Challenges in detecting and managing mild cognitive impairment in primary care: a focus group study in Shanghai, China

Post-Stroke Cognitive Impairment: A Narrative Review of the Comprehensive Screening and Detecting Process

PURPOSE

To examine screening procedures and tools for post-stroke cognitive impairment (PSCI) to guide future practices and research.

METHOD

Searches in PubMed, CINAHL, PsycINFO, and Google Scholar included articles from 2013 to 2023 focusing on individuals with first ever ischemic stroke and confirming PSCI within 1 year. Thematic analysis was synthesized narratively.

RESULTS

Eight studies (two cross-sectional and six prospective cohorts) with 25,443 participants were reviewed. Screening for PSCI was typically performed within 3 to 6 months post-stroke. Montreal Cognitive Assessment and Mini-Mental State Examination were the most commonly used tools, but cutoff scores varied widely. Screening involved pre- and post-stroke cognitive screening and identifying risk factors.

CONCLUSION

Significant variability exists in PSCI assessment tools, cutoff, and timing. Further research is needed to standardize screening protocols, focusing on criteria, timing, accuracy, and feasibility. Early and repeated screening with risk management can improve PSCI prevention. [Journal of Gerontological Nursing, 51(3), 19-27.].

A Stable and Scalable Digital Composite Neurocognitive Test for Early Dementia Screening Based on Machine Learning: Model Development and Validation Study

BACKGROUND

Dementia has become a major public health concern due to its heavy disease burden. Mild cognitive impairment (MCI) is a transitional stage between healthy aging and dementia. Early identification of MCI is an essential step in dementia prevention.

OBJECTIVE

Based on machine learning (ML) methods, this study aimed to develop and validate a stable and scalable panel of cognitive tests for the early detection of MCI and dementia based on the Chinese Neuropsychological Consensus Battery (CNCB) in the Chinese Neuropsychological Normative Project (CN-NORM) cohort.

METHODS

CN-NORM was a nationwide, multicenter study conducted in China with 871 participants, including an MCI group (n=327, 37.5%), a dementia group (n=186, 21.4%), and a cognitively normal (CN) group (n=358, 41.1%). We used the following 4 algorithms to select candidate variables: the F-score according to the SelectKBest method, the area under the curve (AUC) from logistic regression (LR), P values from the logit method, and backward stepwise elimination. Different models were constructed after considering the administration duration and complexity of combinations of various tests. Receiver operating characteristic curve and AUC metrics were used to evaluate the discriminative ability of the models via stratified sampling cross-validation and LR and support vector classification (SVC) algorithms. This model was further validated in the Alzheimer's Disease Neuroimaging Initiative phase 3 (ADNI-3) cohort (N=743), which included 416 (56%) CN subjects, 237 (31.9%) patients with MCI, and 90 (12.1%) patients with dementia.

RESULTS

Except for social cognition, all other domains in the CNCB differed between the MCI and CN groups (P<.008). In feature selection results regarding discrimination between the MCI and CN groups, the Hopkins Verbal Learning Test-5 minutes Recall had the best performance, with the highest mean AUC of up to 0.80 (SD 0.02) and an F-score of up to 258.70. The scalability of model 5 (Hopkins Verbal Learning Test-5 minutes Recall and Trail Making Test-B) was the lowest. Model 5 achieved a higher level of discrimination than the Hong Kong Brief Cognitive test score in distinguishing between the MCI and CN groups (P<.05). Model 5 also provided the highest sensitivity of up to 0.82 (range 0.72-0.92) and 0.83 (range 0.75-0.91) according to LR and SVC, respectively. This model yielded a similar robust discriminative performance in the ADNI-3 cohort regarding differentiation between the MCI and CN groups, with a mean AUC of up to 0.81 (SD 0) according to both LR and SVC algorithms.

CONCLUSIONS

We developed a stable and scalable composite neurocognitive test based on ML that could differentiate not only between patients with MCI and controls but also between patients with different stages of cognitive impairment. This composite neurocognitive test is a feasible and practical digital biomarker that can potentially be used in large-scale cognitive screening and intervention studies.

Public Awareness, Knowledge, and Attitude Toward Alzheimer's Disease in Makkah, Saudi Arabia

BACKGROUND

Alzheimer's disease is a neurodegenerative disease that slowly deteriorates cognitive function over time. This condition disables the geriatric population worldwide. Knowing its symptoms and presentation could help the general population seek medical attention early.

