Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis
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 pr...
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sg-ntu-dr.10356-1454122023-03-05T16:43:53Z Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis Yuan, Qi Tan, Tee Hng Wang, Peizhi Devi, Fiona Ong, Hui Lin Abdin, Edimansyah Harish, Magadi Goveas, Richard Ng, Li Ling Chong, Siow Ann Subramaniam, Mythily Lee Kong Chian School of Medicine (LKCMedicine) Science::Medicine Caregiver Dementia 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. Ministry of Health (MOH) National Medical Research Council (NMRC) Published version The study is funded by the Singapore Ministry of Health’s National Medical Research Council under the Center Grant Programme (Grant No.: NMRC/CG/004/2013) and the Institute of Mental Health Bridging Fund (CRCref No.: 545-2016). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. 2020-12-21T05:46:37Z 2020-12-21T05:46:37Z 2020 Journal Article Yuan, Q., Tan, T. H., Wang, P., Devi, F., Ong, H. L., Abdin, E., . . . Subramaniam, M. (2020). Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis. PLOS ONE, 15(1), e0227857-. doi:10.1371/journal.pone.0227857 1932-6203 https://hdl.handle.net/10356/145412 10.1371/journal.pone.0227857 31940419 1 15 en NMRC/CG/004/2013 545-2016 PLOS ONE © 2020 Yuan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. application/pdf |
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Science::Medicine Caregiver Dementia Yuan, Qi Tan, Tee Hng Wang, Peizhi Devi, Fiona Ong, Hui Lin Abdin, Edimansyah Harish, Magadi Goveas, Richard Ng, Li Ling Chong, Siow Ann Subramaniam, Mythily Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
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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. |
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Lee Kong Chian School of Medicine (LKCMedicine) |
author_facet |
Lee Kong Chian School of Medicine (LKCMedicine) Yuan, Qi Tan, Tee Hng Wang, Peizhi Devi, Fiona Ong, Hui Lin Abdin, Edimansyah Harish, Magadi Goveas, Richard Ng, Li Ling Chong, Siow Ann Subramaniam, Mythily |
format |
Article |
author |
Yuan, Qi Tan, Tee Hng Wang, Peizhi Devi, Fiona Ong, Hui Lin Abdin, Edimansyah Harish, Magadi Goveas, Richard Ng, Li Ling Chong, Siow Ann Subramaniam, Mythily |
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Yuan, Qi |
title |
Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
title_short |
Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
title_full |
Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
title_fullStr |
Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
title_full_unstemmed |
Staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
title_sort |
staging dementia based on caregiver reported patient symptoms : implications from a latent class analysis |
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2020 |
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https://hdl.handle.net/10356/145412 |
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1759857195946606592 |