IRCCS SYNLAB SDN, Naples, Italy.
Department of Medicine, Surgery and Health Sciences, Rehabilitation Unit, Trieste University Hospital-ASUGI, University of Trieste, Trieste, Italy.
J Alzheimers Dis. 2024;101(1):293-308. doi: 10.3233/JAD-240339.
In the era of disease-modifying therapies, empowering the clinical neuropsychologist's toolkit for timely identification of mild cognitive impairment (MCI) is crucial.
Here we examine the clinimetric properties of the Montreal Cognitive Assessment (MoCA) for the early diagnosis of MCI due to Alzheimer's disease (MCI-AD).
Data from 48 patients with MCI-AD and 47 healthy controls were retrospectively analyzed. Raw MoCA scores were corrected according to the conventional Nasreddine's 1-point correction and demographic adjustments derived from three normative studies. Optimal cutoffs were determined while previously established cutoffs were diagnostically reevaluated.
The original Nasreddine's cutoff of 26 and normative cutoffs (non-parametric outer tolerance limit on the 5th percentile of demographically-adjusted score distributions) were overly imbalanced in terms of Sensitivity (Se) and Specificity (Sp). The optimal cutoff for Nasreddine's adjustment showed adequate clinimetric properties (≤23.50, Se = 0.75, Sp = 0.70). However, the optimal cutoff for Santangelo's adjustment (≤22.85, Se = 0.65, Sp = 0.87) proved to be the most effective for both screening and diagnostic purposes according to Larner's metrics. The results of post-probability analyses revealed that an individual testing positive using Santangelo's adjustment combined with a cutoff of 22.85 would have 84% post-test probability of receiving a diagnosis of MCI-AD (LR+ = 5.06).
We found a common (mal)practice of bypassing the applicability of normative cutoffs in diagnosis-oriented clinical practice. In this study, we identified optimal cutoffs for MoCA to be allocated in secondary care settings for supporting MCI-AD diagnosis. Methodological and psychometric issues are discussed.
在疾病修饰疗法时代，为临床神经心理学家提供及时识别轻度认知障碍（MCI）的工具包至关重要。
本研究旨在检验蒙特利尔认知评估量表（MoCA）在诊断阿尔茨海默病相关 MCI（MCI-AD）中的临床计量学特性。
回顾性分析了 48 例 MCI-AD 患者和 47 例健康对照者的数据。根据常规的 Nasreddine 1 点校正和来自三项正态研究的人口统计学校正，对原始 MoCA 评分进行校正。在确定最佳截断值的同时，还对之前建立的截断值进行了诊断性重新评估。
原始 Nasreddine 截断值为 26 分和正态截断值（根据人口统计学调整后得分分布的第 5 个百分位数的非参数外部容忍限）在敏感性（Se）和特异性（Sp）方面存在严重不平衡。Nasreddine 校正的最佳截断值显示出良好的临床计量学特性（≤23.50，Se=0.75，Sp=0.70）。然而，根据 Larner 的指标，Santangelo 校正的最佳截断值（≤22.85，Se=0.65，Sp=0.87）对于筛查和诊断目的均是最有效的。后验概率分析的结果表明，使用 Santangelo 校正和 22.85 截断值检测为阳性的个体，其 MCI-AD 的后验诊断概率为 84%（LR+ = 5.06）。
我们发现，在以诊断为导向的临床实践中，存在一种常见的（错误）做法，即绕过正态截断值的适用性。在这项研究中，我们确定了 MoCA 的最佳截断值，以在二级保健机构中支持 MCI-AD 的诊断。讨论了方法学和心理计量学问题。
