ArticleCNS neuroscience & therapeutics2020
Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model.
Article in CNS neuroscience & therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia.The Cochrane database of systematic reviews · 2023Pooled it
- Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation.Frontiers in aging neuroscience · 2021Pooled it
- Psilocybin for dementia prevention? The potential role of psilocybin to alter mechanisms associated with major depression and neurodegenerative diseases.Pharmacology & therapeutics · 2024Review
- Sleep-associated insulin resistance promotes neurodegeneration.Molecular biology reports · 2023Review
- Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model.CNS neuroscience & therapeutics · 2020Article
- Parkinson´s and Alzheimer´s Diseases and Natural Products: Pathologies and Medication of the New Times.Current neuropharmacologyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsBoth amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population.
methodsThe CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.
resultsAD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R
conclusionsThe CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of 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.