Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data

Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record-based approach

BACKGROUND

We sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease (AD).

METHOD

Using EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR-based machine learning model for predicting AD onset within 10 years.

RESULTS

Applied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63-0.77). Important predictive factors included cardiovascular factors and laboratory blood testing.

CONCLUSION

Routinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction

The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care.

Objectives

We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers.

Methods

SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors.

Results

From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years.

Conclusion

We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment

Dementia has a large negative impact on the global healthcare and society. Diagnosis is rather challenging as there is no standardised test. The purpose of this paper is to conduct an analysis on ADNI data and determine its effectiveness for building classification models to differentiate the categories Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Dementia (DEM), based on tuning three Deep Learning models: two Multi-Layer Perceptron (MLP1 and MLP2) models and a Convolutional Bidirectional Long Short-Term Memory (ConvBLSTM) model. The results show that the MLP1 and MLP2 models accurately distinguish the DEM, MCI and CN classes, with accuracies as high as 0.86 (SD 0.01). The ConvBLSTM model was slightly less accurate but was explored in view of comparisons with the MLP models, and for future extensions of this work that will take advantage of time-related information. Although the performance of ConvBLSTM model was negatively impacted by a lack of visit code data, opportunities were identified for improvement, particularly in terms of pre-processing.

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches

BACKGROUND

Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.

METHODS

We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination.

RESULTS

The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing.

CONCLUSIONS

Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.

The value of the GP's clinical judgement in predicting dementia: a multicentre prospective cohort study among patients in general practice

BACKGROUND

Clinical judgement is intrinsic to diagnostic strategies in general practice; however, empirical evidence for its validity is sparse.

AIM

To ascertain whether a GP's global clinical judgement of future cognitive status has an added value for predicting a patient's likelihood of experiencing dementia.

DESIGN AND SETTING

Multicentre prospective cohort study among patients in German general practice that took place from January 2003 to October 2016.

METHOD

Patients without baseline dementia were assessed with neuropsychological interviews over 12 years; 138 GPs rated the future cognitive decline of their participating patients. Associations of baseline predictors with follow-up incident dementia were analysed with mixed-effects logistic and Cox regression.

RESULTS

A total of 3201 patients were analysed over the study period (mean age = 79.6 years, 65.3% females, 6.7% incident dementia in 3 years, 22.1% incident dementia in 12 years). Descriptive analyses and comparison with other cohorts identified the participants as having frequent and long-lasting doctor-patient relationships and being well known to their GPs. The GP baseline rating of future cognitive decline had significant value for 3-year dementia prediction, independent of cognitive test scores and patient's memory complaints (GP ratings of very mild (odds ratio [OR] 1.97, 95% confidence intervals [95% CI] = 1.28 to 3.04); mild (OR 3.00, 95% CI = 1.90 to 4.76); and moderate/severe decline (OR 5.66, 95% CI = 3.29 to 9.73)). GPs' baseline judgements were significantly associated with patients' 12-year dementia-free survival rates (Mantel-Cox log rank test P<0.001).

CONCLUSION

In this sample of patients in familiar doctor-patient relationships, the GP's clinical judgement holds additional value for predicting dementia, complementing test performance and patients' self-reports. Existing and emerging primary care-based dementia risk models should consider the GP's judgement as one predictor. Results underline the importance of the GP-patient relationship.

Risk classification for conversion from mild cognitive impairment to Alzheimer's disease in primary care

There is a pressing need to identify individuals at high risk of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on available repeated cognitive measures in primary care. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we applied a joint latent class mixed model (JLCM) to derive a 3-class solution: low risk (72.65%), medium risk (20.41%) and high risk (6.94%). In the low-risk group, individuals with lower daily activity and ApoEε4 carriers were at greater risk of conversion from MCI to AD. In the medium-risk group, being female, single, and an ApoEε4 carrier increased risk of conversion to AD. In the high-risk group, individuals with lower education level and single individuals were at greater risk of conversion to AD. Individual dynamic prediction for conversion from MCI to AD after 10 years was derived. Accurate identification of conversion from MCI to AD contributes to earlier close monitoring, appropriate management, and targeted interventions. Thereby, it can reduce avoidable hospitalizations for the high-risk MCI population. Moreover, it can avoid expensive follow-up tests that may provoke unnecessary anxiety for low-risk individuals and their families.

