Identification Of Dementia Research Articles

Stepwise identification of prodromal dementia: Testing a practical model for primary care.

Prodromal dementia is largely underdiagnosed in primary care. To develop a clinical model for detecting prodromal dementia within the operative boundaries of primary care practice. The study employed the Functional Activities Questionnaire (FAQ) and Montreal Cognitive Assessment (MoCA) to evaluate a "functional-cognitive" step-down screening model, in which the MoCA is administered subsequent to reported symptoms on the FAQ. It classified participants from the Alzheimer's Disease Imaging Initiative to three diagnostic categories: (1) healthy cognition (n = 396), (2) mild cognitive impairment without conversion (n = 430), and (3) prodromal dementia assessed 24 months before diagnosis (n = 164). Analyses indicated that the step-down model (Model 1) performed significantly better than an alternative model that applied the FAQ as a single measure (Model 2) and compared well with another model that administered both screening measures to all participants (Model 3). Gradient Boosting Trees classifications yielded the following estimations for Model 1/Model 2/ Model 3, respectively: Sensitivity = 0.87/0.77/0.89, Specificity = 0.68/0.47/0.70, PPV = 0.73/0.40/0.75, NVP = 0.84/0.81/0.87, F1 Score = 0.79/0.52/0.81, AUC = 0.78/0.67/0.79. These analyses support the proposed model. The study offers algorithms for validated measures, which were developed from a well characterized clinical sample. Their accuracy will likely improve further with new data from diverse clinical settings. These results can serve primary care in a timely manner in light of the recent advances in pharmacological treatment of dementia and the expected increase in demand for screening.

Factors associated with dementia among older people in Malaysia: Findings from National Health and Morbidity Survey.

To identify factors associated with dementia among older people in Malaysia. This study used data from a nationwide cross-sectional survey in Malaysia. Participants involved were older people aged 60 years and above. Data collected were on dementia risk factors as well as dementia screening. Dementia screening was done using the Identification and Intervention for Dementia in Elderly Africans cognitive screening tool. Univariate analysis and multiple logistic regression were carried out to determine the factors associated with dementia. There were 3774 participants involved in this study. Multiple logistic regression showed factors associated with dementia among older people were those aged 70 years and above, Indian ethnic group, being single, primary or no formal education, as well as those with hypertension. Interestingly, our findings also showed that older people with hypercholesterolemia have lower odds of having dementia. Multiple factors were associated with dementia in Malaysia, highlighting the need to implement multiple interventions strategies, by taking a lifetime approach emphasizing education, physical as well as social aspects.

A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation.

Mild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction. This study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset. This study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate. Among the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement. ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults.

Characteristics of and Risk Factors for Depressive Symptoms Preceding Dementia: A Study of 82-Year-Old Men From the Uppsala Longitudinal Study of Adult Men.

Depression and dementia are known to be associated. The identification of characteristics distinguishing depression prodromal to dementia from other depressive symptoms would be of value for early identification of dementia. The study of risk factors for depressive symptoms prodromal to dementia could improve preventive care and provide clues to the causes of dementia. Dementia-free 82-year-old participants were stratified into groups that did (n=126) and did not (n=378) subsequently develop dementia. Examinations took place from 2003 to 2005 and follow-up ended 1 January 2015. Their baseline characteristics and depressive symptoms, measured using the 15-item Geriatric Depression Scale (GDS-15), were compared. Multivariate regression analyses were performed for the two groups separately, with the total GDS-15 score as the dependent variable. The groups did not differ significantly in answers to any of the GDS-15 questions, or mean±SD score, which was 2.4±2.5 among those who developed dementia and 2.1±2.3 among those who did not. (p=0.33). Stroke before the age of 82years and the inability to use stairs had significant impacts on the GDS-15 scores in both groups. For those who did not develop dementia, age, dependence in activities of daily living, and cancer also had significant impacts. Cancer had opposite associations with depressive symptoms in the two groups. No difference was found in depressive symptoms preceding and not preceding dementia using the GDS-15. The results suggest that risk factors for depressive symptoms may differ depending on whether they precede dementia.

