ArticleNeuroImage. Clinical2019
Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- Trial
- Platelet proteomic signatures of amyloid β-positive mild cognitive impairment and Alzheimer's disease.Molecular brain · 2026Article
- Should all MCI with Alzheimer's biological diagnosis receive anti-amyloid therapy?Cell death & disease · 2026Review
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Comprehensive Clinical and Behavioral Characteristics of Mild Cognitive Impairment According to Amyloid Positivity: A Large Multi-Center Korean Cohort.Dementia and neurocognitive disorders · 2025Article
- Comparing the predictive validity of four MCI definitions for incident dementia in demographically diverse community-dwelling individuals: Results from the Einstein Aging Study (EAS).Journal of the International Neuropsychological Society : JINS · 2025Article
- Elucidating the Link Between Anxiety/Depression and Alzheimer's Dementia in the Australian Imaging Biomarkers and Lifestyle (AIBL) Study.Journal of epidemiology and global health · 2024Article
- Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.PLOS digital health · 2024Article
- A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting.Journal of Alzheimer's disease reports · 2024Article
- Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients.Current Alzheimer research · 2024Article
- Weight Change and Neuropsychiatric Symptoms in Alzheimer's Disease and Frontotemporal Dementia: Associations with Cognitive Decline.Journal of Alzheimer's disease reports · 2023Article
- Cognitive trajectories preluding the imminent onset of Alzheimer's disease dementia in individuals with normal cognition: results from the HELIAD cohort.Aging clinical and experimental research · 2023Article
- The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review.Molecular neurobiology · 2022Review
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
- Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling.NeuroImage. Clinical · 2022Article
- Grey matter network markers identify individuals with prodromal Alzheimer's disease who will show rapid clinical decline.Brain communications · 2022Article
- The Clinical Course of Early and Late Mild Cognitive Impairment.Frontiers in neurology · 2022Article
- Cross-Sectional and Longitudinal Hippocampal Atrophy, Not Cortical Thinning, Occurs in Amyloid-Negative, p-Tau-Positive, Older Adults With Non-Amyloid Pathology and Mild Cognitive Impairment.Frontiers in neuroimaging · 2022Article
- A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation.Alzheimer's research & therapy · 2021Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
It may be possible to classify patients with Aβ positive (+) mild cognitive impairment (MCI) into fast and slow decliners according to their biomarker status. In this study, we aimed to develop a risk prediction model to predict fast decline in the Aβ+ MCI population using multimodal biomarkers. We included 186 Aβ+ MCI patients who underwent florbetapir PET, brain MRI, cerebrospinal fluid (CSF) analyses, and FDG PET at baseline. We defined conversion to dementia within 3 years (= fast decline) as the outcome. The associations of potential covariates (MCI stage, APOE4 genotype, corrected hippocampal volume (HV), FDG PET SUVR, AV45 PET SUVR, CSF Aβ, total tau (t-tau), and phosphorylated tau (p-tau)) with the outcome were tested and nomograms were constructed using logistic regression models in the training dataset (n=124, n of fast decliners=52). The model was internally validated with the testing dataset (n=62, n of fast decliners=22). The multivariable analysis (including CSF t-tau) showed that MCI stage (late MCI vs. early MCI; OR 15.88, 95% CI 4.59, 54.88), APOE4 (OR 5.65, 95% CI 1.52, 20.98), corrected HV*1000 (OR 0.22, 95% CI 0.09, 0.57), FDG SUVR*10 (OR 0.43, 95% CI 0.27, 0.71), and log
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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.