Anticholinergic Drug Exposure and the Risk of Dementia: A Nested Case-Control Study

IMPORTANCE

Anticholinergic medicines have short-term cognitive adverse effects, but it is uncertain whether long-term use of these drugs is associated with an increased risk of dementia.

OBJECTIVE

To assess associations between anticholinergic drug treatments and risk of dementia in persons 55 years or older.

DESIGN, SETTING, AND PARTICIPANTS

This nested case-control study took place in general practices in England that contributed to the QResearch primary care database. The study evaluated whether exposure to anticholinergic drugs was associated with dementia risk in 58 769 patients with a diagnosis of dementia and 225 574 controls 55 years or older matched by age, sex, general practice, and calendar time. Information on prescriptions for 56 drugs with strong anticholinergic properties was used to calculate measures of cumulative anticholinergic drug exposure. Data were analyzed from May 2016 to June 2018.

EXPOSURES

The primary exposure was the total standardized daily doses (TSDDs) of anticholinergic drugs prescribed in the 1 to 11 years prior to the date of diagnosis of dementia or equivalent date in matched controls (index date).

MAIN OUTCOMES AND MEASURES

Odds ratios (ORs) for dementia associated with cumulative exposure to anticholinergic drugs, adjusted for confounding variables.

RESULTS

Of the entire study population (284 343 case patients and matched controls), 179 365 (63.1%) were women, and the mean (SD) age of the entire population was 82.2 (6.8) years. The adjusted OR for dementia increased from 1.06 (95% CI, 1.03-1.09) in the lowest overall anticholinergic exposure category (total exposure of 1-90 TSDDs) to 1.49 (95% CI, 1.44-1.54) in the highest category (>1095 TSDDs), compared with no anticholinergic drug prescriptions in the 1 to 11 years before the index date. There were significant increases in dementia risk for the anticholinergic antidepressants (adjusted OR [AOR], 1.29; 95% CI, 1.24-1.34), antiparkinson drugs (AOR, 1.52; 95% CI, 1.16-2.00), antipsychotics (AOR, 1.70; 95% CI, 1.53-1.90), bladder antimuscarinic drugs (AOR, 1.65; 95% CI, 1.56-1.75), and antiepileptic drugs (AOR, 1.39; 95% CI, 1.22-1.57) all for more than 1095 TSDDs. Results were similar when exposures were restricted to exposure windows of 3 to 13 years (AOR, 1.46; 95% CI, 1.41-1.52) and 5 to 20 years (AOR, 1.44; 95% CI, 1.32-1.57) before the index date for more than 1095 TSDDs. Associations were stronger in cases diagnosed before the age of 80 years. The population-attributable fraction associated with total anticholinergic drug exposure during the 1 to 11 years before diagnosis was 10.3%.

CONCLUSIONS AND RELEVANCE

Exposure to several types of strong anticholinergic drugs is associated with an increased risk of dementia. These findings highlight the importance of reducing exposure to anticholinergic drugs in middle-aged and older people.

Development and Validation of a Dementia Risk Prediction Model in the General Population: An Analysis of Three Longitudinal Studies

OBJECTIVE

Identification of individuals at high risk of dementia is essential for development of prevention strategies, but reliable tools are lacking for risk stratification in the population. The authors developed and validated a prediction model to calculate the 10-year absolute risk of developing dementia in an aging population.

METHODS

In a large, prospective population-based cohort, data were collected on demographic, clinical, neuropsychological, genetic, and neuroimaging parameters from 2,710 nondemented individuals age 60 or older, examined between 1995 and 2011. A basic and an extended model were derived to predict 10-year risk of dementia while taking into account competing risks from death due to other causes. Model performance was assessed using optimism-corrected C-statistics and calibration plots, and the models were externally validated in the Dutch population-based Epidemiological Prevention Study of Zoetermeer and in the Alzheimer's Disease Neuroimaging Initiative cohort 1 (ADNI-1).

