Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.

中文翻译：

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预测阿尔茨海默氏症的病程。

阿尔茨海默氏病是最常见的神经退行性疾病，其特征是淀粉样β肽的积累导致脑中斑块和tau蛋白缠结的形成。这些神经病理学特征在认知障碍和阿尔茨海默氏痴呆症发生之前已有很多年。为了更好地了解和预测从早期无症状到晚期痴呆的病程，研究多种标记物的进展模式至关重要。特别是，我们的目标是仅对标记物进行一次观察，即可预测其未来可能的发展过程。改善个人水平的预测可能会导致改善临床护理和临床试验。我们提出了一种用于建模和预测认知，功能，大脑成像，体液生物标志物，同时使用多个域的个人诊断。在第一阶段，联合（或多元）混合效果模型用于随时间推移同时对多个标记进行建模。在第二阶段中，基于第一阶段模型，根据连续标记的预测，使用随机森林来预测分类诊断（认知正常，轻度认知障碍或痴呆）。两种模型的结合使人们可以利用其关键优势来获得更高的准确性。我们使用阿尔茨海默氏病神经影像学计划的数据来表征这种两阶段方法的预测准确性。与对每个连续结果使用单独的单变量混合效果模型相比，对所有连续结果使用单个联合混合效果模型的两阶段方法可产生更好的诊断分类准确性。在2.5年内，总体预测精度达到80％以上。结果还表明，与仅使用单个域的标记相比，在预测算法中使用来自多个评估域的标记（例如认知，功能和脑成像）时，总体准确性得到了改善。