LAUSR

OBJECTIVE To develop and test the performance of Positive Aβ Risk Score (PARS) for prediction of Aβ positivity in cognitively unimpaired individuals for use in clinical research. BACKGROUND Detecting β-amyloid (Aβ) positivity is essential for identifying those at-risk individuals who are candidates for early intervention with amyloid targeted treatments. METHODS We used data from 4134 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 ADL-PI, Geriatric Depression Scale (GDS), and Memory Complaint Questionnaire), objective measures (free recall, MMSE, immediate recall, digit symbol substitution, and delayed logical memory scores), and APOE4 status. Performance of the risk scores were evaluated in the independent test set. RESULTS PARS Model-1 included age, BMI, and family history and had an 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, yet 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%). CONCLUSION PARS Models are a set of simple and practical risk scores which 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. Additionally, this approach can be followed with 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 AB positivity with moderate accuracy.