Quality improvement in neurology: Mild cognitive impairment quality measurement set

Mild cognitive impairment among LRRK2 and GBA1 patients with Parkinson's disease

BACKGROUND

Mild cognitive impairment (MCI) is common in Parkinson's disease (PD). We aimed to assess the incidence of MCI among patients with PD, carriers of mutations in LRRK2 and GBA1 genes, based on the movement disorder society (MDS) criteria for the diagnosis of MCI in early-stage PD.

METHODS

Patients with PD were included if they scored ≤2 on the Hoehn and Yahr and ≤6 years since motor symptom onset. A group of age and gender matched healthy adults served as controls. A neuropsychological cognitive battery was used covering five cognitive domains (executive functions, working memory, memory, visuospatial and language). MCI was explored while applying two methods (level I and II). Frequency of MCI was assessed in comparison between groups.

RESULTS

70 patients with idiopathic PD (iPD) (68 % males), 42 patients with LRRK2-PD (61 % males), 83 patients with GBA1-PD (63 % males) and 132 age and gender matched controls (61 % males), participated in this study. PD groups were similar in clinical characteristics. Level I criteria were positive in 57.5 % of iPD, 43 % of LRRK2-PD and 63.4 % of the GBA1-PD (p = 0.071). Level II criteria was met by 39 % of iPD, 14 % LRRK2-PD and 41 % of GBA1-PD (p < 0.001), when using a 2 standard-deviation (SD) threshold. GBA1-PD and iPD showed impairments on multiple domains even in the more conservative 2 SD, reflecting MCI.

CONCLUSIONS

The majority of our PD cohort was classified as MCI when assessed with strict criteria. GBA1-PD and iPD showed a more widespread pattern of MCI compared with LRRK2-PD.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is the most common type of dementia. Amnestic mild cognitive impairment (aMCI), a pre-dementia stage is an important stage for early diagnosis and intervention. This study aimed to investigate the diagnostic value of qEEG, APOA-I, and APOE ɛ4 allele in aMCI and AD patients and found the correlation between qEEG (Delta + Theta)/(Alpha + Beta) ratio (DTABR) and different cognitive domains.

METHODS

All participants were divided into three groups: normal controls (NCs), aMCI, and AD, and all received quantitative electroencephalography (qEEG), neuropsychological scale assessment, apolipoprotein epsilon 4 (APOE ɛ4) alleles, and various blood lipid indicators. Different statistical methods were used for different data.

RESULTS

The cognitive domains except executive ability were all negatively correlated with DTABR in different brain regions while executive ability was positively correlated with DTABR in several brain regions, although without statistical significance. The consequences confirmed that the DTABR of each brain area were related to MMSE, MoCA, instantaneous memory, and the language ability (p < 0.05), and the DTABR in the occipital area was relevant to all cognitive domains (p < 0.01) except executive function (p = 0.272). Also, occipital DTABR was most correlated with language domain when tested by VFT with a moderate level (r = 0.596, p < 0.001). There were significant differences in T3, T5, and P3 DTABR between both AD and NC and aMCI and NCs. As for aMCI diagnosis, the maximum AUC was achieved when using T3 combined with APOA-I and APOE ε4 (0.855) and the maximum AUC was achieved when using T5 combined with APOA-I and APOE ε4 (0.889) for AD diagnosis.

CONCLUSION

These findings highlight that APOA-I, APOE ɛ4, and qEEG play an important role in aMCI and AD diagnosis. During AD continuum, qEEG DTABR should be taken into consideration for the early detection of AD risk.

Improved Accuracy of the Addenbrooke's Cognitive Examination-Revised in the Diagnosis of Mild Cognitive Impairment, Mild Dementia Due to Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Mokken Scale Analysis

Background

The Addenbrooke's Cognitive Examination-Revised (ACE-R) is an accessible cognitive tool that supports the early detection of mild cognitive impairment (MCI), Alzheimer's disease (AD), and behavioral variant frontotemporal dementia (bvFTD).

