The assessment of dementia in primary care

Markers of dementia-related health in primary care electronic health records

OBJECTIVES

Identifying routinely recorded markers of poor health in patients with dementia may help treatment decisions and evaluation of earlier outcomes in research. Our objective was to determine whether a set of credible markers of dementia-related health could be identified from primary care electronic health records (EHR).

METHODS

The study consisted of (i) rapid review of potential measures of dementia-related health used in EHR studies; (ii) consensus exercise to assess feasibility of identifying these markers in UK primary care EHR; (iii) development of UK EHR code lists for markers; (iv) analysis of a regional primary care EHR database to determine further potential markers; (v) consensus exercise to finalise markers and pool into higher domains; (vi) determination of 12-month prevalence of domains in EHR of 2328 patients with dementia compared to matched patients without dementia.

RESULTS

Sixty-three markers were identified and mapped to 13 domains: Care; Home Pressures; Severe Neuropsychiatric; Neuropsychiatric; Cognitive Function; Daily Functioning; Safety; Comorbidity; Symptoms; Diet/Nutrition; Imaging; Increased Multimorbidity; Change in Dementia Drug. Comorbidity was the most prevalent recorded domain in dementia (69%). Home Pressures were the least prevalent domain (1%). Ten domains had a statistically significant higher prevalence in dementia patients, one (Comorbidity) was higher in non-dementia patients, and two (Home Pressures, Diet/Nutrition) showed no association with dementia.

CONCLUSIONS

EHR captures important markers of dementia-related health. Further research should assess if they indicate dementia progression. These markers could provide the basis for identifying individuals at risk of faster progression and outcome measures for use in research.

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease

Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

Indicators of dementia disease progression in primary care: An electronic health record cohort study

BACKGROUND AND PURPOSE

The objectives were to assess the feasibility and validity of using markers of dementia-related health as indicators of dementia progression in primary care, by assessing the frequency with which they are recorded and by testing the hypothesis that they are associated with recognised outcomes of dementia. The markers, in 13 domains, were derived previously through literature review, expert consensus, and analysis of regional primary care records.

METHODS

The study population consisted of patients with a recorded dementia diagnosis in the Clinical Practice Research Datalink, a UK primary care database linked to secondary care records. Incidence of recorded domains in the 36 months after diagnosis was determined. Associations of recording of domains with future hospital admission, palliative care, and mortality were derived.

RESULTS

There were 30,463 people with diagnosed dementia. Incidence of domains ranged from 469/1000 person-years (Increased Multimorbidity) to 11/1000 (Home Pressures). An increasing number of domains in which a new marker was recorded in the first year after diagnosis was associated with hospital admission (hazard ratio for ≥4 domains vs. no domains = 1.24; 95% confidence interval = 1.15-1.33), palliative care (1.87; 1.62-2.15), and mortality (1.57; 1.47-1.67). Individual domains were associated with outcomes with varying strengths of association.

CONCLUSIONS

Feasibility and validity of potential indicators of progression of dementia derived from primary care records are supported by their frequency of recording and associations with recognised outcomes. Further research should assess whether these markers can help identify patients with poorer prognosis to improve outcomes through stratified care and targeted support.

Mobile memory clinic: implementing a nurse practitioner-led, collaborative dementia model of care within general practice

The limited capacity of secondary health services to address the increasing prevalence of dementia within the community draws attention to the need for an enhanced role for nurses working collaboratively with GPs in diagnosing and coordinating post-diagnostic care for patients with dementia. This study investigated the feasibility and acceptability of a nurse practitioner-led mobile memory clinic that was embedded within general practice and targeted to caring for patients and their carers in areas of socioeconomic disadvantage with poor access to specialist health services. Over the period from mid-2013 to mid-2014, 40 GPs referred 102 patients, with the nurse practitioner conducting assessments with 77 of these patients in their homes. Overall, there was a strong interest in this model of care by general practice staff, with the assessment and care provided by the nurse practitioner evaluated as highly acceptable by both patients and their carers. Nonetheless, there are financial and structural impediments to this model of care being implemented within the current Australian health service framework, necessitating further research investigating its cost-effectiveness and efficacy.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Health professionals' perceptions of developing dementia services in primary care settings in China: a qualitative study

OBJECTIVES

Primary care plays a crucial role in the timely diagnosis and proper management of dementia. Evidence from low and middle income countries is much needed to inform service development in primary care and to address the dementia burden in these countries. The aim of this study was to explore community health professionals' perceptions of dementia service development using China as a case.

METHOD

An interpretive study design was utilized and focus groups were used for data collection guided by a semi-structured interview guide. Each focus group lasted between 90-120 min. Thematic analysis was applied for data analysis.

RESULTS

Twenty-one community health professionals participated in this study and three major themes were identified. These themes are: incorporating dementia components in the government-subsidized primary care services; an under-prepared workforce to meet the demand for dementia care; and an enabling environment to sustain dementia care.

CONCLUSION

Government policies, regulations, standards and guidelines need to be established for dementia service development in primary care to improve the home care for people with dementia and to create a dementia-friendly society. Regular education and training activities for health professionals are a way to build dementia care service capacity in primary care.

Digital Support Platform: a qualitative research study investigating the feasibility of an internet-based, postdiagnostic support platform for families living with dementia

OBJECTIVES

To establish the feasibility of the Digital Support Platform (DSP), an internet-based, postdiagnostic tool designed for families living with a diagnosis of dementia.

DESIGN

Qualitative methods using normalisation process theory as an analysis framework for semistructured interview transcriptions.

SETTING

A community care setting in the South-East Scotland.

PARTICIPANTS

We interviewed 10 dyads of people with Alzheimer's, vascular or mixed dementia (PWD), and their family carers, who had been given and had used the DSP for at least 2 months.

RESULTS

Our analysis revealed that the DSP was predominantly understood and used by the carers rather than PWD, and was used alongside tools and methods they already used to care for their relative. The DSP was interpreted as a tool that may be of benefit to those experiencing later stages of dementia or with physical care needs. Carers stated that the DSP may be of benefit in the future, reflecting a disinclination to prepare for or anticipate for future needs, rather than focus on those needs present at the time of distribution. PWD spoke positively about an interest in learning to use technology more effectively and enjoyed having their own tablet devices.

CONCLUSIONS

The DSP was not wholly appropriate for families living with dementia in its early stages. The views of carers confirmed that postdiagnostic support was valued, but emphasised the importance of tailoring this support to the exact needs and current arrangements of families. There may be a benefit to introducing, encouraging, providing and teaching internet-enabled technology to those PWD who do not currently have access. Training should be provided when introducing new technology to PWD.