دورية أكاديمية
Assessment of RMTs for Discriminating Stages of Alzheimer's Disease.
| العنوان: | Assessment of RMTs for Discriminating Stages of Alzheimer's Disease. |
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| المؤلفون: | Lentzen, Manuel, Vairavan, Srinivasan, Curcic, Jelena, Atreya, Alankar, Hinds, Chris, Muurling, Marijn, Conde, Pauline, Grammatikopoulou, Margarita, Lazarou, Ioulietta, Nikolopoulos, Spiros, Coello, Neva, de Boer, Casper, Aarsland, Dag, Brem, Anna‐Katharine, Fröhlich, Holger |
| المصدر: | Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2023 Supplement 22, Vol. 19 Issue 22, p1-3, 3p |
| مستخلص: | Background: RADAR‐AD is a European project in the context of the Innovative Medicine Initiative (IMI) focusing on the earlier identification of patients at risk for developing Alzheimer's Disease (AD) via a panel of remote monitoring technologies (RMTs), including smartphone apps and wearable devices. Method: We examined the ability of 6 RMTs (Altoida, Axivity, Banking app, Fitbit, Physilog, and Mezurio) to distinguish between healthy controls (HC) and disease stages of preclinical (PreAD), prodromal (ProAD), and mild to moderate Alzheimer's disease (MildAD) based on 175 patients (interim analysis). We trained three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) in a pairwise setting (HC vs. PreAD, HC vs. ProAD, HC vs. MildAD, PreAD vs. ProAD, and ProAD vs. MildAD). Since the interim dataset is still limited, we performed repeated, stratified nested cross‐validation to get a robust performance estimate. Each classifier was trained with the features of the different devices and a set of baseline variables. The latter include a patient's gender, age, years of education, and body mass index (BMI) when physical conditions might play a role (Axivitiy, Fitbit, Physilog). In addition, we checked whether specific patterns of the study groups allowed discrimination of the different study groups based on the baseline variables alone. Therefore, we trained one Logistic Regression model with these variables and compared the performance of the other three models with this baseline. The models trained with the baseline and questionnaire‐based data served as the reference value in our benchmark that represents how well the discrimination of the different groups works with clinical tests. Result: Our preliminary data show that RMTs can identify patients already in a prodromal disease stage (AUC ∼69%, Figure 1). Furthermore, the pairwise combination of data from a banking app and an app monitoring functional cognitive abilities via an augmented reality game slightly increased our model's discriminative ability (Altoida ‐ Banking, Figure 2). The overall best performance was achieved when combining RMTs with the Amsterdam I‐ADL questionnaire. Conclusion: Our results demonstrate the potential of RMTs and the Amsterdam I‐ADL questionnaire for identifying patients in prodromal stage in primary care settings. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: | Supplemental Index |
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