Algorithm-defined memory impairment in older American Indians: The Strong Heart Study.

IF 2.6 3区 心理学 Q3 NEUROSCIENCES Neuropsychology Pub Date : 2024-09-01 Epub Date: 2024-07-08 DOI:10.1037/neu0000963
Astrid M Suchy-Dicey, Thomas Grabowski, Dedra Buchwald, W T Longstreth, Kristoffer Rhoads
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Abstract

Objective: Assessment of cognition in American Indians poses challenges, including barriers to healthcare, unvalidated clinical standards, and confounding social determinants of health. Alternative strategies for case identification include algorithmic methods, which can outperform clinical judgment in some circumstances.

Method: Algorithmic methods can be maximized using single-domain tests with multiple-serial trial tasks, such as the California Verbal Learning Test II-Short Form (CVLT-SF). We collected CVLT-SF and detailed clinical data, including dementia gold standard by consensus adjudication, in 818 American Indians aged 65-95 in 2010-2013 and repeated in 403 returning participants in 2017-2019 (mean follow-up 6.7 years, range: 4-9). Our algorithm categorized CVLT-SF scores into four memory deficit categories: none, encoding, storage, and retrieval.

Results: At Visit 1, 75.4% had no memory deficit, 15.6% encoding deficit, 3.5% storage deficit, and 5.5% retrieval deficit. At Visit 2, comparable percentages were 68.7%, 10.6%, 6.5%, and 14.2% (respectively). The majority with any deficit at Visit 1-especially encoding-were lost to follow-up by Visit 2. Most with deficits at Visit 2 were newly categorized from those previously intact. The performance of our memory algorithm, compared with adjudication for dementia, was moderately good: correct classification 69%, sensitivity 51%, and specificity 91%.

Conclusions: These descriptive findings encompass a novel contribution in defining memory impairment among American Indians from a single cognitive test. However, more work is needed to improve the sensitivity of this algorithm and maximize its utility for case identification over conventional methods. Altogether, these data provide an important step toward better cognitive characterization and dementia care for an understudied, underserved population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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美国老年印第安人的算法定义记忆损伤:强心研究
目的:评估美国印第安人的认知能力是一项挑战,其中包括医疗保健方面的障碍、未经验证的临床标准以及健康的社会决定因素。病例识别的替代策略包括算法方法,在某些情况下,算法方法可以优于临床判断:方法:使用具有多重序列试验任务的单域测试(如加利福尼亚言语学习测试 II-简表(CVLT-SF))可以最大限度地利用算法方法。我们在 2010-2013 年收集了 818 名年龄在 65-95 岁之间的美国印第安人的 CVLT-SF 和详细临床数据,包括通过共识裁定的痴呆症黄金标准,并在 2017-2019 年对 403 名返回的参与者进行了重复(平均随访 6.7 年,范围:4-9)。我们的算法将 CVLT-SF 分数分为四个记忆缺陷类别:无、编码、存储和检索:在第 1 次就诊时,75.4% 的人没有记忆缺陷,15.6% 的人有编码缺陷,3.5% 的人有存储缺陷,5.5% 的人有检索缺陷。在第 2 次就诊时,可比百分比分别为 68.7%、10.6%、6.5% 和 14.2%。在第 1 次就诊时出现任何缺陷(尤其是编码缺陷)的大多数患者在第 2 次就诊时都失去了随访机会。在第 2 次就诊时出现缺陷的大多数人都是从以前的完好者中新归类出来的。与痴呆判定相比,我们的记忆算法表现中规中矩:分类正确率为 69%,灵敏度为 51%,特异性为 91%:这些描述性研究结果为通过单一认知测试界定美国印第安人的记忆损伤做出了新的贡献。然而,与传统方法相比,还需要做更多的工作来提高该算法的灵敏度,并最大限度地发挥其在病例识别方面的作用。总之,这些数据为更好地认知特征描述和痴呆症护理迈出了重要的一步,这些数据针对的是研究不足、服务欠缺的人群。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuropsychology
Neuropsychology 医学-神经科学
CiteScore
4.10
自引率
4.20%
发文量
132
审稿时长
6-12 weeks
期刊介绍: Neuropsychology publishes original, empirical research; systematic reviews and meta-analyses; and theoretical articles on the relation between brain and human cognitive, emotional, and behavioral function.
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