RESULTS

During a follow-up of 20,324 person-years, 181 participants developed dementia. A basic dementia risk model using age, history of stroke, subjective memory decline, and need for assistance with finances or medication yielded a C-statistic of 0.78 (95% CI=0.75, 0.81). Subsequently, an extended model incorporating the basic model and additional cognitive, genetic, and imaging predictors yielded a C-statistic of 0.86 (95% CI=0.83, 0.88). The models performed well in external validation cohorts from Europe and the United States.

CONCLUSIONS

In community-dwelling individuals, 10-year dementia risk can be accurately predicted by combining information on readily available predictors in the primary care setting. Dementia prediction can be further improved by using data on cognitive performance, genotyping, and brain imaging. These models can be used to identify individuals at high risk of dementia in the population and are able to inform trial design.

The association of vascular disorders with incident dementia in different age groups

Background

There is increasing evidence that dementia risk associated with vascular disorders is age dependent. Large population-based studies of incident dementia are necessary to further elucidate this effect. Therefore, the aim of the present study was to determine the association of vascular disorders with incident dementia in different age groups in a large primary care database.

Methods

We included 442,428 individuals without dementia aged ≥ 65 years from the longitudinal primary care Integrated Primary Care Information (IPCI) database. We determined in 6 age groups (from 65–70 to ≥ 90 years) the risk of hypertension, diabetes mellitus, dyslipidemia, stroke, myocardial infarction, heart failure, and atrial fibrillation for all-cause dementia using incidence rate ratios, Cox regression, and Fine and Gray regression models.

Results

The mean age at inclusion of the total study sample was 72.4 years, 45.7% of the participants were male, and median follow-up was 3.6 years. During 1.4 million person-years of follow-up, 13,511 individuals were diagnosed with dementia. The risk for dementia decreased with increasing age for all risk factors and was no longer significant in individuals aged ≥ 90 years. Adjusting for mortality as a competing risk did not change the results.

Conclusions

We conclude that vascular disorders are no longer a risk factor for dementia at high age. Possible explanations include selective survival of individuals who are less susceptible to the negative consequences of vascular disorders and differences in follow-up time between individuals with and without a vascular disorder. Future research should focus on the identification of other risk factors than vascular disorders, for example, genetic or inflammatory processes, that can potentially explain the strong age-related increase in dementia risk.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0496-x) contains supplementary material, which is available to authorized users.

Models for predicting risk of dementia: a systematic review

BACKGROUND

Information from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.

METHODS

We conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.

RESULTS

Of 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer's disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery, Alzheimer's Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.

CONCLUSION

The predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

Global & Community Health: Brief in-hospital cognitive screening anticipates complex admissions and may detect dementia

OBJECTIVE

With the long-term goal of improving community health by screening for dementia, we tested the utility of integrating the Six-Item Screener (SIS) into our emergency department neurology consultations.

METHODS

In this cross-sectional observational study, we measured SIS performance within 24 hours of hospital arrival in 100 consecutive English-speaking patients aged ≥45 years. Performance was compared to patient age, previously charted cognitive impairment, and proxies for in-hospital complexity: whether or not a patient was admitted to the hospital and the number of medical studies ordered.

RESULTS

Those with poor SIS performance were older (p = 0.02) and more likely to have previously charted cognitive impairment (p < 0.01; sensitivity 86%, specificity 77%). Poor performers were more likely to be admitted to the hospital (p = 0.04; odds ratio 3.6) and were subjected to more tests once admitted (p < 0.01), relationships that persisted after accounting for age and history of cognitive impairment.

CONCLUSIONS

Poor performance on the SIS was associated with previously charted cognitive impairment, justifying future study of its ability to detect unrecognized dementia cases. Until then, its ability to inexpensively anticipate medically complex hospital admissions motivates broader emergency department use of the SIS.