Objective

To investigate the diagnostic efficacy of the ACE-R in MCI, AD, and bvFTD through the identification of novel coefficients for differentiation between these diseases.

Methods

We assessed 387 individuals: 102 mild AD, 37 mild bvFTD, 87 with amnestic MCI patients, and 161 cognitively unimpaired controls. The Mokken scaling technique facilitated the extraction out of the 26 ACE-R items that exhibited a common latent trait, thereby generating the Mokken scales for the AD group and the MCI group. Subsequently, we performed logistic regression, integrating each Mokken scales with sociodemographic factors, to differentiate between AD and bvFTD, as well as between AD or MCI and control groups. Ultimately, the Receiver Operating Characteristic curve analysis was employed to assess the efficacy of the coefficient's discrimination.

Results

The AD-specific Mokken scale (AD-MokACE-R) versus bvFTD exhibited an Area Under the Curve (AUC) of 0.922 (88% sensitivity and specificity). The AD-MokACE-R versus controls achieved an AUC of 0.968 (93% sensitivity, 94% specificity). The MCI-specific scale (MCI-MokACE-R) versus controls demonstrated an AUC of 0.859 (78% sensitivity, 79% specificity).

Conclusions

The ACE-R's capacity is enhanced through statistical methods and demographic integration, allowing for accurate differentiation between AD and bvFTD, as well as between MCI and controls. This new method not only reinforces its clinical value in early diagnosis but also surpasses traditional approaches noted in prior studies.

Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts

BACKGROUND

Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed.

OBJECTIVE

Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations.

METHODS

Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort.

RESULTS

The algorithms achieved a performance of AUC  0.73 and AUC  0.77 in the respective training cohorts and AUC  0.81 in the unseen validation cohorts.

CONCLUSION

The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

A framework for addressing Alzheimer's disease: Without a frame, the work has no aim

Confronting Alzheimer's disease (AD) involves patients, healthcare professionals, supportive services, caregivers, and government agencies interacting along a continuum from initial awareness to diagnosis, treatment, support, and care. This complex scope presents a challenge for health system transformation supporting individuals at risk for, or diagnosed with, AD. The AD systems preparedness framework was developed to help health systems identify specific opportunities to implement and evaluate focused improvement programs. The framework is purposely flexible to permit local adaptation across different health systems and countries. Health systems can develop solutions tailored to system-specific priorities considered within the context of the overall framework. Example metric concepts and initiatives are provided for each of ten areas of focus. Examples of funded projects focusing on screening and early detection are provided. It is our hope that stakeholders utilize the common framework to generate and share additional implementation evidence to benefit individuals with AD.

Applying ACE-III, M-ACE and MMSE to Diagnostic Screening Assessment of Cognitive Functions within the Polish Population

The research aims to compare the accuracy of the mini-mental state examination (MMSE), the Addenbrooke's cognitive examination III (ACE-III) and the mini-Addenbrooke's cognitive examination (M-ACE) within the Polish population. The model comprised several stages: the features of each test were compared; the shifts in result categorisations between the norm and below the norm were analysed; a third category-mild cognitive impairment (MCI)-was included. Additionally, particular ACE-III domains that scored below domain-specific norm thresholds were analysed to establish the potential early predictors of dementia. All tests correlated to a high and very high degree-cf. MMSE and ACE-III (r = 0.817; p < 0.001), MMSE and M-ACE (r = 0.753; p < 0.001), ACE-III and M-ACE (r = 0.942; p < 0.001). The area under the ROC curve for the ACE-III diagnostic variable had a high value (AUC = 0.920 ± 0.014). A cut-off point of 81 points was suggested for ACE-III; the M-ACE diagnostic variable had an equally high value (AUC = 0.891 ± 0.017). A cut-off point of 20 points was suggested. A significant decrease in the mean score values for people who scored norm or below the norm under ACE-III, as compared to the MMSE results for norm (p < 0.0001), occurred for speech fluency (which decreased by 26.4%). The tests in question are characterised by high sensitivity and specificity. Targeted ACE-III seems best recommended for use in specialised diagnostic centres, whereas M-ACE appears to be a better suited diagnostic alternative for primary health care centres in comparison to MMSE.