Guideline summary: assessment, diagnosis, care and support for people with dementia and their carers [Scottish Intercollegiate Guidelines Network SIGN Guideline 168

The Scottish Intercollegiate Guidelines Network (SIGN) have recently published their guideline SIGN168 on 'Assessment, Diagnosis, Care, and Support for People with Dementia and their Carers'. The guideline makes evidence-based recommendations for best practice in the assessment, care and support of adults living with dementia. Topics featured in this guideline are limited to those prioritised by stakeholders, especially people with lived and living experience, and those not well covered under pre-existing guidance. We summarise the guideline recommendations related to identification and diagnosis of dementia, investigative procedures, postdiagnostic support living with dementia, including non-pharmacological approaches for distressed behaviours, using technology to support people with dementia, grief and dementia and changing needs of people with dementia. The guideline content is summarised as officially published, with additional commentary in the final section.

The Hong Kong version of Montreal Cognitive Assessment for the Visually Impaired (HKMoCA-VI): Proposed cut-off and cognitive functioning survey of visually impaired elderly in residential homes.

Visual impairment has been strongly associated with the incidence of dementia. Appropriate cognitive screening for the elderly with visual impairment is crucial for early identification of dementia and its management. Due to challenges in processing visually presented stimuli among participants, the cut-off score of the Hong Kong version of the Montreal Cognitive Assessment for the Visually Impaired (HKMoCA-VI), also known as MoCA-BLIND or MoCA-22, was unknown. Besides, the cognitive status of elderly with visual impairment residing in care homes is rarely investigated. The current study aimed to 1) establish the cut-off score for HKMoCA-VI and 2) examine the general cognitive functioning of elderly with visual impairment living in residential homes in Hong Kong in terms of MoCA-VI percentile scores. HKMoCA-VI and the Cantonese version of the Mini-Mental State Examination (CMMSE) were administered to 123 visually impaired elderly residents in care homes in Hong Kong. Percentile scores of HKMoCA-VI by age and education level were determined, and the concurrent validity, sensitivity, and specificity of HKMoCA-VI were assessed. A cut-off score 12 was suggested for HKMoCA-VI, which yielded a sensitivity and specificity of 89.29% and 83.58%, respectively. Moreover, it strongly correlated with CMMSE, indicating satisfactory concurrent validity. HKMoCA-VI is suggested to be a viable cognitive screening tool for elderly individuals with visual impairment in residential homes. Further modifications to enhance the sensitivity and specificity of the measure are proposed.

Care needs of people with dementia in Tanzania and associated impact on carers: A cross-sectional, observational study.

Objectives: This study aimed to understand the care needs, care arrangements and burden of care for people with dementia in Northern Tanzania. Methods: This was a cross-sectional, observational study. People with dementia and their carers (n = 53) were recruited from an outpatient clinic, and data on carer burden and independence in activities of daily living were collected. Associations with carer burden and characteristics were explored through non-parametric tests and regression analyses. Results: Thirty-six carers were female (68%). Levels of impairment in instrumental activities of daily living were high, with a median score of 38 out of 44 on the Identification and Intervention for Dementia in Elderly Africans - Instrumental Activities of Daily Living (IDEA-IADL). Carer burden was moderate with a median Zarit Burden Interview (ZBI) score of 46 out of 88. Being a female carer was associated with higher carer burden (odds ratio 3.68, 95% CI 1.04-12.99). Discussion: Carer burden was found to be higher than in previous studies based in low-and-middle income countries. Further research is needed to explore this difference, and to identify interventions to support care needs and reduce carer burden.

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

A comparison of dementia diagnoses and cognitive function measures in Medicare claims and the Minimum Data Set.

Gold standard dementia assessments are rarely available in large real-world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims-based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three. Using a 20% random sample of 2018-2019 Medicare beneficiaries, we identified MDS admission assessments for non-skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims-based algorithm for Alzheimer's disease and non-Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non-Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS. Among 346,013 non-SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%). Claims- and MDS-based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population.

Nurses' perspectives on shared decision-making in the daily care of hospitalized patients with dementia: An exploratory qualitative study.

