NPS) and associated with poor clinical outcomes. remains under‐researched its medical classification unclear, potentially fact that apathy commonly a comorbidity depression. Research helps define phenotype urgently needed, particularly for biomarker studies. Method Using latent class analysis (LCA) we analysed data from KCL Care Home Network study 349 participants (an observational people living dementia in care homes). The aim was to understand how depression phenotypes cluster together. were defined on basis item G (Apathy) D (Depression) NPI‐NH scale, combining 7 subquestions 8 subquestions, rated as yes or no. Predictors membership then analysed, including other NPS, demographics, medication Clinical Dementia Rating (CDR). Result LCA showed 4‐cluster group which considered best model: No symptoms, predominantly depression, apathy/depression, group. Those apathy/depression more likely experience psychosis symptoms compared no class, whilst those have higher CDR score class. Conclusion driven method, show distinct clustering this cohort. There evidence these clusters different associations may help inform diagnostic categories research studies practice. Potentially, four subtypes be used future further evaluate their utility dementia.