CNS neuroscience & therapeutics · 2020
Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model.
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Lu Xiang, Chen Jiu, Shu Hao, Wang Zan, Shi Yong-Mei, Yuan Yong-Gui, Xie Chun-Ming, Liao Wen-Xiang, Su Fan, Shi Ya-Chen, Zhang Zhi-Jun
Аннотация
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 high risk for AD development either in an independent aMCI population or in an rLOD population.
METHODS: The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.
RESULTS: AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2 = .17, P < .001) at baseline in the combined high-risk population (N = 72).
CONCLUSIONS: The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.
METHODS: The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.
RESULTS: AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2 = .17, P < .001) at baseline in the combined high-risk population (N = 72).
CONCLUSIONS: The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.