Gain insight into the process of shared decision-making (SDM) in daily hospital care for patients with dementia from nurses' perspectives. Explorative qualitative design. In-depth digital interviews were conducted with 14 registered nurses between June and November 2022. A phenomenological approach was applied using Colaizzi's seven-step method. Five themes were identified in the data: (1) SDM in daily care: How shared decision-making is applied; (2) Nurses' perceptions and competence: How nurses perceive and manage SDM; (3) Nurses' roles and advocacy: The evolving roles of nurses and their advocacy efforts, (4) Recognition of dementia and its impact: How nurses recognize and manage dementia; and (5) Interventions to support SDM: Strategies and interventions to facilitate SDM. This study highlights the complexity of SDM in patients with dementia. It demonstrates the importance of the involvement of relatives, omission of patient goals in discussions and perceived deficiencies of nurses. The early identification of dementia, evaluation of nuanced capacity and targeted communication are essential. Further research and enhanced training are required to improve care in this context. Potential areas for further research on SDM in nurses involving patients with dementia include investigating the effects of integrating goal discussions into SDM training for nurses, overcoming barriers to SDM competence, and challenging the idea that SDM is solely the responsibility of physicians. These findings highlight the need for policies that encourage interdisciplinary collaboration, address misconceptions and recommend training programmes that focus on applying SDM to the daily care of patients with dementia, thereby improving the overall quality of patient care. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist was used for reporting. No patient or public contribution.

Which factors preceding dementia identification impact future healthcare use trajectories: multilevel analyses in administrative data

BackgroundHealthcare use patterns preceding a diagnosis of Alzheimer’s Disease and Related Diseases (ADRD) may be associated with the quality of healthcare use trajectories (HUTs) after diagnosis. We aimed to identify determinants of future favorable HUTs, notably healthcare use preceding ADRD identification.MethodsThis nationwide retrospective observational study was conducted on subjects with incident ADRD identified in 2012 in the French health insurance database. We studied the 12-month healthcare use ranging between 18 and 6 months preceding ADRD identification. The five-year HUTs after ADRD identification were qualified by experts as favorable or not. In order to take into account geographical differences in healthcare supply, we performed mixed random effects multilevel multivariable logistic regression model to identify determinants of future favorable HUTs. Analyses were stratified by age group (65–74, 75–84, ≥ 85).ResultsBeing a woman, and preventive and specialist care preceding ADRD identification increased the probability of future favorable HUT, whereas institutionalization, comorbidities, medical transportation and no reimbursed drug during [-18;-6] months decreased it. Besides, some specificities appeared according to age groups. Among the 65–74 years subjects, anxiolytic dispensing preceding ADRD identification decreased the probability of future favorable HUT. In the 75–84 years group, unplanned hospitalization and emergency room visit preceding ADRD identification decreased this probability. Among subjects aged 85 and older, short hospitalization preceding ADRD identification increased the probability of future favorable HUTs.ConclusionRegular healthcare use with preventive and specialist care preceding ADRD identification increased the probability of future favorable HUTs whereas dependency decreased it.

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment. 371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors. The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment. ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

Mixed Dementia Alzheimer and Lewy body with congenital diseases: Case Study

Objectives: Dementia due to Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are the diseases that appear together, especially in elderly people. Still, clinical identification of dementia is difficult in such circumstances. Therefore, we conducted a case study with a comprehensive prospective clinical assessment and complete mental, physical, and psychiatric examination to diagnose such mixed cases clinically and for curative management of mixed dementia of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). Case: a 76 years old woman have anxiety that fluctuated. The history of medical disorders, physical examination and supporting examinations, was found that the patient had hypertension, dyslipidemia, hypothyroidism in treatment, Barrett's esophagus in therapy, osteoarthritis genu dextra, history of malnutrition, breast cancer and hysterectomy. The patient was advised to administer antipsychotics because the patient's agitation was severe and the caregiver's distress was high. The patient improved with pharmacological therapy and family monitoring support. Conclusion: The diagnosis of mixed dementia patients (Alzheimer's disease (AD) and dementia with Lewy bodies (DLB)) with a medical history of congenital disease has overlapping symptoms so that very selective drug treatment is needed so as not to worsen the condition of other diseases. The supportive role of the family can help cure mixed Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) by creating a logbook of daily behavior and symptoms that appear in the patient.