Trazodone use and risk of dementia: A population-based cohort study

BACKGROUND

In vitro and animal studies have suggested that trazodone, a licensed antidepressant, may protect against dementia. However, no studies have been conducted to assess the effect of trazodone on dementia in humans. This electronic health records study assessed the association between trazodone use and the risk of developing dementia in clinical practice.

METHODS AND FINDINGS

The Health Improvement Network (THIN), an archive of anonymised medical and prescribing records from primary care practices in the United Kingdom, contains records of over 15 million patients. We assessed patients from THIN aged ≥50 years who received at least two consecutive prescriptions for an antidepressant between January 2000 and January 2017. We compared the risk of dementia among patients who were prescribed trazodone to that of patients with similar baseline characteristics prescribed other antidepressants, using a Cox regression model with 1:5 propensity score matching. Patients prescribed trazodone who met the inclusion criteria (n = 4,716; 59.2% female) were older (mean age 70.9 ± 13.1 versus 65.6 ± 11.4 years) and were more likely than those prescribed other antidepressants (n = 420,280; 59.7% female) to have cerebrovascular disease and use anxiolytic or antipsychotic drugs. After propensity score matching, 4,596 users of trazadone and 22,980 users of other antidepressants were analysed. The median time to dementia diagnosis for people prescribed trazodone was 1.8 years (interquartile range [IQR] = 0.5-5.0 years). Incidence of dementia among patients taking trazodone was higher than in matched users of other antidepressants (1.8 versus 1.1 per 100 person-years), with a hazard ratio (HR) of 1.80 (95% confidence interval [CI] 1.56-2.09; p < 0.001). However, our results do not suggest a causal association. When we restricted the control group to users of mirtazapine only in a sensitivity analysis, the findings were very similar to the results of the main analysis. The main limitation of our study is the possibility of indication bias, because people in the prodromal stage of dementia might be preferentially prescribed trazodone. Due to the observational nature of this study, we cannot rule out residual confounding.

CONCLUSIONS

In this study of UK population-based electronic health records, we found no association between trazodone use and a reduced risk of dementia compared with other antidepressants. These results suggest that the clinical use of trazodone is not associated with a reduced risk of dementia.

Stroke and dementia risk: A systematic review and meta-analysis

INTRODUCTION

Stroke is an established risk factor for all-cause dementia, though meta-analyses are needed to quantify this risk.

METHODS

We searched Medline, PsycINFO, and Embase for studies assessing prevalent or incident stroke versus a no-stroke comparison group and the risk of all-cause dementia. Random effects meta-analysis was used to pool adjusted estimates across studies, and meta-regression was used to investigate potential effect modifiers.

RESULTS

We identified 36 studies of prevalent stroke (1.9 million participants) and 12 studies of incident stroke (1.3 million participants). For prevalent stroke, the pooled hazard ratio for all-cause dementia was 1.69 (95% confidence interval: 1.49-1.92; P < .00001; I2 = 87%). For incident stroke, the pooled risk ratio was 2.18 (95% confidence interval: 1.90-2.50; P < .00001; I2 = 88%). Study characteristics did not modify these associations, with the exception of sex which explained 50.2% of between-study heterogeneity for prevalent stroke.

DISCUSSION

Stroke is a strong, independent, and potentially modifiable risk factor for all-cause dementia.

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients

SUMMIT PARTICIPANTS

ORGANIZATIONS REPRESENTED

Trends in dementia diagnosis rates in UK ethnic groups: analysis of UK primary care data

Objectives

We compared incidence of dementia diagnosis by white, black, and Asian ethnic groups and estimated the proportion of UK white and black people developing dementia in 2015 who had a diagnosis for the first time in a UK-wide study.

Methods

We analyzed primary care electronic health records from The Health Improvement Network database between 2007 and 2015 and compared incidence of dementia diagnosis to dementia incidence from community cohort studies. The study sample comprised of 2,511,681 individuals aged 50–105 years who did not have a dementia diagnosis prior to the start of follow-up.

