Department of Neuropsychiatry, Towada City Hospital, Towada, Aomori, Japan.
Department of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Tochigi, Japan.
PLoS One. 2021 Feb 22;16(2):e0247427. doi: 10.1371/journal.pone.0247427. eCollection 2021.
Alzheimer's disease (AD) is assessed by carefully examining a patient's cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice.
We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved.
The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities.
Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings.
阿尔茨海默病(AD)的评估需要仔细检查患者的认知障碍。然而,之前的研究报告称,在初级保健环境中,痴呆的诊断准确性不足。许多医院使用称为基于体素的特定区域分析系统阿尔茨海默病(VSRAD)的自动定量评估方法,其中使用脑 MRI 数据评估与 AD 相关的脑形态异常。同样,广泛使用称为简易 Z 评分成像系统(eZIS)的自动定量评估应用程序,该程序使用脑 SPECT 数据检测与 AD 相关的局部脑血流减少。这些应用程序有几个指标,每个指标都与 AD 的程度相关。然而,在实际临床实践中,这些指标联合使用是否效果更好尚不完全清楚。
我们纳入了 112 例轻度认知障碍(MCI)和 128 例早期 AD 患者。所有患者均接受 MRI、SPECT 和简易精神状态检查(MMSE)。采用单变量分析评估人口统计学和临床特征,并进行 MMSE 评分与 VSRAD 和 eZIS 指标相结合的逻辑回归分析,以验证在区分 MCI 和早期 AD 方面,诊断准确性是否有所提高。
MMSE 评分单独的受试者工作特征曲线下面积(AUC)为 0.835。通过将 MMSE 评分与 VSRAD 和 eZIS 两个定量指标相结合,评估脑异常程度,AUC 显著提高至 0.870。
与 MMSE 评分单独相比,将 MMSE 评分与 VSRAD 和 eZIS 指标相结合可显著提高区分 MCI 和早期 AD 患者的准确性。实施 VSRAD 和 eZIS 不需要在痴呆治疗方面具备专业的临床经验。因此,在实际的基层医疗保健环境中,医生对痴呆的诊断准确性可能会轻易提高。