Inter-cohort staging efficacy of gaussian process progression model for Alzheimer's disease Neuroimaging / Optimal neuroimaging measures for tracking disease progression
Inter-cohort staging efficacy of gaussian process progression model for Alzheimer's disease Neuroimaging / Optimal neuroimaging measures for tracking disease progression
复制标题
阿尔茨海默病高斯过程进展模型的队列间分期功效神经影像学/跟踪疾病进展的最佳神经影像学措施
DOI：
10.1002/alz.043246
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Archetti D
中科院分区：
文献类型：
--
作者：
Archetti D
了解从轻度认知障碍（MCI）到阿尔茨海默病（AD）的路径，可以深入了解痴呆症的病理生理学，并可以为临床试验中的患者分层提供信息。我们的目标是训练用于估计平均疾病轨迹的统计计算模型，以便更好地了解患者如何根据ADNI人群的生物标志物进化，然后在PharmaCog队列的MCI受试者上验证该模型。方法通过高斯过程进展模型（GPPM） （Lorenzi et al. 2017, DOI: 10.1016/j.n neuroimage.2017.08.059）建立疾病轨迹，该模型训练来自ADNI数据库中338名受试者的纵向生物标志物数据，这些受试者在撰写本文时已从MCI转换为AD。利用认知评分、脑脊液和T13D磁共振成像等相关生物标志物建立疾病模型。通过PharmaCog数据库中的139名MCI受试者进行验证，其中20人根据进展模型时间表进展为AD。为了模拟临床环境，仅使用生物标志物的基线横截面测量来对药理学受试者进行分期。采用敏感性、特异性、平衡准确性和曲线下面积（AUC）来衡量分期表现。结果药理学研究对象的分期在疾病时间线上显示MCI稳定（sMCI）和MCI进展（pMCI）的明显区别。smci的平均分期时间为72.7±3.6年，而pMCIs的分期时间为3年后（p值<0.01），为75.5±2.8年。sMCI与pMCI受试者的分类结果灵敏度为0.68，特异性为0.85，平衡准确度为0.77，ROC曲线（图1）的AUC为0.75。我们使用基于研究数据（ADNI）训练的数据驱动的AD进展计算模型，在临床数据集（PharmaCog）中识别出MCI转化为AD。分类性能与其他数据驱动工具相当（Young等人，2014,DOI: 10.1093/brain/awu176），但在更具挑战性的临床环境中执行。这验证了这些模型的分期效果，并显示了它们在未来医疗保健和临床试验中的应用。
BackgroundUnderstanding the path that leads from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) provides insights into dementia pathophysiology and can inform patient stratification in clinical trials. Our objective is to train statistical computational models for estimating an average disease trajectory to better understand how patients evolve on the basis of biomarkers of from the ADNI population, then validate the model on MCI subjects from the PharmaCog cohort.MethodThe disease trajectory was built via a Gaussian Process Progression Model (GPPM) (Lorenzi et al. 2017, DOI: 10.1016/j.neuroimage.2017.08.059) trained on longitudinal biomarker data from 338 subjects in the ADNI database who had converted from MCI to AD at the time of writing. Biomarkers related to cognitive scores, cerebrospinal fluid and T13D magnetic resonance imaging were used to build the disease model. Validation was performed by staging 139 MCI subjects from the PharmaCog database, 20 of whom progressed to AD, along the progression model timeline. Only baseline cross sectional measures of biomarkers were used to stage PharmaCog subjects in order to simulate clinical settings. Measures of sensitivity, specificity, balanced accuracy and area under curve (AUC) were used to measure the staging performance.ResultThe staging of PharmaCog subjects shows a clear separation between MCI stable (sMCI) subjects and MCI progressors (pMCI) on the disease timeline. On average, sMCIs were staged at year 72.7±3.6 while pMCIs are staged 3 years later (p‐value<0.01) at year 75.5±2.8. Classification of sMCI vs pMCI subjects returned a sensitivity equal to 0.68, specificity equal to 0.85, balanced accuracy equal to 0.77 and the ROC curve (Figure 1) had AUC equal to 0.75.ConclusionWe identified MCI converters to AD in a clinical data set (PharmaCog) using a data‐driven computational model of AD progression trained on research data (ADNI). Classification performance was comparable to other data‐driven tools (Young et al., 2014, DOI: 10.1093/brain/awu176), but performed in a more challenging clinical setting. This validates the staging efficacy of such models and shows their utility for future application in healthcare and clinical trials.