Results

A total of 66,083 individuals had a dementia diagnosis (4.87/1,000 person-years at risk, 95% CI 4.83–4.90); this incidence increased from 3.75 to 5.65/1,000 person-years at risk between 2007 and 2015. Compared with white women, the incidence of dementia diagnosis was 18% lower among Asian women (adjusted incidence rate ratio (IRR) 0.82, 95% CI 0.72–0.95) and 25% higher among black women (IRR 1.25, 95% CI 1.07–1.46). For men, incidence of dementia diagnosis was 28% higher in the black ethnic group (IRR 1.28, 95% CI 1.08–1.50) and 12% lower in the Asian ethnic group (IRR 0.88, 95% CI 0.76–1.01) relative to the white ethnic group. Based on diagnosis incidence in The Health Improvement Network data and projections of incidence from community cohort studies, we estimated that 42% of black men developing dementia in 2015 were diagnosed compared with 53% of white men.

Conclusion

People from the black ethnic group had a higher incidence of dementia diagnosis and those from the Asian ethnic group had lower incidence compared with the white ethnic group. We estimated that black men developing dementia were less likely than white men to have a diagnosis of dementia, indicating that the increased risk of dementia diagnosis reported in the black ethnic group might underestimate the higher risk of dementia in this group. It is unclear whether the lower incidence of dementia diagnosis in the Asian ethnic group reflects lower community incidence or underdiagnosis. A cohort study to determine this is needed.

Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

Background

Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.

Aim

The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.

Design & setting

The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.

Method

Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.

Results

The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.

Conclusion

The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time.

External validation of four dementia prediction models for use in the general community-dwelling population: a comparative analysis from the Rotterdam Study

To systematically review the literature for dementia prediction models for use in the general population and externally validate their performance in a head-to-head comparison. We selected four prediction models for validation: CAIDE, BDSI, ANU-ADRI and DRS. From the Rotterdam Study, 6667 non-demented individuals aged 55 years and older were assessed between 1997 and 2001. Subsequently, participants were followed for dementia until 1 January, 2015. For each individual, we computed the risk of dementia using the reported scores from each prediction model. We used the C-statistic and calibration plots to assess the performance of each model to predict 10-year risk of all-cause dementia. For comparisons, we also evaluated discriminative accuracy using only the age component of these risk scores for each model separately. During 75,581 person-years of follow-up, 867 participants developed dementia. C-statistics for 10-year dementia risk prediction were 0.55 (95% CI 0.53–0.58) for CAIDE, 0.78 (0.76–0.81) for BDSI, 0.75 (0.74–0.77) for ANU-ADRI, and 0.81 (0.78–0.83) for DRS. Calibration plots showed that predicted risks were too extreme with underestimation at low risk and overestimation at high risk. Importantly, in all models age alone already showed nearly identical discriminative accuracy as the full model (C-statistics: 0.55 (0.53–0.58) for CAIDE, 0.81 (0.78–0.83) for BDSI, 0.77 (0.75–0.79) for ANU-ADRI, and 0.81 (0.78–0.83) for DRS). In this study, we found high variability in discriminative ability for predicting dementia in an elderly, community-dwelling population. All models showed similar discriminative ability when compared to prediction based on age alone. These findings highlight the urgent need for updated or new models to predict dementia risk in the general population.

Predicting dementia from primary care records: A systematic review and meta-analysis

INTRODUCTION

Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Variables in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care.

METHODS AND FINDINGS

We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer's (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results.

CONCLUSIONS

These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should also be explored in future studies.

Proactive approaches to identifying dementia and dementia risk; a qualitative study of public attitudes and preferences

OBJECTIVES

The aim of this study was to critically explore the views of the public about the acceptability and feasibility of proactive approaches to earlier dementia diagnosis and also identification of people at high risk of dementia.

DESIGN

Qualitative study using task group methodology and thematic data analysis.

SETTING

Task groups were held either at the university (n=5) or at a carers' centre (n=1).

PARTICIPANTS

A convenience sample of 31 of 54 participants identified by local non-statutory agencies took part in a task group. All were aged between 40 years and 80 years, 21 were women and 10men participated.

