Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review
Dementia is a leading cause of disability and death globally. Individuals with diseases such as cardiovascular, cardiometabolic and cerebrovascular disease are often at increased dementia risk. However, while numerous models have been developed to predict dementia, they are often not tailored to dis...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-1729/14/11/1489 |
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| author | Eugene Yee Hing Tang Jacob Brain Serena Sabatini Eduwin Pakpahan Louise Robinson Maha Alshahrani Aliya Naheed Mario Siervo Blossom Christa Maree Stephan |
| author_facet | Eugene Yee Hing Tang Jacob Brain Serena Sabatini Eduwin Pakpahan Louise Robinson Maha Alshahrani Aliya Naheed Mario Siervo Blossom Christa Maree Stephan |
| author_sort | Eugene Yee Hing Tang |
| collection | DOAJ |
| description | Dementia is a leading cause of disability and death globally. Individuals with diseases such as cardiovascular, cardiometabolic and cerebrovascular disease are often at increased dementia risk. However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. Yet, different disease groups may have unique risk factor profiles and tailored models that account for these differences may have enhanced predictive accuracy. In this review, we synthesise findings from three previous systematic reviews on dementia risk model development and testing to present an overview of the literature on dementia risk prediction modelling in people with a history of disease. Nine studies met the inclusion criteria. Currently, disease-specific models have only been developed in people with a history of diabetes where demographic, disease-specific and comorbidity data were used. Some existing risk models, including CHA<sub>2</sub>DS<sub>2</sub>-VASc and CHADS<sub>2</sub>, have been externally validated for dementia outcomes in those with atrial fibrillation and heart failure. One study developed a dementia risk model for their whole population, which had similar predictive accuracy when applied in a sub-sample with stroke. This emphasises the importance of considering disease status in identifying key predictors for dementia and generating accurate prediction models for dementia. |
| format | Article |
| id | doaj-art-0beddd93cdb5486d8a32b16eec6d18f4 |
| institution | DOAJ |
| issn | 2075-1729 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-0beddd93cdb5486d8a32b16eec6d18f42025-08-20T02:48:05ZengMDPI AGLife2075-17292024-11-011411148910.3390/life14111489Disease-Specific Risk Models for Predicting Dementia: An Umbrella ReviewEugene Yee Hing Tang0Jacob Brain1Serena Sabatini2Eduwin Pakpahan3Louise Robinson4Maha Alshahrani5Aliya Naheed6Mario Siervo7Blossom Christa Maree Stephan8Population Health Sciences Institute, Newcastle University, Newcastle NE2 4HH, UKInstitute of Mental Health, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UKSchool of Psychology, University of Surrey, Guildford GU2 7XH, UKApplied Statistics Research Group, Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UKPopulation Health Sciences Institute, Newcastle University, Newcastle NE2 4HH, UKDementia Centre of Excellence, Curtin enAble Institute, Curtin University, Perth, WA 6102, AustraliaNon Communicable Diseases, Nutrition Research Division, icddr,b, Mohakhali, Dhaka 1000, BangladeshDementia Centre of Excellence, Curtin enAble Institute, Curtin University, Perth, WA 6102, AustraliaInstitute of Mental Health, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UKDementia is a leading cause of disability and death globally. Individuals with diseases such as cardiovascular, cardiometabolic and cerebrovascular disease are often at increased dementia risk. However, while numerous models have been developed to predict dementia, they are often not tailored to disease-specific groups. Yet, different disease groups may have unique risk factor profiles and tailored models that account for these differences may have enhanced predictive accuracy. In this review, we synthesise findings from three previous systematic reviews on dementia risk model development and testing to present an overview of the literature on dementia risk prediction modelling in people with a history of disease. Nine studies met the inclusion criteria. Currently, disease-specific models have only been developed in people with a history of diabetes where demographic, disease-specific and comorbidity data were used. Some existing risk models, including CHA<sub>2</sub>DS<sub>2</sub>-VASc and CHADS<sub>2</sub>, have been externally validated for dementia outcomes in those with atrial fibrillation and heart failure. One study developed a dementia risk model for their whole population, which had similar predictive accuracy when applied in a sub-sample with stroke. This emphasises the importance of considering disease status in identifying key predictors for dementia and generating accurate prediction models for dementia.https://www.mdpi.com/2075-1729/14/11/1489dementiarisk factorscomorbidity |
| spellingShingle | Eugene Yee Hing Tang Jacob Brain Serena Sabatini Eduwin Pakpahan Louise Robinson Maha Alshahrani Aliya Naheed Mario Siervo Blossom Christa Maree Stephan Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review Life dementia risk factors comorbidity |
| title | Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review |
| title_full | Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review |
| title_fullStr | Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review |
| title_full_unstemmed | Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review |
| title_short | Disease-Specific Risk Models for Predicting Dementia: An Umbrella Review |
| title_sort | disease specific risk models for predicting dementia an umbrella review |
| topic | dementia risk factors comorbidity |
| url | https://www.mdpi.com/2075-1729/14/11/1489 |
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