OBJECTIVE

This study aims to assess the level of awareness, knowledge, and attitude towards Alzheimer's disease among the general population in Makkah City.

METHODS

This cross-sectional study employed an online questionnaire distributed randomly in Makkah, Saudi Arabia. A sociodemographic and attitude panel is included under each section of the questionnaire, as well as a knowledge panel based on the Alzheimer's Disease Knowledge Scale (ADKS). The knowledge and awareness level regarding Alzheimer's disease was determined by adding up discrete scores for each correct knowledge item. A participant's awareness level was categorized as poor if their score was less than 60%. Participants whose scores were 60% or higher were considered to have a high level of awareness Results: A total of 545 participants were investigated; 316 (58%) were females. A range of ages was represented among the participants, from 18 to over 60. Of the study respondents, 68 (12.5%) had an overall good awareness and knowledge of Alzheimer's disease and its management while 477 (87.5%) had a poor knowledge level. Among divorced/widowed participants, 16.2% had an overall good knowledge level of the disease compared to 8.3% of married respondents with recorded statistical significance (P=.049). Also, 20.4% of those with relatives diagnosed with Alzheimer's disease had good knowledge of the disease versus 10.7% of others without (P=.009).

CONCLUSION

According to the results, there is a lack of awareness and knowledge of Alzheimer's disease. This study suggests increasing public awareness and knowledge of Alzheimer's disease through campaigns and public education so that the disease is detected earlier.

Association between cardiopulmonary function, health-related quality of life and cognitive impairment among the older nursing home residents in Shanghai, China

BACKGROUND

This study aimed to examine the association between cardiopulmonary function, health-related quality of life (HRQOL) and cognitive function among nursing home residents aged 80 years and over.

METHODS

A nursing home-based, cross-sectional study was implemented among 677 aged over 80 years in Shanghai, China. A total of 197 participants underwent effective cardiopulmonary function examinations. Mini-Mental Status Examination (MMSE) and Short Form-36 scales (SF-36) were used to assess cognitive function and HRQOL, respectively.

RESULTS

Decline in left ventricular ejection fractions (LVEF) [adjusted odds ratio (AOR), 1.98; 95% confidential interval (CI), 1.03-3.81)] and vital capacity (VC) (AOR, 2.08; 95%CI, 1.07-4.04) was associated with cognitive impairment. After adjusting confounding factors, relationships between cognitive function and physical functioning (PF) (AOR, 0.98; 95%CI, 0.97-0.99) still existed.

CONCLUSIONS

Healthcare professionals should pay more attention to cardiopulmonary health and HRQOL in the nursing home residents. Actions of public health strategies focus on the improvement of cardiopulmonary function, and PF among older nursing home residents with cognitive impairment is required.

Knowledge, Awareness, and Attitude of Healthcare Stakeholders on Alzheimer's Disease and Dementia in Qatar

Dementia is characterized by progressive cognitive decline, memory impairment, and disability. Alzheimer's disease (AD) accounts for 60-70% of cases, followed by vascular and mixed dementia. Qatar and the Middle East are at increased risk owing to aging populations and high prevalence of vascular risk factors. Appropriate levels of knowledge, attitudes, and awareness amongst health care professionals (HCPs) are the need of the hour, but literature indicates that these proficiencies may be inadequate, outdated, or markedly heterogenous. In addition to a review of published quantitative surveys investigating similar questions in the Middle East, a pilot cross-sectional online needs-assessment survey was undertaken to gauge these parameters of dementia and AD among healthcare stakeholders in Qatar between 19 April and 16 May 2022. Overall, 229 responses were recorded between physicians (21%), nurses (21%), and medical students (25%), with two-thirds from Qatar. Over half the respondents reported that >10% of their patients were elderly (>60 years). Over 25% reported having contact with >50 patients with dementia or neurodegenerative disease annually. Over 70% had not undertake related education/training in the last 2 years. The knowledge of HCPs regarding dementia and AD was moderate (mean score of 5.3 ± 1.5 out of 7) and their awareness of recent advances in basic disease pathophysiology was lacking. Differences existed across professions and location of respondents. Our findings lay the groundwork for a call-to-action for healthcare institutions to improve dementia care within Qatar and the Middle East region.