RESULTS

Despite the use of task group methodology, participants expressed limited understandings of dementia and confusion between proactive approaches. Nevertheless, they highlighted a range of potential benefits and limitations of proactive approaches and the ethical issues raised. There was a preference to embed risk assessment within routine health checks, which focused on achieving a healthier lifestyle, rather than specifically on dementia. Participants emphasised the need to ensure informed consent prior to use of proactive approaches and to provide appropriate support. They also suggested alternative approaches that could potentially facilitate the early detection of dementia or reduce risk at a population level.

CONCLUSIONS

As international policy on dementia shifts towards a prevention agenda there is growing interest in identifying those at risk of developing dementia. This study provides useful insights into the acceptability of the use of such proactive approaches among the public. The introduction of proactive approaches to dementia identification raises complex practical and ethical issues, particularly in the context of low public understanding of dementia. The importance of better quality information about dementia (and the likelihood of developing dementia) and provision of psychological support for those undergoing risk assessment were highlighted.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance.

Blood pressure at age 60-65 versus age 70-75 and vascular dementia: a population based observational study

Background

Vascular dementia (VaD) is the second most common form of dementia. However, there were mixed evidences about the association between blood pressure (BP) and risk of VaD in midlife and late life and limited evidence on the association between pulse pressure and VaD.

Methods

This is a population-based observational study. 265,897 individuals with at least one BP measurement between the ages of 60 to 65 years and 211,116 individuals with at least one BP measurement between the ages of 70 to 75 years were extracted from The Health Improvement Network in United Kingdom. Blood pressures were categorized into four groups: normal, prehypertension, stage 1 hypertension, and stage 2 hypertension. Cases of VaD were identified from the recorded clinical diagnoses. Multivariable survival analysis was used to adjust other confounders and competing risk of death. All the analysis were stratified based on antihypertensive drug use status. Multiple imputation was used to fill in missing values.

Results

After accounting for the competing risk of death and adjustment for potential confounders, there was an association between higher BP levels in the age 60–65 cohort with the risk of developing VaD (hazard ratio [HR] 1.53 (95% confidence interval: 1.04, 2.25) for prehypertension, 1.90 (1.30, 2.78) for stage 1 hypertension, and 2.19 (1.48, 3.26) for stage 2 hypertension) in the untreated group. There was no statistically significant association between BP levels and VaD in the treated group in the age 60–65 cohort and age 70–75 cohort. Analysis on Pulse Pressure (PP) stratified by blood pressure level showed that PP was not independently associated with VaD.

Conclusion

High BP between the ages of 60 to 65 years is a significant risk for VaD in late midlife. Greater efforts should be placed on early diagnosis of hypertension and tight control of BP for hypertensive patients for the prevention of VaD.

Electronic supplementary material

The online version of this article (10.1186/s12877-017-0649-3) contains supplementary material, which is available to authorized users.

Dementia Population Risk Tool (DemPoRT): study protocol for a predictive algorithm assessing dementia risk in the community

INTRODUCTION

The burden of disease from dementia is a growing global concern as incidence increases dramatically with age, and average life expectancy has been increasing around the world. Planning for an ageing population requires reliable projections of dementia prevalence; however, existing population projections are simple and have poor predictive accuracy. The Dementia Population Risk Tool (DemPoRT) will predict incidence of dementia in the population setting using multivariable modelling techniques and will be used to project dementia prevalence.

METHODS AND ANALYSIS

The derivation cohort will consist of elderly Ontario respondents of the Canadian Community Health Survey (CCHS) (2001, 2003, 2005 and 2007; 18 764 males and 25 288 females). Prespecified predictors include sociodemographic, general health, behavioural, functional and health condition variables. Incident dementia will be identified through individual linkage of survey respondents to population-level administrative healthcare databases (1797 and 3281 events, and 117 795 and 166 573 person-years of follow-up, for males and females, respectively, until 31 March 2014). Using time of first dementia capture as the primary outcome and death as a competing risk, sex-specific proportional hazards regression models will be estimated. The 2008/2009 CCHS survey will be used for validation (approximately 4600 males and 6300 females). Overall calibration and discrimination will be assessed as well as calibration within predefined subgroups of importance to clinicians and policy makers.