THE MAKING OF AFRICAN MADE: MEMORY AND DEMENTIA EDUCATION BY AND FOR THE AFRICAN IMMIGRANT COMMUNITY

Abstract African immigrants are a fast-growing segment of the U.S. Black population but dementia literacy and care needs of this population are not fully understood. A dementia care assessment in the community highlighted dementia education as a high priority need in the African immigrant community in Minnesota. This presentation will describe the collaborative process between the research team and a community project advisory board (PAB) to develop a culturally tailored dementia education program and booklet. Following several meetings and reviews, the PAB approved an interview guide that was used to conduct 10 key informant interviews (with leaders including priest, executive directors etc.) and three community conversations (30 participants). This data was used to identify important cultural dimensions for incorporation into the education program. Themes identified were need for respect, attitudes, beliefs and challenges related to dementia care. Identification and debunking of dementia and dementia care myths were also highlighted. The resulting educational content has been presented at 3 community events/organizations with many more scheduled in spring 2023. The African community in Minnesota is very receptive of the education program with many individuals noting that it truly captures African immigrant cultural norms and values. The collaborative process between the research team and the advisory board could serve as a model for creating culturally tailored dementia education programs in other communities. Additionally, successful implementation of this education program has implications for improving dementia care among African immigrants and reducing health disparities in this population.

Including drawings in future dementia research: identification and modification of negative stereotyping

AbstractBackgroundMost countries in Western civilization are segregated societies with little or no contact between generations. Elementary schools and nursing homes are separated worlds in proximity. Children alienate further from the elderly and rely on indirect experiences with old age and dementia. The elderly can embody negative stereotypes from media and society leading to disparities in health care, social isolation and loss of functional ability.MethodIn this study we collected 796 drawings from 98 children in 4 elementary schools in Belgium and The Netherlands. The 10‐y‐old pupils made portraits of grandparents and the elderly in neutral, happy and unhappy conditions. The standardized drawing assignments were repeated after a psychosocial intervention program of 16 weeks to connect generations. The effects of non‐pharmacological interventions in Alzheimer’s research are mostly studied with classical questionnaires. These tools are insufficient to detect unconscious motives and subtle mental representations of old age and dementia. Before and after drawings are an added value to the field of research. The implicit measurements circumvent social desirability, go beyond the level of awareness and give a clearer picture of negative stereotypes of old age and dementia.ResultThe content analysis of the drawings demonstrated that children represent ‘happy elderly people’ as: outdoors, active, and surrounded by others, while ‘unhappy elderly people’ are generally portrayed indoors, sedentary and lonely.ConclusionChildren’s drawings as implicit measurement are an added value for non‐pharmacological interventions. The act of drawing is pleasant, easy and effective for the identification and modification negative stereotyping of old age and dementia.

Challenges in identification of HIV‐Associated Neurocognitive Disorders, a focus on low and middle income countries

AbstractBackgroundHIV‐associated neurocognitive disorders (HAND) are highly prevalent amongst people living with HIV (PLWH), especially in Sub‐Saharan Africa (SSA) where the majority of PLWH live. Current HAND identification measures lack accuracy in older PLWH on treatment, with substantial heterogeneity observed in SSA prevalence meta‐analyses There is a need to better understand the current cognitive phenotype of this newly emergent population, to develop appropriate screening tools. We report two linked studies.Study 1 compared diagnostic accuracy of the International HIV dementia scale (IHDS), and Identification and Interventions for Dementia in Elderly Africans (IDEA) screen, for identification of HAND in older PLWH in TanzaniaStudy 2 aimed to identify cognitive domains significantly associated with symptomatic HAND (s‐HAND) in older PLWH in Tanzania, with potential utility in future screening measures.MethodA systematic sample of 253 PLWH aged ≥ 50 attending a Government clinic underwent comprehensive HAND evaluation by consensus American Academy of Neurology (AAN) criteria including locally‐normed low‐literacy clinical neuropsychological assessment. A diagnostic accuracy study of the IHDS and IDEA screen versus AAN criteria was completed with strict blinding. Participants were offered identical annual reassessment at Y1 Y2 and Y3 and the relationship of cognitive domains with s‐HAND in a logistic mixed effects modelResultHAND was highly prevalent (47%) at baseline, increasing to 57.9% at year 3.At baseline, both IHDS and IDEA had limited diagnostic accuracy for HAND (AUROC 0.639–0.667 IHDS, 0.647–0.713 IDEA). On secondary analysis of longitudinal data, tests of verbal learning and memory, categorical verbal fluency, visual memory and visuoconstruction had AUROC > 0.7 for s‐HAND (0.70 ‐ 0.72; p<0.001 for all). Tests of visual memory, verbal learning and memory (delayed recall and recognition memory), psychomotor speed, language comprehension and categorical verbal fluency were independently associated with s‐HAND (p<0.01 for all). Neuropsychological impairment pattern varied by education.ConclusionCurrent HAND screening tests no longer perform well in the new population of older PLWH in Africa. Tests most associated with s‐HAND include broad cortical and subcortical domains, some not included in existing HAND screening tests and vary by educational background. This should be considered in future HAND screening measure development.

