PDF(Pubmed)
Abstract：
Alzheimer\'s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the \'severity index\' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI).
We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk \"disease-like\" or low-risk \"CN-like\". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.
In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).
When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.
We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk \"disease-like\" or low-risk \"CN-like\". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data.
In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57).
When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.
摘要：
背景：阿尔茨海默病（AD）是一种进行性神经退行性疾病，其病理生理变化在临床症状发作前几十年开始。使用结构MRI和多变量数据分析对脑萎缩模式的分析是识别患有主观认知下降（SCD）的患者进展为AD痴呆的高风险的有效工具。从训练过的模型中获得的萎缩模式对晚期AD和正常受试者进行分类，对于早期的受试者来说可能不是最佳的，比如SCD.在这项研究中,我们比较了使用"严重程度指数"预测SCD进展的准确性,该指数是在AD痴呆患者身上训练的标准分类模型与在遗忘型轻度认知障碍(aMCI)的β-淀粉样蛋白(Aβ)阳性患者身上训练的新模型.
方法：我们使用了来自瑞典BioFINDER-1研究队列的504例患者的结构MRI数据（认知正常（CN），Aβ-阴性=220；SCD，Aβ阳性和阴性=139；aMCI，Aβ阳性=106；AD痴呆=39）。我们应用多变量数据分析来创建两个预测模型，这些模型被训练来区分CN个体与Aβ阳性aMCI或AD痴呆的个体。将模型应用于患有SCD的个体，将其萎缩模式分类为高风险“类疾病”或低风险“类CN”。使用8年的纵向数据评估临床轨迹和模型准确性。
结果：在预测从SCD到MCI或痴呆的进展方面，标准,基于痴呆症的模型，特异性达到100%，但灵敏度仅为10.6%，而新的,基于aMCI的模型，敏感性为72.3%,特异性为60.9%。基于aMCI的模型在预测从SCD到MCI或痴呆的进展方面具有优势，与基于痴呆症的模型（AUC=0.57）相比，达到更高的受试者工作特征曲线下面积（AUC=0.72；P=0.037）。
结论：当使用结构MRI数据预测从SCD到MCI或痴呆的转化时，与基于标准痴呆的模型相比,基于轻度萎缩(即aMCI)个体的预测模型可能提供更优越的临床价值.
方法：我们使用了来自瑞典BioFINDER-1研究队列的504例患者的结构MRI数据（认知正常（CN），Aβ-阴性=220；SCD，Aβ阳性和阴性=139；aMCI，Aβ阳性=106；AD痴呆=39）。我们应用多变量数据分析来创建两个预测模型，这些模型被训练来区分CN个体与Aβ阳性aMCI或AD痴呆的个体。将模型应用于患有SCD的个体，将其萎缩模式分类为高风险“类疾病”或低风险“类CN”。使用8年的纵向数据评估临床轨迹和模型准确性。
结果：在预测从SCD到MCI或痴呆的进展方面，标准,基于痴呆症的模型，特异性达到100%，但灵敏度仅为10.6%，而新的,基于aMCI的模型，敏感性为72.3%,特异性为60.9%。基于aMCI的模型在预测从SCD到MCI或痴呆的进展方面具有优势，与基于痴呆症的模型（AUC=0.57）相比，达到更高的受试者工作特征曲线下面积（AUC=0.72；P=0.037）。
结论：当使用结构MRI数据预测从SCD到MCI或痴呆的转化时，与基于标准痴呆的模型相比,基于轻度萎缩(即aMCI)个体的预测模型可能提供更优越的临床价值.