Copolymer-1 as a potential therapy for mild cognitive impairment

Mild cognitive impairment (MCI) is a prodromal stage of memory impairment that may precede dementia. MCI is classified by the presence or absence of memory impairment into amnestic or non-amnestic MCI, respectively. More than 90% of patients with amnestic MCI who progress towards dementia meet criteria for Alzheimer's disease (AD). A combination of mechanisms promotes MCI, including intracellular neurofibrillary tangle formation, extracellular amyloid deposition, oxidative stress, neuronal loss, synaptodegeneration, cholinergic dysfunction, cerebrovascular disease, and neuroinflammation. However, emerging evidence indicates that neuroinflammation plays an important role in the pathogenesis of cognitive impairment. Unfortunately, there are currently no Food and Drug Administration (FDA)-approved drugs for MCI. Copolymer-1 (Cop-1), also known as glatiramer acetate, is a synthetic polypeptide of four amino acids approved by the FDA for the treatment of relapsing-remitting multiple sclerosis. Cop-1 therapeutic effect is attributed to immunomodulation, promoting a switch from proinflammatory to anti-inflammatory phenotype. In addition to its anti-inflammatory properties, it stimulates brain-derived neurotrophic factor (BDNF) secretion, a neurotrophin involved in neurogenesis and the generation of hippocampal long-term potentials. Moreover, BDNF levels are significantly decreased in patients with cognitive impairment. Therefore, Cop-1 immunization might promote synaptic plasticity and memory consolidation by increasing BDNF production in patients with MCI.

Online awareness of performance on a functional cognitive assessment in individuals with stroke: A case-control study

Unawareness is a significant barrier to cognitive rehabilitation following acquired brain injury. Little is known about online awareness of cognitively-based instrumental activities of daily living (C-IADL) after stroke, particularly C-IADLs that emphasize executive functions. Our goal was to evaluate in stroke patients (1) online awareness during and immediately after a C-IADL task that emphasizes executive functions and (2) the association between awareness and performance on the C-IADL task. Seventy-seven stroke patients on an acute inpatient rehabilitation unit and 77 control participants completed the 10-item Weekly Calendar Planning Activity (WCPA-10), a standardized C-IADL task that requires working memory, planning, shifting, and inhibition. Trained examiners observed the use of a self-checking strategy and self-recognition of errors during the task. Immediately after the task, participants estimated their accuracy, and rated their own performance, which was compared with objective accuracy. Relative to the control group, stroke patients overestimated their accuracy, less often recognized errors, and less frequently used a self-checking strategy. Overestimation was associated with worse overall performance on the WCPA-10. Findings suggest that poor online awareness of C-IADL performance is common in stroke patients undergoing acute inpatient rehabilitation. Increasing awareness through metacognitive interventions should be a core focus of early post-stroke cognitive rehabilitation.

CoGNIT Automated Tablet Computer Cognitive Testing in Patients With Mild Cognitive Impairment: Feasibility Study

Background

Early diagnosis of cognitive disorders is becoming increasingly important. Limited resources for specialist assessment and an increasing demographical challenge warrants the need for efficient methods of evaluation. In response, CoGNIT, a tablet app for automatic, standardized, and efficient assessment of cognitive function, was developed. Included tests span the cognitive domains regarded as important for assessment in a general memory clinic (memory, language, psychomotor speed, executive function, attention, visuospatial ability, manual dexterity, and symptoms of depression).

Objective

The aim of this study was to assess the feasibility of automatic cognitive testing with CoGNIT in older patients with symptoms of mild cognitive impairment (MCI).

