ArticleBMC neurology2021
Construction of a risk prediction model for Alzheimer's disease in the elderly population.
Article in BMC neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 41 citations in OpenAlex.
- Toward Intelligent Rehabilitation Program Management: A System-Level Review of AI Architectures.Bioengineering (Basel, Switzerland) · 2026Review
- Predicting mild cognitive impairment among rural older adults in China: development and validation of a risk prediction model.BMC geriatrics · 2026Article
- Cross-Linguistic and Multicultural Effects on Animal Fluency Performance in Persons With Aphasia.American journal of speech-language pathology · 2025Article
- Nomogram for suicidal ideation and non-suicidal self-injury risk assessment in Dehong District nursing staff: a cross-sectional study.BMJ open · 2025Article
- Article
- Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.Alzheimer's research & therapy · 2024Article
- Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk.Scientific reports · 2024Article
- Profiles of subgingival microbiomes and gingival crevicular metabolic signatures in patients with amnestic mild cognitive impairment and Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Development and validation of risk of CPS decline (RCD): a new prediction tool for worsening cognitive performance among home care clients in Canada.BMC geriatrics · 2023Article
- Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.Age and ageing · 2023Article
- Going beyond established model systems of Alzheimer's disease: companion animals provide novel insights into the neurobiology of aging.Communications biology · 2023Review
- From imbalance to impairment: the central role of reactive oxygen species in oxidative stress-induced disorders and therapeutic exploration.Frontiers in pharmacology · 2023Review
- Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model.Frontiers in pediatrics · 2023Article
- Correlation between Mild Cognitive Impairment and Sarcopenia: The Prospective Role of Lipids and Basal Metabolic Rate in the Link.Nutrients · 2022Article
- A model to predict a risk of allergic rhinitis based on mitochondrial DNA copy number.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2022Article
- Article
- Potential Mechanism Underlying Exercise Upregulated Circulating Blood Exosome miR-215-5p to Prevent Necroptosis of Neuronal Cells and a Model for Early Diagnosis of Alzheimer's Disease.Frontiers in aging neuroscience · 2022Article
- A lightweight machine learning tool for Alzheimer's disease prediction.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDementia is one of the greatest global health and social care challenges of the twenty-first century. The etiology and pathogenesis of Alzheimer's disease (AD) as the most common type of dementia remain unknown. In this study, a simple nomogram was drawn to predict the risk of AD in the elderly population.
methodsNine variables affecting the risk of AD were obtained from 1099 elderly people through clinical data and questionnaires. Least Absolute Shrinkage Selection Operator (LASSO) regression analysis was used to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 9 predictor variables (nomogram-see precise definition in the text) was drawn to predict the risk of AD in the elderly population. In addition, calibration diagram, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to verify the model.
resultsSix predictors namely sex, age, economic status, health status, lifestyle and genetic risk were identified by LASSO regression analysis of nine variables (body mass index, marital status and education level were excluded). The area under the ROC curve in the training set was 0.822, while that in the validation set was 0.801, suggesting that the model built with these 6 predictors showed moderate predictive ability. The DCA curve indicated that a nomogram could be applied clinically if the risk threshold was between 30 and 40% (30 to 42% in the validation set).
conclusionThe inclusion of sex, age, economic status, health status, lifestyle and genetic risk into the risk prediction nomogram could improve the ability of the prediction model to predict AD risk in the elderly patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.