受损或未受损:蒙特利尔认知评估在检测酒精使用障碍患者认知障碍方面的准确性。

IF 3 Q2 SUBSTANCE ABUSE
Kristoffer Høiland, Espen Kristian Ajo Arnevik, Lien My Diep, Tove Mathisen, Katie Witkiewitz, Jens Egeland
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引用次数: 0

摘要

背景:认知障碍在酒精使用障碍(AUD)中很常见,但只有少数研究调查了蒙特利尔认知评估(MoCA)在这一人群中的准确性。我们在 AUD 患者样本中检验了 MoCA 检测认知障碍的准确性和精确度。此外,我们还研究了MoCA是否能预测治疗的过早中断:方法:我们对 126 名在专科医疗机构接受治疗的 AUD 患者样本进行了 MoCA 和 12 项神经心理学测试。从参考测试中得出了五个认知领域。这些测试的综合总分被用作确定MoCA正确和错误分类的参考标准。我们分析了MoCA的最佳截断分数以及MoCA和参考测试之间分类的准确性和一致性:接收者操作特征曲线(ROC)分析得出的曲线下面积(AUC)为 0.77(95% CI [0.67, 0.87])。以 25 为临界值,MoCA 的灵敏度为 0.77,特异度为 0.62。PPV 为 0.53。NPV 为 0.84。以 24 分为临界值的灵敏度较低,为 0.60,但特异性明显较高,为 0.79,PPV 为 0.68。PPV 为 0.68。NPV 为 0.82。MoCA 与参考测试之间的 Kappa 一致度为中等偏上,25 分界点的 Kappa 一致度为 0.38,24 分界点的 Kappa 一致度为 0.44。MoCA不能预测治疗的中断:我们的研究结果表明,MoCA 在预测 AUD 认知功能障碍方面的分类准确性存在局限性。既要准确识别认知功能受损的病例,又不能包含过多的假阳性病例,要在这两者之间取得适当的平衡是非常具有挑战性的。此外,MoCA 无法预测治疗的中断。总之,研究结果不支持将 MoCA 作为一种省时的筛查工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Impaired or not impaired: The accuracy of the Montreal Cognitive Assessment in detecting cognitive impairment among patients with alcohol use disorder.

Impaired or not impaired: The accuracy of the Montreal Cognitive Assessment in detecting cognitive impairment among patients with alcohol use disorder.

Background: Cognitive impairments are common in alcohol use disorder (AUD), but only a few studies have investigated the accuracy of the Montreal Cognitive Assessment (MoCA) in this population. We examined the accuracy and precision of the MoCA in detecting cognitive impairment in a sample of patients with AUD. In addition, we investigated whether the MoCA predicts premature discontinuation from treatment.

Method: A sample of 126 persons with AUD undergoing treatment in specialist health services were administered the MoCA and a battery of 12 neuropsychological tests. Five cognitive domains were derived from the reference tests. A composite total score from these tests was used as a reference criterion for determining correct and incorrect classifications for the MoCA. We analyzed the optimal cut-off score for the MoCA and the accuracy and agreement of classification between the MoCA and the reference tests.

Results: Receiver operating characteristic (ROC) curve analyzes yielded an area under the curve (AUC) of 0.77 (95% CI [0.67, 0.87]). Applying 25 as the cut-off, MoCA sensitivity was 0.77 and specificity 0.62. The PPV was 0.53. The NPV was 0.84. Using a cut-off score of 24 yielded a lower sensitivity 0.60, but specificity was significantly better i.e., 0.79. PPV was 0.68. The NPV was 0.82. Kappa agreement between MoCA and the reference tests was fair to moderate, 0.38 for the cut-off of 25, and 0.44 for the cut-off of 24. MoCA did not predict discontinuation from treatment.

Conclusions: Our findings indicate limitations in the classification accuracy of the MoCA in predicting cognitive impairment in AUD. Achieving the right balance between accurately identifying impaired cases without including too many false positives can be challenging. Further, MoCA does not predict discontinuation from treatment. Overall, the results do not support MoCA as a time-efficient screening instrument.

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