Methods

Patients older than 55 years with symptoms of MCI (n=36) were recruited at the research clinic at the Blekinge Institute of Technology (BTH), Karlskrona, Sweden. A research nurse administered the Mini-Mental State Exam (MMSE) and the CoGNIT app on a tablet computer. Technical and testing issues were documented.

Results

The test battery was completed by all 36 patients. One test, the four-finger–tapping test, was performed incorrectly by 42% of the patients. Issues regarding clarity of instructions were found in 2 tests (block design test and the one finger-tapping test). Minor software bugs were identified.

Conclusions

The overall feasibility of automatic cognitive testing with the CoGNIT app in patients with symptoms of MCI was good. The study highlighted tests that did not function optimally. The four-finger–tapping test will be discarded, and minor improvements to the software will be added before further studies and deployment in the clinic.

Comparison of dementia incidence and prevalence between individuals with and without HIV infection in primary care from 2000 to 2016

OBJECTIVE

To compare dementia incidence and prevalence after age 50 years by HIV status.

DESIGN

Observational cohort, 2000-2016.

METHODS

People with HIV (PWH) on antiretroviral therapy (ART) and demographically similar people without HIV (PWoH), all aged 50 years and older, were identified from Kaiser Permanente healthcare systems in Northern California, Southern California, and Mid-Atlantic States (Maryland, Virginia, Washington DC). Dementia diagnoses were obtained from electronic health records. Incidence and prevalence of dementia, overall and by time period (i.e. 2000-2002, 2003-2004, …, 2015-2016), were calculated using Poisson regression. Trends were examined using Joinpoint regression. Rate ratios were used to compare dementia by HIV status with adjustment for sociodemographics, substance use, and clinical factors.

RESULTS

The study included 13 296 PWH and 155 354 PWoH (at baseline: for both, mean age = 54 years, 89% men; for PWH, 80% with HIV RNA <200 copies/ml). From 2000 to 2016, overall incidence of dementia was higher among PWH [adjusted incidence rate ratio (aIRR) = 1.80, 95% confidence interval (CI) = 1.60-2.04]. Dementia incidence decreased among both PWH and PWoH (-8.0 and -3.1% per period, respectively) but remained higher among PWH in the most recent time period, 2015-2016 (aIRR = 1.58, 95% CI = 1.18-2.12). The overall prevalence of dementia from 2000 to 2016 was higher among PWH [adjusted prevalence ratio (aPR) = 1.86, 95% CI = 1.70-2.04] and was also higher among PWH in 2015-2016 (aPR = 1.75, 95% CI = 1.56-1.97).

CONCLUSION

Reductions in dementia incidence are encouraging and may reflect ART improvement, but PWH are still more likely to have dementia than PWoH. Monitoring the burden of dementia among PWH is important as this population ages.

Cardiometabolic Conditions and All-Cause Dementia Among American Indian and Alaska Native People

BACKGROUND

Diabetes, hypertension, and cardiovascular disease (CVD) are modifiable lifestyle-related cardiometabolic conditions associated with dementia. Yet, little is known regarding these associations among American Indian and Alaska Native (AI/AN) people. Thus, we examined the association of diabetes, hypertension, and CVD with all-cause dementia among AI/ANs aged 65 years and older.

METHOD

This was a cross-sectional analysis of the Indian Health Service Improving Health Care Delivery Data Project. Our study population was a 1:1 matched sample of 4 074 AI/ANs aged 65 years and older and Indian Health Service active users during fiscal year 2013. We employed International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes for all-cause dementia, hypertension, and CVD. Diabetes was measured with a validated algorithm to identify adults with diabetes that uses diagnoses, laboratory test results, and medication criteria.

RESULTS

Multivariable analyses revealed that diabetes and CVD were associated with increased odds of all-cause dementia and hypertension was not. Cardiovascular disease types associated with all-cause dementia differed with cerebrovascular disease having the strongest association. Analyses stratified by gender revealed that diabetes and CVD were associated with increased odds of all-cause dementia for women and only CVD was associated with all-cause dementia for men.

