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Staging dementia based on caregiver reported patient symptoms: Implications from a latent class analysis

Qi Yuan, Tee Hng Tan, Peizhi Wang, Fiona Devi, Hui Lin Ong, Edimansyah Abdin, Magadi Harish, Richard Goveas, Li Ling Ng, Siow Ann Chong and Mythily Subramaniam

PLOS ONE, 2020, vol. 15, issue 1, 1-12

Abstract: Background: Tailoring interventions to the needs of caregivers is an important feature of successful caregiver support programs. To improve cost-effectiveness, group tailoring based on the stage of dementia could be a good alternative. However, existing staging strategies mostly depend on trained professionals. Objective: This study aims to stage dementia based on caregiver reported symptoms of persons with dementia. Methods: Latent class analysis was used. The classes derived were then mapped with disease duration to define the stages. Logistic regression with receiver operating characteristic curve was used to generate the optimal cut-offs. Results: Latent class analysis suggested a 4-class solution, these four classes were named as early (25.9%), mild (25.2%), moderate (16.7%) and severe stage (32.3%). The stages based on the cut-offs generated achieved an overall accuracy of 90.8% compared to stages derived from latent class analysis. Conclusion: The current study confirmed that caregiver reported patient symptoms could be used to classify persons with dementia into different stages. The new staging strategy is a good complement of existing dementia clinical assessment tools in terms of better supporting informal caregivers.

Date: 2020
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DOI: 10.1371/journal.pone.0227857

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