A New Computer-Based Cognitive Measure for Early Detection of Dementia Risk (Japan Cognitive Function Test): Validation Study.

IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2025-02-14 DOI:10.2196/59015
Hiroyuki Shimada, Takehiko Doi, Kota Tsutsumimoto, Keitaro Makino, Kenji Harada, Kouki Tomida, Masanori Morikawa, Hyuma Makizako
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Abstract

Background: The emergence of disease-modifying treatment options for Alzheimer disease is creating a paradigm shift in strategies to identify patients with mild symptoms in primary care settings. Systematic reviews on digital cognitive tests reported that most showed diagnostic performance comparable with that of paper-and-pencil tests for mild cognitive impairment and dementia. However, most studies have small sample sizes, with fewer than 100 individuals, and are based on case-control or cross-sectional designs.

Objective: This study aimed to examine the predictive validity of the Japanese Cognitive Function Test (J-Cog), a new computerized cognitive battery test, for dementia development.

Methods: We randomly assigned 2520 older adults (average age 72.7, SD 6.7 years) to derivation and validation groups to determine and validate cutoff points for the onset of dementia. The Mini-Mental State Examination (MMSE) was used for comparison purposes. The J-Cog consists of 12 tasks that assess orientation, designation, attention and calculation, mental rotation, verbal fluency, sentence completion, working memory, logical reasoning, attention, common knowledge, word memory recall, and episodic memory recall. The onset of dementia was monitored for 60 months. In the derivation group, receiver operating characteristic curves were plotted to determine the MMSE and J-Cog cutoff points that best discriminated between the groups with and without dementia. In the validation group, Cox proportional regression models were developed to predict the associations of the group classified using the cutoff points of the J-Cog or MMSE with dementia incidence. Harrell C-statistic was estimated to summarize how well a predicted risk score described an observed sequence of events. The Akaike information criterion was calculated for relative goodness of fit, where lower absolute values indicate a better model fit.

Results: Significant hazard ratios (HRs) for dementia incidence were found using the MMSE cutoff between 23 and 24 point (HR 1.93, 95% CI 1.13-3.27) and the J-Cog cutoff between 43 and 44 points (HR 2.42, 95% CI 1.50-3.93). In the total validation group, the C-statistic was above 0.8 for all cutoff points. Akaike information criterion with MMSE cutoff between 23 and 24 points as a reference showed a poor fit for MMSE cutoff between 28 and 29 points, and a good fit for the J-Cog cutoff between 43 and 44 points.

Conclusions: The J-Cog has higher accuracy in predicting the development of dementia than the MMSE and has advantages for use in the community as a test of cognitive function, which can be administered by nonprofessionals.

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一种新的基于计算机的认知方法用于早期发现痴呆风险(日本认知功能测试):验证研究。
背景:阿尔茨海默病的疾病改善治疗方案的出现正在创造一种模式转变,在初级保健机构中识别轻度症状患者的策略。对数字认知测试的系统评价报告称,大多数数字认知测试的诊断性能与纸笔测试对轻度认知障碍和痴呆症的诊断性能相当。然而,大多数研究的样本量较小,少于100人,并且基于病例对照或横断面设计。目的:研究日本认知功能测验(J-Cog)对痴呆发展的预测效度。方法:我们将2520名老年人(平均年龄72.7岁,标准差6.7岁)随机分配到衍生组和验证组,以确定和验证痴呆发病的截止点。采用简易精神状态检查(MMSE)进行比较。J-Cog包括12项任务,评估定向、指定、注意和计算、心理旋转、语言流畅性、句子完成、工作记忆、逻辑推理、注意、常识、单词记忆回忆和情景记忆回忆。痴呆的发病情况监测了60个月。在衍生组,绘制受试者工作特征曲线,以确定MMSE和J-Cog截止点,以最好地区分痴呆组和非痴呆组。在验证组中,采用Cox比例回归模型来预测使用J-Cog或MMSE截断点分类的组与痴呆发病率的关联。估计Harrell c统计量来总结预测的风险评分对观察到的事件序列的描述程度。赤池信息准则计算相对拟合优度,其中较低的绝对值表示较好的模型拟合。结果:使用MMSE截止点在23 - 24点之间(HR 1.93, 95% CI 1.13-3.27)和J-Cog截止点在43 - 44点之间(HR 2.42, 95% CI 1.50-3.93)发现痴呆发病率的显著风险比(HR)。在总验证组中,所有截止点的c统计量均大于0.8。以MMSE截止点在23 ~ 24点之间作为参考的赤池信息标准显示,MMSE截止点在28 ~ 29点之间的拟合性较差,而J-Cog截止点在43 ~ 44点之间的拟合性较好。结论:J-Cog在预测痴呆发展方面比MMSE具有更高的准确性,作为一种认知功能测试在社区中使用具有优势,可由非专业人员使用。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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