ArticleJournal of Alzheimer's disease : JAD2018
A 'Framingham-like' Algorithm for Predicting 4-Year Risk of Progression to Amnestic Mild Cognitive Impairment or Alzheimer's Disease Using Multidomain Information.
Article in Journal of Alzheimer's disease : JAD, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients.Current Alzheimer research · 2024Article
- Alzheimer's Disease: Epidemiology and Clinical Progression.Neurology and therapy · 2022Review
- Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Article
- 2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework.European journal of nuclear medicine and molecular imaging · 2021Review
- Comparison of the predictive accuracy of multiple definitions of cognitive impairment for incident dementia: a 20-year follow-up of the Whitehall II cohort study.The lancet. Healthy longevity · 2021Article
- Development of a Sex-Specific Risk Scoring System for the Prediction of Cognitively Normal People to Patients With Mild Cognitive Impairment (SRSS-CNMCI).Frontiers in aging neuroscience · 2021Article
- The impact of mild cognitive impairment on healthcare utilization and costs: A UK Biobank study.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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7 authors.
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Abstract
backgroundThere are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer's disease (AD).
objectiveUse ADNI data to develop a 'Framingham-like' prediction model for a 4-year period.
methodsWe developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful.
resultsThere were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk.
conclusionOur parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.
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