ETHICS AND DISSEMINATION

Research ethics approval has been granted by the Ottawa Health Science Network Research Ethics Board. DemPoRT results will be submitted for publication in peer-review journals and presented at scientific meetings. The algorithm will be assessable online for both population and individual uses.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT03155815, pre-results.

Routine screening in the general hospital: what happens after discharge to those identified as at risk of dementia?

Cognitive screening is recommended for older patients with unplanned hospital admission. We determined rates of reassessment/specialist memory referral after routine inclusion of at risk of dementia status in discharge documentation to primary care. Questionnaires were sent to relevant GP practices on consecutive patients aged ≥75 years identified as at risk and discharged 6 months earlier. Among 53 patients (mean age ±SD = 87.3±6.0 years, mean±SD Abbreviated Mental Test Score = 4.4±2.7), 49 (92%) patients had been reviewed since discharge, and 12/43 (28%) without previously known cognitive problem had had a cognitive reassessment. The most common reasons for non-assessment/referral included clinical factors (eg terminal illness/comorbidities) (n=15) and patient/family wishes (n=5) and that confusion was expected in unwell older patients (n=5). Routine cognitive reassessment/specialist referral appears unjustified in patients identified as at risk of dementia during unplanned hospital admission. However, the prognostic value of delirium/confusion in acute illness is under-recognised and could be used to highlight those at risk.

Longitudinal Study-Based Dementia Prediction for Public Health

The issue of public health in Korea has attracted significant attention given the aging of the country's population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical "big data"-based disease prediction model may assist the diagnosis of any disease more correctly.

The UK experience of promoting dementia recognition and management in primary care

Background

The early and timely recognition of dementia syndrome is a policy imperative in many countries. In the UK the achievement of earlier and timelier recognition has been pursued through educational interventions, incentivisation of general practitioners and the promotion of a network of memory clinics.

Objective

The effectiveness of education, incentivisation and memory clinic activity are unknown. This article analyses data from different sources to evaluate the impact of these interventions on the incidence and prevalence of dementia, and the diagnostic performance of memory clinics.

Material and methods

Three data sources were used: 1) aggregated, anonymised data from a network of general practices using the same electronic medical record software, The Health Information Network (THIN), 2) UK Health & Social Care Information Centre data reports and 3) Responses to Freedom of Information Act requests.

Results

Educational interventions did not appear to change the recorded incidence of dementia syndrome. There was no apparent effect of education, incentives or memory clinic activity on the reported incidence of dementia syndrome between 1997 and 2011 but there were signs of change in the documentation of consultations with people with dementia. There was no clear impact of incentivisation and memory clinic activity in prevalence data. Memory clinics are seeing more patients but fewer are being diagnosed with dementia.

Conclusion

It is not clear why there has been no upturn in documented incidence or prevalence of dementia syndrome despite substantial efforts and this requires further investigation to guide policy changes. The performance of memory clinics also needs further study.

Middle age self-report risk score predicts cognitive functioning and dementia in 20-40 years

INTRODUCTION

On the basis of the proxy measures of cognitive reserve, we created a middle age self-report risk score for early prediction of dementia.

METHODS

We used a longitudinal population-based study of 2602 individuals with a replication sample (N = 1011). Risk score at a mean age of 47 years was based on questions on educational and occupational attainments. Cognitive status at a mean age of 74 was determined via two validated telephone instruments.

RESULTS

The prevalence of dementia was 10% after a mean follow-up of 28 years. Risk score was a good predictor of dementia: area under the curve = 0.77 (95% confidence interval, 0.74-0.80). The risk of dementia decreased as a function of risk score from 36% to 0%. The risk score was significantly associated with cognition after a mean follow-up of 39 years in the replication sample.

DISCUSSION

Self-report risk score predicted cognitive functioning and dementia risk 20-40 years later.