Identifying probable dementia in undiagnosed Black and White Americans using machine learning in Veterans Health Administration electronic health records

AbstractBackgroundThe application of machine learning (ML) tools in electronic health records (EHRs) can help reduce the underdiagnosis of dementia, but models that are not designed to reflect minority population may perpetuate that underdiagnosis.MethodTo address the underdiagnosis of dementia in both Black Americans (BAs) and white Americans (WAs), we sought to develop and validate ML models that assign race‐specific risk scores. These scores were used to identify undiagnosed dementia in BA and WA Veterans in EHRs. More specifically, risk scores were generated separately for BAs (n = 10K) and WAs (n = 10K) in training samples of cases and controls by performing ML, equivalence mapping, topic modeling, and a support vector‐machine (SVM) in structured and unstructured EHR data. Scores were validated via blinded manual chart reviews (n = 1.2K) of controls from a separate sample (n = 20K). AUCs and negative and positive predictive values (NPVs and PPVs) were calculated to evaluate the models.ResultAmong Veterans with undiagnosed dementia who underwent chart review, those diagnosed by expert clinician reviewers with possible/probable dementia had higher ADRD ML risk scores compared to those diagnosed with no dementia (0.45 [0.38] vs. ‐0.02 [0.51] for BAs, and 0.38 [0.41] vs. ‐0.02 [0.47] for WAs; (see figure 1).Of the 1,200 Veterans who underwent chart review, 15.3% (n = 92) of BAs and 9.5% (n = 57) of WAs were identified with possible/probable dementia. However, adjusting for oversampling of Veterans with higher scores, the adjusted estimated prevalence was 4.1% for BA Veterans and 3.6% for WA Veterans. There was a strong positive relationship between risk scores and the prevalence of undiagnosed dementia (Figure 2). As anticipated, for Veterans with scores below the 90th percentile, the percentages of undiagnosed dementia were low: 3.9% for BA and 2.9% for WA Veterans. Among Veterans with scores above the 90th percentile, we found that a higher percentage of BA Veterans had undiagnosed dementia than WA Veterans: 25.6% vs. 15.3%.ConclusionOur findings suggest that race‐specific ML models can assist in the identification of undiagnosed dementia, particularly in BAs. Future studies should investigate implementing EHR‐based risk scores in clinics that serve both BA and WA Veterans.

