ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2018
Computer-assisted prediction of clinical progression in the earliest stages of AD.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Long Non-Coding RNAs and Alzheimer's Disease: Towards Personalized Diagnosis.International journal of molecular sciences · 2024Article
- The criteria used to rule out mild cognitive impairment impact dementia incidence rates in subjective cognitive decline.Alzheimer's research & therapy · 2024Article
- Development and design of a diagnostic report to support communication in dementia: Co-creation with patients and care partners.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
- Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology.NeuroImage. Clinical · 2020Article
- Article
- Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project.PloS one · 2020Article
- The quantitative neuroradiology initiative framework: application to dementia.The British journal of radiology · 2019Review
- Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study.Alzheimer's research & therapy · 2019Article
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Authors and funding
21 authors.
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Abstract
introductionIndividuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression.
methodsWe included 674 patients with SCD (46% female, 64 ± 9 years, Mini-Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts.
resultsAfter 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8). DISCUSSION: We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable.
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