Predicting amyloid status using self‐report information from an online research and recruitment registry: The Brain Health Registry

Abstract

Introduction This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging. Methods Aβ positron emission tomography (PET) was obtained from multiple in‐clinic studies. Using logistic regression, we predicted Aβ using self‐report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross‐validated area under the curve (cAUC) evaluated the predictive performance. Results The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short‐Form, self‐reported Everyday Cognition, and self‐reported cognitive impairment. The cross‐validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations. Disucssion The findings suggest that a novel, online approach could aid in Aβ prediction.

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

“…Cogstate One Card Learning test; ONB_BS, Cogstate One Back test The range of AUC scores generated by the machine learning models used in this study in the absence of SP-ECog and CBB metrics are lower than AUC scores previously reported by Ashford et al for a statistical model that used logistic regression to analyze a similar set of features from the BHR 20. In particular, Ashford et al reported a cross-validated AUC score of 0.66, and we obtained an average AUC score of 0.587 and 0.586 for RF and SVM classifiers, respectively, based on a similar set of Figure1), which is similar to the score reported by Ashford et al Our results are also in line with Langford et al,24 which reported an AUC score of 0.598 when training an XGBoost classifier to predict Aβ status using data from remote, web-based testing.…”

“…Inclusion criteria for these studies have been described elsewhere. 20 were listed in the BHR as having dementia, 459 as having mild cognitive impairment (MCI), and 92 as cognitively unimpaired (CU) (8 participants had no information on cognitive status). The impairment level for this subgroup was clinician-rated for participants enrolled in the IDEAS study and self-reported for participants enrolled in the SFVAMC studies.…”

“…Several low-cost markers that may be predictive of Aβ status in the brain have been suggested previously, including demographics, apolipoprotein E (APOE) genotype, cognition, [7][8][9][10] polygenic risk scores, 11,12 magnetic resonance imaging (MRI), [13][14][15] plasma Aβ, [16][17][18] and other blood biomarkers. 16,19 In addition, Ashford et al 20 recently described a statistical model for predicting Aβ status that leveraged data collected in an online research and recruitment registry for cognitive aging, the Brain Health Registry (BHR). The data used by this model consisted solely of information collected by the BHR through online surveys from participants, demonstrating the feasibility of using low-cost, self-reported data to identify Aβ+ patients.…”

Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

“…To date, the most common candidate predictors considered for Aβ-positivity were age, APOE genotype and measures of cognition, largely because they are easier to collect with widely available standardized protocols. Of these, age has been the most common predictor of elevated brain Aβ followed by the APOE genotype (reviewed in Ashford et al (2021) ), consistent with the notion that after advanced age, APOE ε4 genotype is a major risk factor for developing AD ( Payami et al , 1997 ). Consistent with the prior knowledge, age and APOE genotype were important predictors of Aβ-positivity for ADNI CU and CI participants (cf.…”

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

“…A global effort is underway to establish various trial-ready registries like the Brain Health Registry (www.brainhealthregistry.org, accessed on 4 February 2022) to facilitate AD trial recruitment. Therefore, there are many algorithms that aim to evaluate online information for prescreening [36,37]. For instance, Extreme Gradient Boosting (XGBoost) [38] is a tree-based ML technique that gives larger weights to misclassified data points at each iteration.…”