Background: Stroke is associated with an increased risk of dementia; however, the impact of stroke on cognition has been found to be variable, such that stroke survivors can show decline, remain stable, or revert to baseline cognitive functioning. Knowing the natural history of cognitive impairment after stroke is important for intervention. The aim of this systematic review is to investigate the longitudinal course of cognitive function in stroke survivors. Methods and Results: Three electronic databases (Medline, Embase, PsycINFO) were searched using OvidSP from inception to July 15, 2016. Longitudinal studies with ≥2 time points of cognitive assessment after stroke were included. In total, 5952 articles were retrieved and 14 were included. There was a trend toward significant deterioration in cognitive test scores in stroke survivors (8 studies). Cognitive stability (3 studies) and improvement (3 studies) were also demonstrated, although follow‐up time tended to be shorter in these studies. Variables associated with impairment included age, ethnicity, premorbid cognitive performance, depression, stroke location, and history of previous stroke. Associations with APOE*E4 (apolipoprotein E with the E4 allele) allele status and sex were mixed. Conclusions: Stroke is associated with an increased risk of cognitive decline, but cognitive decline is not a consequence. Factors associated with decline, such as sociodemographic status, health‐related comorbidity, stroke history, and clinical features could be used in models to predict future risk of dementia after stroke. A risk model approach could identify patients at greatest risk for timely intervention to reduce the frequency or delay the onset of poststroke cognitive impairment and dementia.

Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance. Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included. In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model. There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia.Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases.Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort.Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice.Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Dementia is a significant global health issue, particularly for low-income and middle-income countries which majorly contribute to the dementia cases reported globally (67%). We estimated the prevalence of dementia among older people in Bangladesh and compared the estimate across different sociodemographic characteristics and divisions. A cross-sectional study was conducted in 2019 among individuals aged 60 years or older in seven administrative divisions in Bangladesh. Equal numbers of male and female participants were recruited from each division through a multi-stage random sampling technique. Recruitment was proportionally distributed in urban and rural areas in each division. Following consent, the Mini Mental State Examination (MMSE) was performed on all participants. Dementia was defined as an MMSE score of <24 out of 30. Data on age, sex, education, marital status, occupation, socioeconomic status, and type of community (urban or rural) were obtained using a structured questionnaire to compare the prevalence of dementia across different sociodemographic characteristics. Between January and December 2019, 2795 individuals were recruited including ∼400 from each of the seven administrative divisions. The mean age was 67 years (SD: 7), 68% were from rural areas and 51% were female. The prevalence of dementia was 8.0% (95% CI: 7.0–8.9%) with variations across age, sex, education, marital status, occupation, and division. No variations in prevalence were observed across urban/rural locations or socioeconomic status. After adjusting for age, sex, education, occupation and marital status, the odds of dementia was two times higher in females than males (OR: 2.15, 95% CI: 1.43–3.28); nine times higher in people aged ≥90 years than people aged 60–69 years (OR: 9.62, 95% CI: 4.79–19.13), and three times higher in people with no education compared to those who had completed primary school (OR: 3.10, 95% CI: 1.95–5.17). The prevalence of dementia is high in Bangladesh and varies across sociodemographic characteristics with a higher prevalence among females, older people, and people with no education. There is an urgent need to identify the key risk factors for dementia in developing countries, such as Bangladesh, to inform the development of context-relevant risk reduction and prevention strategies. None.

Background: Strategies for the involvement of primary care in the management of patients with presumed or diagnosed dementia are heterogeneous across Europe. We wanted to explore attitudes of primary care physicians (PCPs) when managing dementia: (i) the most popular cognitive tests, (ii) who had the right to initiate or continue cholinesterase inhibitor or memantine treatment, and (iii) the relationship between the permissiveness of these rules/guidelines and PCP's approach in the dementia investigations and assessment. Methods: Key informant survey. Setting: Primary care practices across 25 European countries. Subjects: Four hundred forty-five PCPs responded to a self-administered questionnaire. Two-step cluster analysis was performed using characteristics of the informants and the responses to the survey. Main outcome measures: Two by two contingency tables with odds ratios and 95% confidence intervals were used to assess the association between categorical variables. A multinomial logistic regression model was used to assess the association of multiple variables (age class, gender, and perceived prescription rules) with the PCPs’ attitude of “trying to establish a diagnosis of dementia on their own.” Results: Discrepancies between rules/guidelines and attitudes to dementia management was found in many countries. There was a strong association between the authorization to prescribe dementia drugs and pursuing dementia diagnostic work-up (odds ratio, 3.45; 95% CI 2.28–5.23). Conclusions: Differing regulations about who does what in dementia management seemed to affect PCP's engagement in dementia investigations and assessment. PCPs who were allowed to prescribe dementia drugs also claimed higher engagement in dementia work-up than PCPs who were not allowed to prescribe.

Stroke is a common cause of physical disability but is also strongly associated with cognitive impairment and a risk for future dementia. Despite national clinical guidelines, the service provided for stroke survivors with cognitive and memory difficulties varies across localities. This study critically evaluated the views of healthcare professionals about barriers and facilitators to their care. Seventeen semi-structured individual interviews were conducted by a single interviewer with both primary and secondary care clinicians in regular contact with stroke-survivors. This included stroke medicine specialists, specialist nurses, physiotherapists, occupational therapists, general practitioners and primary care nurses. Topics included individual experiences of the current care offered to patients with cognitive impairment, assessment processes and inter-professional communication. Interviews were audio recorded and transcribed verbatim. Transcripts were thematically analysed and themes grouped into broad categories to facilitate interpretation. Data analysis identified four key themes as barriers to optimal care for stroke-survivors with memory difficulties: 1) Less focus on memory and cognition in post-stroke care; 2) Difficulties bringing up memory and cognitive problems post-stroke; 3) Lack of clarity in current services; and, 4) Assumptions made by healthcare professionals introducing gaps in care. Facilitators included stronger links between primary and secondary care in addition to information provision at all stages of care. The care provided by stroke services is dominated by physical impairments. Clinicians are unsure who should take responsibility for follow-up of patients with cognitive problems. This is made even more difficult by the lack of experience in assessment and stigma surrounding potential diagnoses associated with these deficits. Service development should focus on increased cohesiveness between hospital and community care to create a clear care pathway for post-stroke cognitive impairment.