Predicting Brain Amyloid PET Positivity Using the Amyloid Beta Composite (ABC) Index in Patients With Cognitive Impairment

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Abstract
Background and Purpose: Amyloid positron emission tomography (PET) is a crucial diagnostic tool for Alzheimer's disease (AD), but its application is constrained by cost and accessibility. This study aimed to create a practical composite index to predict cerebral amyloid positivity in patients with cognitive impairment. Methods: We included patients with mild cognitive impairment or early-stage AD who underwent amyloid PET. Various combinations of clinical and imaging variables were assessed through receiver operating characteristic analysis to identify the optimal model for predicting amyloid positivity. The Amyloid Beta Composite (ABC) index, a risk scoring model, was developed using logistic regression and a weighted scoring system. We evaluated the ABC index's performance based on accuracy, sensitivity, and specificity, and conducted internal validation using a chronological split-sample from the same cohort. Results: We analyzed a total of 223 patients. The best-performing model incorporated five variables: Mini-Mental State Examination (MMSE), secondary memory recall from the modified MMSE, Clinical Dementia Rating-Sum of Boxes, medial temporal lobe atrophy, and apolipoprotein E genotype. This model demonstrated excellent performance in the development group (area under the curve [AUC], 0.88; 95% confidence interval, 0.82-0.94). In the validation group, the ABC index achieved an AUC of 0.74, with an accuracy of 0.70, sensitivity of 0.63, and specificity of 0.78. Conclusions: The ABC index, utilizing commonly accessible clinical data, serves as a simple and practical screening tool for predicting cerebral amyloid deposition. It may aid in patient selection for amyloid PET, anti-amyloid therapies, and clinical trials, thereby reducing unnecessary imaging.

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