ArticleNeurology2022
Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data.
Article in Neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Predicting Brain Amyloid PET Positivity Using the Amyloid Beta Composite (ABC) Index in Patients With Cognitive Impairment.Dementia and neurocognitive disorders · 2026Article
- Prognostic value of plasma glial fibrillary acidic protein in cognitively unimpaired older adults: Results from the A4 study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- AI-driven fusion of multimodal data for Alzheimer's disease biomarker assessment.Nature communications · 2025Article
- Tau Pathology in the Medial Temporal Lobe and Neocortex: Implications for Cognitive Unimpaired in Cognitively Unimpaired Older Adults.Balkan medical journal · 2025Article
- Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.Human brain mapping · 2025Article
- Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.Alzheimer's research & therapy · 2025Observational
- Development and validation of a machine learning-based nomogram for preoperative prediction of laparoscopic surgical difficulty in gallstone patients.Translational gastroenterology and hepatology · 2025Article
- Article
- Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.The journal of prevention of Alzheimer's disease · 2024Article
- Predicting amyloid-beta pathology in the general population.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.Radiology · 2023Article
- Association of Stages of Objective Memory Impairment With Incident Symptomatic Cognitive Impairment in Cognitively Normal Individuals.Neurology · 2023Article
- Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach.PloS one · 2023Article
- AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes.BMC medical research methodology · 2022Article
- Association of item-level responses to cognitive function index with tau pathology and hippocampal volume in the A4 Study.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
BACKGROUND AND
objectivesTo develop and test the performance of the Positive Aβ Risk Score (PARS) for prediction of β-amyloid (Aβ) positivity in cognitively unimpaired individuals for use in clinical research. Detecting Aβ positivity is essential for identifying at-risk individuals who are candidates for early intervention with amyloid targeted treatments.
methodsWe used data from 4,134 cognitively normal individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study. The sample was divided into training and test sets. A modified version of AutoScore, a machine learning-based software tool, was used to develop a scoring system using the training set. Three risk scores were developed using candidate predictors in various combinations from the following categories: demographics (age, sex, education, race, family history, body mass index, marital status, and ethnicity), subjective measures (Alzheimer's Disease Cooperative Study Activities of Daily Living-Prevention Instrument, Geriatric Depression Scale, and Memory Complaint Questionnaire), objective measures (free recall, Mini-Mental State Examination, immediate recall, digit symbol substitution, and delayed logical memory scores), and
resultsPARS model 1 included age, body mass index (BMI), and family history and had an area under the curve (AUC) of 0.60 (95% CI 0.57-0.64). PARS model 2 included free recall in addition to the PARS model 1 variables and had an AUC of 0.61 (0.58-0.64). PARS model 3, which consisted of age, BMI, and DISCUSSION: PARS models are a set of simple and practical risk scores that may improve our ability to identify individuals more likely to be amyloid positive. The models can potentially be used to enrich trials and serve as a screening step in research settings. This approach can be followed by the use of additional variables for the development of improved risk scores. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in cognitively unimpaired individuals PARS models predict Aβ positivity with moderate accuracy.
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Registered trials
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.