Meta-analysis on a Cognition Assessment Tool and the Prediction of Risk Factors on Older Drivers With Mild Cognitive Impairment (MCI)

2014 ◽  
Vol 22 (2) ◽  
pp. 47-64 ◽  
Author(s):  
A-Young Song ◽  
◽  
Min-Kyoung Park ◽  
Jae-Shin Lee
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Older Drivers

scholarly journals Predicting On-Road Driving Skills, Fitness to Drive, and Prospective Accident Risk in Older Drivers and Drivers with Mild Cognitive Impairment: The Importance of Non-Cognitive Risk Factors

2021 ◽  
Vol 79 (1) ◽  
pp. 401-414
Author(s):  
Max Toepper ◽  
Philipp Schulz ◽  
Thomas Beblo ◽  
Martin Driessen
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Older Drivers ◽  
Accident Risk ◽  
Fitness To Drive ◽  
Cognitive Predictors ◽  
Driving Skills ◽  
Cognitive Risk ◽  
Cognitive Risk Factors

Background: On-road driving behavior can be impaired in older drivers and particularly in drivers with mild cognitive impairment (MCI). Objective: To determine whether cognitive and non-cognitive risk factors for driving safety may allow an accurate and economic prediction of on-road driving skills, fitness to drive, and prospective accident risk in healthy older drivers and drivers with MCI, we examined a representative combined sample of older drivers with and without MCI (N = 74) in an observational on-road study. In particular, we examined whether non-cognitive risk factors improve predictive accuracy provided by cognitive factors alone. Methods: Multiple and logistic hierarchical regression analyses were utilized to predict different driving outcomes. In all regression models, we included cognitive predictors alone in a first step and added non-cognitive predictors in a second step. Results: Results revealed that the combination of cognitive and non-cognitive risk factors significantly predicted driving skills (R2adjusted = 0.30) and fitness to drive (81.2% accuracy) as well as the number (R2adjusted = 0.21) and occurrence (88.3% accuracy) of prospective minor at-fault accidents within the next 12 months. In all analyses, the inclusion of non-cognitive risk factors led to a significant increase of explained variance in the different outcome variables. Conclusion: Our findings suggest that a combination of the most robust cognitive and non-cognitive risk factors may allow an economic and accurate prediction of on-road driving performance and prospective accident risk in healthy older drivers and drivers with MCI. Therefore, non-cognitive risk factors appear to play an important role.

scholarly journals Risk factors for predicting progression from normal cognition to mild cognitive impairment: protocol for a systematic review and meta-analysis of cohort studies

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e027313 ◽  
Author(s):  
Jie Wang ◽  
Lina Wang ◽  
Xianglian Zhou ◽  
Xiaohong Wen ◽  
Xueting Zhen
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cohort Studies ◽  
Meta Analysis ◽  
Primary Data ◽  
Cochrane Library ◽  
The Impact ◽  
Normal Cognition

IntroductionMild cognitive impairment (MCI) often represents the earliest stage of Alzheimer’s disease. There has been considerable research investigating specific risk factors regarding the progression from normal cognition to MCI. However, different studies have come to different conclusions on the impact of particular risk factors. Therefore, it is necessary to conduct a meta-analysis of the risk factors that predict cognitive disruption in individuals based on associations with MCI.Methods and analysisWe will search seven electronic databases without time limit, including MEDLINE, EMBASE, The Cochrane Library, Web of Science, China National Knowledge Infrastructure, the Wan Fang Database and China Biology Medicine. Two researchers will independently screen for eligibility and perform data extraction. Data were extracted from cohort studies meeting the inclusive criteria according to the Newcastle Ottawa Scale (NOS) methods. A third member of the research team will be contacted when a consensus cannot be reached. Any disagreement will be settled by consensus. The NOS will be used to assess the quality of the studies. All analyses were performed using Stata V.15.1.Ethics and disseminationWe will report this review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. We will disseminate our findings through a publication in a peer-reviewed journal. This systematic review does not require ethical approval as no primary data are collected.PROSPERO registration numberCRD42018109099.

scholarly journals Decisional capacity to consent to treatment and research in patients affected by Mild Cognitive Impairment. A systematic review and meta-analysis

2021 ◽  
pp. 1-14
Author(s):  
Giovanna Parmigiani ◽  
Antonio Del Casale ◽  
Gabriele Mandarelli ◽  
Benedetta Barchielli ◽  
Georgios D. Kotzalidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Assessment Tool ◽  
Meta Analysis ◽  
Competence Assessment ◽  
Decisional Capacity ◽  
Capacity To Consent ◽  
Decision Making Capacity ◽  
Consent To Treatment

ABSTRACT Objectives: To perform a meta-analysis of clinical studies on the differences in treatment or research decision-making capacity among patients with Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD), and healthy comparisons (HCs). Design: A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Standardized mean differences and random-effects model were used in all cases. Setting: The United States, France, Japan, and China. Participants: Four hundred and ten patients with MCI, 149 with AD, and 368 HCs were included. Measurements: The studies we included in the analysis assessed decisional capacity to consent by the MacArthur Competence Assessment Tool for Treatment (MAcCAT-T), MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR), Capacity to Consent to Treatment Instrument (CCTI), and University of California Brief Assessment of Capacity to Consent (UBACC). Results: We identified 109 potentially eligible studies from 1672 records, and 7 papers were included in the meta-analysis. The meta-analysis showed that there was significant impairment in a decision-making capacity in MCI patients compared to the HCs group in terms of Understanding (SMD = −1.04, 95% CI: −1.31 to −0.77, P < 0.001; I2 = 52%, P = 0.07), Appreciation (SMD = −0.51, 95% CI: −0.66 to −0.36, P < 0.001; I2 = 0%, P = 0.97), and Reasoning (SMD = −0.62, 95% CI: −0.77, −0.47, P < 0.001; I2=0%, P =0.46). MCI patients scored significantly higher in Understanding (SMD = 1.50, 95% CI: 0.91, 2.09, P = 0.01, I2 = 78%, P = 0.00001) compared to patients affected by AD. Conclusions: Patients affected by MCI are at higher risk of impaired capacity to consent to treatment and research compared to HCs, despite being at lower risk compared to patients affected by AD. Clinicians and researchers need to carefully evaluate decisional capacity in MCI patients providing informed consent.

