Predicting conversion to Alzheimer's disease among individual high‐risk patients using the Characterizing AD Risk Events index model

• DOI: 10.1111/cns.13371
• Publication Date: 2020-04-03T15:58:00Z
• Authors: Xiang Lu; Jiu Chen; Hao Shu; Zan Wang; Yong‐mei Shi; Yong‐gui Yuan; Chun‐ming Xie; Wen‐xiang Liao; Fan Su; Ya‐chen Shi; Zhi‐jun Zhang
• Keywords: Male; Models, Neurological; Original Articles; Middle Aged; Magnetic Resonance Imaging; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; Alzheimer Disease; Predictive Value of Tests; Risk Factors; Disease Progression; Humans; Cognitive Dysfunction; Female; Aged; Follow-Up Studies

Abstract Aims Both amnestic mild cognitive impairment (aMCI) and remitted late‐onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at for development either an independent aMCI population or rLOD population. Methods The CARE was constructed based on event‐based probabilistic framework fusion biomarkers differentiate progressing from cognitively stable (27 subjects, 6 progressive subjects) (29 10 during follow‐up period. Results diagnoses were predicted with balanced accuracy 80.6%, sensitivity 83.3%, specificity 77.8%. They also 74.5%, 80.0%, 69.0%. In addition, scores observed be negatively correlated composite Z episodic memory ( R 2 = .17, P < .001) baseline combined high‐risk (N 72). Conclusions used prediction both populations effectively. Additionally, it monitor severity patients.