Robust and conventional neuropsychological norms: Diagnosis and prediction of age-related cognitive decline

Robust and conventional neuropsychological norms: Diagnosis and prediction of age-related cognitive decline
复制标题

DOI:
10.1037/0894-4105.22.4.469
复制
发表时间:
2008-07-01
期刊:
影响因子:
2.4
通讯作者:
Reisberg, Barry
Reisberg, Barry
中科院分区:
心理学3区
文献类型:
--
作者:
De Santi, Susan;Pirraglia, Elizabeth;Reisberg, Barry

文献摘要

被引文献

相似文献

该研究的目的是比较稳健型和常规型神经心理学常模在预测健康成年人以及轻度认知障碍(MCI)患者临床衰退方面的表现。作者从113名至少4年保持正常诊断的健康参与者中制定了稳健的基线横断面和纵向变化常模。为256名类似的无后续随访的健康参与者单独创建了基线常规常模。在一个纵向研究的独立队列中对常规常模和稳健常模进行了测试,该队列包括健康参与者(n = 223)、MCI患者(n = 136)和阿尔茨海默病(AD,n = 162)患者;84名健康参与者衰退为MCI或AD(正常 -> 衰退),44名MCI患者衰退为AD(MCI -> AD)。与常规常模相比,基线稳健常模能更准确地识别出在延迟记忆和注意力 - 语言领域存在障碍的从正常衰退为MCI或AD的更高比例的个体。两种常模都能预测从MCI到AD的衰退。延迟记忆和注意力 - 语言的变化常模显著提高了基线分类的准确性。这些发现表明,稳健常模提高了对将会衰退的健康个体的识别能力,并且可能有助于为研究和早期干预选择有风险的参与者。
The aim of the study was to compare the performance of Robust and Conventional neuropsychological norms in predicting clinical decline among healthy adults and in mild cognitive impairment (MCI). The authors developed Robust baseline cross sectional and longitudinal change norms from 113 healthy participants retaining a normal diagnosis for at least 4 years. Baseline Conventional norms were separately created for 256 similar healthy participants without follow-up. Conventional and Robust norms were tested in an independent cohort of longitudinally studied healthy (n = 223), MCI (n = 136), and Alzheimer's disease (AD, n = 162) participants; 84 healthy participants declined to MCI or AD (NL -> DEC), and 44 MCI declined to AD (MCI -> AD). Compared to Conventional norms, baseline Robust norms correctly identified a higher proportion of NL -> DEC with impairment in delayed memory and attention-language domains. Both norms predicted decline from MCI -> AD. Change norms for delayed memory and attention-language significantly incremented baseline classification accuracies. These findings indicate that Robust norms improve identification of healthy individuals who will decline and may be useful for selecting at-risk participants for research studies and early interventions.