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Abstract
Background and Objectives To 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.
Methods We 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 APOE4 status. Performance of the risk scores was evaluated in the independent test set.
Results PARS 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 APOE4 information, had an AUC of 0.73 (0.70–0.76). PARS model 3 showed the highest, but still moderate, performance metrics in comparison with other models with sensitivity of 72.0% (67.6%–76.4%), specificity of 62.1% (58.8%–65.4%), accuracy of 65.3% (62.7%–68.0%), and positive predictive value of 48.1% (44.1%–52.1%).
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.
Glossary
- A4=
- Anti-Amyloid Treatment in Asymptomatic Alzheimer's;
- Aβ=
- β-amyloid;
- AD=
- Alzheimer disease;
- ADCS-ADL=
- Alzheimer's Disease Cooperative Study–Activities of Daily Living;
- AUC=
- area under the receiver operating characteristic curve;
- BMI=
- body mass index;
- CN=
- cognitively normal;
- FCSRT=
- Free and Cued Selective Reminding Test;
- FR=
- free recall;
- fRF=
- full random forest;
- GDS=
- Geriatric Depression Scale;
- LONI=
- Laboratory of NeuroImaging;
- MACQ=
- Memory Complaint Questionnaire;
- MCC=
- Matthews correlation coefficient;
- MCI=
- mild cognitive impairment;
- MMSE=
- Mini-Mental State Examination;
- NPV=
- negative predictive value;
- PACC=
- Preclinical Alzheimer Cognitive Composite;
- PARS=
- Positive Aβ Risk Score;
- PPV=
- positive predictive value;
- RF=
- random forest;
- RFR=
- random forest regression;
- ROC=
- receiver operating characteristic;
- SUVR=
- standardized uptake value ratio;
- vRF=
- random forest with variable selection
Footnotes
Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Class of Evidence: NPub.org/coe
Editorial, page 999
- Received August 23, 2021.
- Accepted in final form March 2, 2022.
- © 2022 American Academy of Neurology
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