CONCLUSIONS

Training and support of primary care clinicians, addressing cultural considerations, and ensuring inclusion of AI/ANs in research are steps that could help meet AI/AN people's needs. Our findings underscore to the importance of improved management and control of diabetes and CVD, which may lead to the prevention of dementia among older AI/ANs.

The estimated prevalence of no reported dementia-related diagnosis in older Americans living with possible dementia by healthcare utilization

BACKGROUND

Screening for dementia in relevant healthcare settings may help in identifying low cognitive functioning for comprehensive cognitive assessments and subsequent dementia treatment after diagnosis.

AIMS

This study sought to estimate the prevalence of no reported dementia-related diagnosis in a nationally-representative sample of older Americans with a cognitive impairment consistent with dementia (CICD) by healthcare utilization.

METHODS

The unweighted analytical sample included 1514 Americans aged ≥ 65 years that were identified as having a CICD without history of stroke, cancers, neurological conditions, or brain damage who participated in at least one-wave of the 2010-2016 waves of the Health and Retirement Study. An adapted Telephone Interview of Cognitive Status assessed cognitive functioning. Those with scores ≤ 6 had a CICD. Dementia-related diagnosis was self-reported. Respondents indicated if they visited a physician, received home healthcare, or experienced an overnight nursing home stay in the previous two years.

RESULTS

The prevalence of no reported dementia-related diagnosis in persons with a CICD who visited a physician was 89.9% (95% confidence interval (CI): 85.4%-93.1%). Likewise, the prevalence of no reported diagnosis in those with a CICD who received home healthcare was 84.3% (CI: 75.1-90.5%). For persons with a CICD that had an overnight nursing home stay, the prevalence of no reported dementia-related diagnosis was 83.0% (CI: 69.1-91.4%).

DISCUSSION

Although the prevalence of no reported dementia-related diagnosis in individuals with a CICD differed across healthcare settings, the prevalence was generally high nonetheless.

CONCLUSIONS

We recommend increased awareness and efforts be given to dementia screenings in various clinical settings.

A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

Adverse Childhood Experiences and Mild Cognitive Impairment in Later Life: Exploring Rural/Urban and Gender Differences Using CHARLS

This paper aims to examine whether and how adverse childhood experiences are associated with mild cognitive impairment among middle-aged and older adults in China, and if the associations vary by gender and rural/urban residence. Using four waves of data from the China Health and Retirement Longitudinal Study, cox proportional hazard models were applied. Results showed that the rural and female subsamples were significantly disadvantaged and were more likely to be cognitively impaired. Moreover, childhood family socioeconomic status and childhood social relationships were significantly associated with the risk of mild cognitive impairment for the study sample. Our findings suggest that, for middle-aged and older Chinese adults, adverse childhood experiences could have long-lasting impacts on cognitive functioning throughout the life course.

Brain Network Integrity Changes in Subjective Cognitive Decline: A Possible Physiological Biomarker of Dementia

Objective: The current study seeks to illustrate potential early and objective neurophysiological biomarkers of neurodegenerative cognitive decline by evaluating features of brain network physiological performance and structure utilizing different modalities. Methods: This study included 17 clinically healthy individuals with self-reported cognitive decline (Subjective Cognitive Decline group, SCD, no objective finding of cognitive decline), 12 individuals diagnosed with amnestic Mild Cognitive Impairment (aMCI), 11 individuals diagnosed with Dementia, and 15 healthy subjects. All subjects underwent computerized cognitive performance testing, MRI scans including T1 for gray matter (GM) volume quantification, DTI for quantification of white matter (WM) microstructure fractional anisotropy (FA) and mean diffusivity (MD), and brain network function evaluation using DELPHI (TMS-EEG) measures of connectivity, excitability, and plasticity. Results: Both DELPHI analysis of network function and DTI analysis detected a significant decrease in connectivity, excitability, and WM integrity in the SCD group compared to healthy control (HC) subjects; a significant decrease was also noted for aMCI and Dementia groups compared to HC. In contrast, no significant decrease was observed in GM volume in the SCD group compared to healthy norms, a significant GM volume decrease was observed only in objectively cognitively impaired aMCI subjects and in dementia subjects. Conclusions: This study results suggest that objective direct measures of brain network physiology and WM integrity may provide early-stage biomarkers of neurodegenerative-related changes in subjects that have not yet displayed any other objective measurable cognitive or GM volume deficits which may facilitate early preventive care for neurodegenerative decline and dementia.

Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis

The critical role of primary care clinicians (PCCs) in Alzheimer's disease (AD) prevention, diagnosis and management must evolve as new treatment paradigms and disease-modifying therapies (DMTs) emerge. Our understanding of AD has grown substantially: no longer conceptualized as a late-in-life syndrome of cognitive and functional impairments, we now recognize that AD pathology builds silently for decades before cognitive impairment is detectable. Clinically, AD first manifests subtly as mild cognitive impairment (MCI) due to AD before progressing to dementia. Emerging optimism for improved outcomes in AD stems from a focus on preventive interventions in midlife and timely, biomarker-confirmed diagnosis at early signs of cognitive deficits (i.e. MCI due to AD and mild AD dementia). A timely AD diagnosis is particularly important for optimizing patient care and enabling the appropriate use of anticipated DMTs. An accelerating challenge for PCCs and AD specialists will be to respond to innovations in diagnostics and therapy for AD in a system that is not currently well positioned to do so. To overcome these challenges, PCCs and AD specialists must collaborate closely to navigate and optimize dynamically evolving AD care in the face of new opportunities. In the spirit of this collaboration, we summarize here some prominent and influential models that inform our current understanding of AD. We also advocate for timely and accurate (i.e. biomarker-defined) diagnosis of early AD. In doing so, we consider evolving issues related to prevention, detecting emerging cognitive impairment and the role of biomarkers in the clinic.

The Role of Brief Global Cognitive Tests and Neuropsychological Expertise in the Detection and Differential Diagnosis of Dementia

Dementia is a global public health problem and its impact is bound to increase in the next decades, with a rapidly aging world population. Dementia is by no means an obligatory outcome of aging, although its incidence increases exponentially in old age, and its onset may be insidious. In the absence of unequivocal biomarkers, the accuracy of cognitive profiling plays a fundamental role in the diagnosis of this condition. In this Perspective article, we highlight the utility of brief global cognitive tests in the diagnostic process, from the initial detection stage for which they are designed, through the differential diagnosis of dementia. We also argue that neuropsychological training and expertise are critical in order for the information gathered from these omnibus cognitive tests to be used in an efficient and effective way, and thus, ultimately, for them to fulfill their potential.

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

BACKGROUND

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.

OBJECTIVE

A systematic review and meta-analysis was conducted to estimate the pooled MCI prevalence among the population aged 55 years and older in China.

METHODS

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 Q test and I2.

RESULTS

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.

CONCLUSIONS

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.

Strategies for the use of Ginkgo biloba extract, EGb 761® , in the treatment and management of mild cognitive impairment in Asia: Expert consensus

BACKGROUND

Mild cognitive impairment (MCI) is a neurocognitive state between normal cognitive aging and dementia, with evidence of neuropsychological changes but insufficient functional decline to warrant a diagnosis of dementia. Individuals with MCI are at increased risk for progression to dementia; and an appreciable proportion display neuropsychiatric symptoms (NPS), also a known risk factor for dementia. Cerebrovascular disease (CVD) is thought to be an underdiagnosed contributor to MCI/dementia. The Ginkgo biloba extract, EGb 761® , is increasingly being used for the symptomatic treatment of cognitive disorders with/without CVD, due to its known neuroprotective effects and cerebrovascular benefits.

AIMS

To present consensus opinion from the ASian Clinical Expert group on Neurocognitive Disorders (ASCEND) regarding the role of EGb 761® in MCI.

