Background: Quantitatively predicting the progression of Alzheimers disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores. Methods: Multivariate regression techniques were employed to model baseline multi-modal data (demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors) and future ADAS-cog scores. Prediction models were subjected to repeated cross-validation and the resulting mean absolute error and cross-validated correlation of the model assessed. Results: Prediction models on multi-modal data outperformed single modal data up to 36 months. Incorporating baseline ADAS-cog scores to prediction models marginally improved predictive performance. Conclusions: Future ADAS-cog scores were successfully estimated via predictive learning aiding clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

中文翻译：

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多模式预测学习对阿尔茨海默氏病的定量纵向预测

背景：连续预测个体中的阿尔茨海默氏病（AD）的进展，例如AD评估量表认知（ADAS-cog）分数，对于个性化方法是有益的，而不是将个体定性地分为广泛的疾病类别。我们假设多模式数据和预测性学习模型可用于纵向预测ADAS-cog分数。方法：采用多元回归技术对基线多模态数据（人口统计学，神经影像学和基于脑脊液的标志物以及遗传因素）和未来的ADAS-cog评分进行建模。对预测模型进行反复的交叉验证，并对所得模型的平均绝对误差和交叉验证的相关性进行评估。结果：在长达36个月的时间里，多模式数据的预测模型优于单模式数据。将基线ADAS-cog分数纳入预测模型可以略微提高预测性能。结论：通过预测性学习帮助临床医生成功地确定了下降风险更大的人，并在疾病早期阶段进行干预，并为参与AD临床试验的个体可能的未来疾病进展提供了成功的信息，从而成功评估了未来的ADAS-cog评分。