Objective Due to high cost of amyloid imaging, its use of amyloid imaging to confirm amyloid pathology is limited in clinical practice. It is of importance to develop a model to predict cerebral amyloid positivity using clinical data obtained from a memory clinic.Methods A total of 410 participants who had symptom of subjective cognitive decline and underwent amyloid PET and apolipoprotein ε (APOE) genotyping were retrospectively enrolled from January 2016 to January 2019. Models for cerebral amyloid positivity prediction were developed in all subjects, mild cognitive impairment (MCI) subjects, and Alzheimer’s disease (AD) dementia subjects through multivariate logistic regression analysis. The performance of the models was assessed using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) values.Results Age, sex, years of education, body mass index (BMI), APOE4, and mini mental state examination score (MMSE) were selected for the final model for all subjects. The AUC value of the ROC curve was 0.775. Age, sex, years of education, BMI, and APOE4 were selected for the final model for MCI subjects. The AUC value was 0.735. Age, sex, years of education, BMI, APOE4, MMSE, and history of hypertension were selected for the final model for AD dementia subjects. The AUC value was 0.845.Conclusion This study found that models using clinical data can predict cerebral amyloid positivity according to cognitive status. These models can be useful as a screening tool predict cerebral amyloid deposition in cognitively impaired patients in a memory clinic.
Objective Due to high cost of amyloid imaging, its use of amyloid imaging to confirm amyloid pathology is limited in clinical practice. It is of importance to develop a model to predict cerebral amyloid positivity using clinical data obtained from a memory clinic.Methods A total of 410 participants who had symptom of subjective cognitive decline and underwent amyloid PET and apolipoprotein ε (APOE) genotyping were retrospectively enrolled from January 2016 to January 2019. Models for cerebral amyloid positivity prediction were developed in all subjects, mild cognitive impairment (MCI) subjects, and Alzheimer’s disease (AD) dementia subjects through multivariate logistic regression analysis. The performance of the models was assessed using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) values.Results Age, sex, years of education, body mass index (BMI), APOE4, and mini mental state examination score (MMSE) were selected for the final model for all subjects. The AUC value of the ROC curve was 0.775. Age, sex, years of education, BMI, and APOE4 were selected for the final model for MCI subjects. The AUC value was 0.735. Age, sex, years of education, BMI, APOE4, MMSE, and history of hypertension were selected for the final model for AD dementia subjects. The AUC value was 0.845.Conclusion This study found that models using clinical data can predict cerebral amyloid positivity according to cognitive status. These models can be useful as a screening tool predict cerebral amyloid deposition in cognitively impaired patients in a memory clinic.
INTRODUCTION METHODS RESULTS DISCUSSION