MATERIALS & METHODS

The ASCEND Group reconvened in September 2019 to present and critically assess the current evidence on the general management of MCI, including the efficacy and safety of EGb 761® as a treatment option.

RESULTS

EGb 761® has demonstrated symptomatic improvement in at least four randomized trials, in terms of cognitive performance, memory, recall and recognition, attention and concentration, anxiety, and NPS. There is also evidence that EGb 761® may help delay progression from MCI to dementia in some individuals.

DISCUSSION

EGb 761® is currently recommended in multiple guidelines for the symptomatic treatment of MCI. Due to its beneficial effects on cerebrovascular blood flow, it is reasonable to expect that EGb 761® may benefit MCI patients with underlying CVD.

CONCLUSION

As an expert group, we suggest it is clinically appropriate to incorporate EGb 761® as part of the multidomain intervention for MCI.

Self-Reported Dementia-Related Diagnosis Underestimates the Prevalence of Older Americans Living with Possible Dementia

BACKGROUND

Dementia screening is an important step for appropriate dementia-related referrals to diagnosis and treat possible dementia.

OBJECTIVE

We sought to estimate the prevalence of no reported dementia-related diagnosis in a nationally representative sample of older Americans with a cognitive impairment consistent with dementia (CICD).

METHODS

The weighted analytical sample included 6,036,224 Americans aged at least 65 years old that were identified as having a CICD without history of stroke, cancers, neurological conditions, or brain damage who participated in at least one-wave of the 2010-2016 Health and Retirement Study. The adapted Telephone Interview of Cognitive Status assessed cognitive functioning. Those with scores≤6 were considered as having a CICD. Healthcare provider dementia-related diagnosis was self-reported. Age, sex, educational achievement, and race and ethnicity were also self-reported.

RESULTS

The overall estimated prevalence of no reported dementia-related diagnosis for older Americans with a CICD was 91.4%(95%confidence interval (CI): 87.7%-94.1%). Persons with a CICD who identified as non-Hispanic black had a high prevalence of no reported dementia-related diagnosis (93.3%; CI: 89.8%-95.6%). The estimated prevalence of no reported dementia-related diagnosis was greater in males with a CICD (99.7%; CI: 99.6%-99.8%) than females (90.2%; CI: 85.6%-93.4%). Moreover, the estimated prevalence of no reported dementia-related diagnosis for non-high school graduates with a CICD was 93.5%(CI: 89.3%-96.1%), but 90.9%(CI: 84.7%-94.7%) for those with at least a high school education.

CONCLUSION

Dementia screening should be encouraged during routine geriatric health assessments. Continued research that evaluates the utility of self-reported dementia-related measures is also warranted.

Practices and opinions about disclosure of the diagnosis of Alzheimer's disease to patients with MCI or dementia: a survey among Belgian medical experts in the field of dementia

Previous surveys revealed that only a minority of clinicians routinely disclosed the diagnosis of Alzheimer's disease (AD) to their patients. Many health professionals fear that the disclosure could be harmful to the patient. Recent advances in the development of biomarkers and new diagnostic criteria allow for an earlier diagnosis of AD at the mild cognitive impairment (MCI) stage. The Belgian Dementia Council, a group of Belgian experts in the field of dementia, performed a survey among its 44 members about their opinions and practices regarding disclosure of the diagnosis of AD, including MCI due to AD, and its consequences. Twenty-six respondents declared that they often or always disclose the diagnosis of AD to patients with dementia and to patients with MCI when AD CSF biomarkers are abnormal. The majority observed that the disclosure of AD is rarely or never harmful to the patients. Their patients and their caregivers rarely or never demonstrated animosity towards the clinicians following disclosure of the diagnosis of AD. These results should reassure clinicians about the safety of AD diagnosis disclosure in most cases whether the patient is at the MCI or the dementia stage.

Dementia medical screening using mobile applications: A systematic review with a new mapping model

Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment

Background:

The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment.

Objective:

Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA).

Methods:

We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features.

Results:

Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999).

Conclusion:

MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.