Race/ethnic disparities in mis‐identification of dementia in Medicare claims

AbstractBackgroundStudies have shown that mis‐identification of dementia in Medicare claims is quite common. We extend the literature by examining potential race/ethnic disparities in mis‐identification of dementia in Medicare claims in a diverse cohort of older adults who underwent careful clinical assessment.MethodParticipants were enrolled the Washington Heights‐Inwood Columbia Aging Project (WHICAP), a multiethnic, population‐based, prospective study of cognitive aging in which dementia status is assessed using a rigorous clinical protocol. The current study included 1,178 participants clinically diagnosed with dementia and 3,048 never diagnosed with dementia. ICD‐9‐CM diagnosis codes in all available Medicare claims (1999‐2019) were compared to clinical dementia diagnosis, and categorized into 3 mutually exclusive groups: (1) congruent identification, (2) over‐identification (no clinical dementia, claims‐identified dementia), and (3) under‐identification (clinical dementia, no claims‐identified dementia) during the study period. Race (White, African American/Black, Other) and ethnicity (Hispanic/Latinx, non‐Hispanic/Latinx) were self‐reported. Multinomial logistic regression model was used to examine the relationship between race/ethnicity and congruency of dementia identification after controlling for clinical (cognition, function, comorbidities) and demographic characteristics (age, sex, education), and inpatient and outpatient utilization. Estimated average marginal effects are reported.ResultThe study included 1,520 White, 1,333 African American/Black, and 1,373 participants of other races. 1,985 participants self‐identified as Hispanic, 2,241 non‐Hispanic. Among participants clinically diagnosed with dementia, the proportion who were accurately identified with dementia in Medicare claims were 83.2%, 77.8%, and 73.2% in White, African American/Black, and other races, respectively; 75.9% and 79.7% in Hispanics and non‐Hispanics, respectively. Among participants clinically diagnosed without dementia, the proportion of participants who were accurately identified without dementia in Medicare claims were: 86.1%, 87.3%, and 82.3% in White, African American/Black, and other races, respectively; 82.4% and 87.5% in Hispanics and non‐Hispanics, respectively. After controlling for participant characteristics, multinomial logistics regression results show the probability of over‐identification with dementia was 2.2% higher for African American/Black participants than White (p = 0.05) and 2.7% higher for Hispanic participants than non‐Hispanics (p = 0.03). Differences in under‐identification by race/ethnicity were no longer statistically significant.ConclusionAfrican American/Black and Hispanic participants were more likely to be over‐identified with dementia in Medicare claims.

Access to Primary Healthcare and Discussion of Memory Loss with a Healthcare Provider in Adults with Subjective Cognitive Decline: Does Race/Ethnicity Matter?

Because subjective cognitive decline (SCD) manifests before the clinical and irreversible onset of dementia, efforts to address SCD are imperative for the early identification and prevention of dementia. For people who actually experience SCD, having a usual source of care may be an important catalyst for addressing memory issues. This study investigates the relationship between having a usual source of care and the discussion of SCD with healthcare providers, while taking into account racial and ethnic differences. The pooled 2019-2020 Behavioral Risk Factor Surveillance System (BRFSS) from 47 states in the U.S. was used. The sample included 7900 individuals aged 50 and older who reported significant cognitive decline. Using logistic regression with state-level clustering, we found that those with a usual source of healthcare were almost twice as more likely to discuss memory loss with a healthcare provider. In order to test for the multiplicative effects, we included an interaction term between each race category and having a primary care provider (PCP). There was a significant interaction effect between Hispanic ethnicity and having a PCP (OR = 0.25, p < 0.05). While non-Hispanic respondents who had a PCP had a higher probability of discussing memory issues with a healthcare provider (42% vs. 25%), Hispanic respondents with a PCP were less likely to discuss their memory loss with a healthcare provider (36% vs. 49%). The study adds to the growing literature on the vital role of the primary care service in provision of service to those with cognitive decline, while revealing ethnic disparity in memory-loss-related communication with healthcare providers in a primary care setting.

Alerting providers to hospitalized persons with dementia using the electronic health record.

While patients with dementia entering the hospital have worse outcomes than those without dementia, early detection of dementia in the inpatient setting is less than 50%. We developed and assessed the positive predictive value (PPV) and feasibility of a novel electronic health record (EHR) banner to identify patients with dementia who present to the inpatient setting using data from the medical record. We developed and implemented an EHR algorithm to flag hospitalized patients age ≥65 years with potential cognitive impairment in the Epic EHR system using dementia ICD-10 codes, FDA-approved medications, and the use of the term "dementia" in the emergency department physician note. Medical records were reviewed for all patients who were flagged with an EHR banner from October 2022 to May 2023. A total of 344 individuals were identified who had a banner on their chart of which 280 (81.4%) were either diagnosed with dementia or were on an FDA-approved dementia medication. Forty-three individuals who had confirmed dementia were identified by a medication only (15.4%). Of the patients without confirmed dementia, the majority (N = 33, 9.6%) had a diagnosis of altered mental status, cognitive dysfunction, or mild cognitive impairment. Only 31 individuals (9.0%) had no indication of dementia or cognitive decline in their problem list, past medical history, or medication list. We found that it was feasible to implement an EHR algorithm for prospective dementia identification with a high PPV. These types of algorithms provide an opportunity to accurately identify hospitalized older individuals for inclusion in quality improvement projects, clinical trials, pay-for-performance programs, and other initiatives.