Elucidating the risk factors for progression from amyloid-negative amnestic mild cognitive impairment to dementia

2020 ◽  
Vol 17 ◽  
Author(s):  
Hyung-Ji Kim ◽  
Jae-Hong Lee ◽  
E-nae Cheong ◽  
Sung-Eun Chung ◽  
Sungyang Jo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amnestic Mild Cognitive Impairment ◽  
Amyloid Pet ◽  
Clinical Progression ◽  
Amnestic Mci

Background: Amyloid PET allows for the assessment of amyloid β status in the brain, distinguishing true Alzheimer’s disease from Alzheimer’s disease-mimicking conditions. Around 15–20% of patients with clinically probable Alzheimer’s disease have been found to have no significant Alzheimer’s pathology on amyloid PET. However, a limited number of studies had been conducted this subpopulation in terms of clinical progression. Objective: We investigated the risk factors that could affect the progression to dementia in patients with amyloid-negative amnestic mild cognitive impairment (MCI). Methods: This study was a single-institutional, retrospective cohort study of patients over the age of 50 with amyloidnegative amnestic MCI who visited the memory clinic of Asan Medical Center with a follow-up period of more than 36 months. All participants underwent brain magnetic resonance imaging (MRI), detailed neuropsychological testing, and fluorine-18[F18]-florbetaben amyloid PET. Results: During the follow-up period, 39 of 107 patients progressed to dementia from amnestic MCI. In comparison with the stationary group, the progressed group had a more severe impairment in verbal and visual episodic memory function and hippocampal atrophy, which showed an Alzheimer’s disease-like pattern despite the lack of evidence for significant Alzheimer’s disease pathology. Voxel-based morphometric MRI analysis revealed that the progressed group had a reduced gray matter volume in the bilateral cerebellar cortices, right temporal cortex, and bilateral insular cortices. Conclusion: Considering the lack of evidence of amyloid pathology, clinical progression of these subpopulation may be caused by other neuropathologies such as TDP-43, abnormal tau or alpha synuclein that lead to neurodegeneration independent of amyloid-driven pathway. Further prospective studies incorporating biomarkers of Alzheimer’s diseasemimicking dementia are warranted.

scholarly journals Risk factors for developing dementia in type 2 diabetes mellitus patients with mild cognitive impairment

2019 ◽  
Vol Volume 15 ◽  
pp. 167-175 ◽  
Author(s):  
Oana Albai ◽  
Mirela Frandes ◽  
Romulus Timar ◽  
Deiana Roman ◽  
Bogdan Timar
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment

scholarly journals The Prevalence of Mild Cognitive Impairment among Chinese People: A Meta-Analysis

2021 ◽  
Vol 55 (2) ◽  
pp. 79-91
Author(s):  
Yan Deng ◽  
Siqi Zhao ◽  
Guangwen Cheng ◽  
Jiajia Yang ◽  
Benchao Li ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diagnostic Criteria ◽  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Internal Validity ◽  
Screening Tools ◽  
Chinese Elderly ◽  
Pooled Prevalence

<b><i>Background:</i></b> Mild cognitive impairment (MCI) induced the majority number of dementia patients. The prevalence of MCI in China varied across studies with different screening tools and diagnostic criteria. <b><i>Objective:</i></b> A systematic review and meta-analysis was conducted to estimate the pooled MCI prevalence among the population aged 55 years and older in China. <b><i>Methods:</i></b> PubMed, EMBASE, CNKI, Wanfang, CQVIP, and CBMdisc were searched for studies on prevalence of MCI among Chinese elderly between January 1, 1980, and February 10, 2020. The quality assessment was conducted via external validity, internal validity, and informativity, the pooled prevalence was calculated through the random-effect model, and the homogeneity was evaluated by Cochran’s <i>Q</i> test and <i>I</i><sup>2</sup>. <b><i>Results:</i></b> Fifty-three studies with 123,766 subjects were included. The pooled prevalence of MCI among Chinese elderly was 15.4% (95% CI: 13.5–17.4%). Subgroup analyses indicated that the prevalence calculated with different screening tools was 20.2% (95% CI: 15.1–25.9%) for Montreal Cognitive Assessment (MoCA) and 13.0% (95% CI: 10.7–15.5%) for Mini-Mental State Examination (MMSE). According to different diagnostic criteria, the prevalence was 14.8% (95% CI: 12.2–17.6%) for Petersen criteria, 15.0% (95% CI: 12.7–17.5%) for DSM-IV, and 21.2% (95% CI: 17.5–25.2%) for Chinese Expert Consensus on Cognitive Impairment (CECCI). Besides, women, older adults, illiterate people, rural residents, and those who lived with unhealthy lifestyles and morbidity showed higher prevalence. <b><i>Conclusions:</i></b> The prevalence of MCI in China was 15.4%, which varied by demographics, lifestyles, morbidity, screening tools, and diagnostic criteria. In further studies, screening tools and diagnosis criteria should be considered when estimating MCI prevalence.

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