Big data and data repurposing - using existing data to answer new questions in vascular dementia research

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

The benefits of data sharing and ensuring open sources of systematic review data

AIMS

The benefits of increasing public access to data from clinical trials are widely accepted. Such benefits extend to the sharing of data from high-quality systematic reviews, given the time and cost involved with undertaking reviews. We describe the application of open sources of review data, outline potential challenges and highlight efforts made to address these challenges, with the intent of encouraging publishers, funders and authors to consider sharing review data more broadly.

RESULTS

We describe the application of systematic review data in: (i) advancing understanding of clinical trials and systematic review methods, (ii) repurposing of data to answer public health policy and practice relevant questions, (iii) identification of research gaps and (iv) accelerating the conduct of rapid reviews to inform decision making. While access, logistical, motivational and legal challenges exist, there has been progress made by systematic review, academic and funding agencies to incentivise data sharing and create infrastructure to support greater access to systematic review data.

CONCLUSION

There is opportunity to maximize the benefits of research investment in undertaking systematic reviews by ensuring open sources of systematic review data. Efforts to create such systems should draw on learnings and principles outlined for sharing clinical trial data.

Computational Drug Repurposing: Classification of the Research Opportunities and Challenges

BACKGROUND

Drug repurposing has grown significantly in recent years. Research and innovation in drug repurposing are extremely popular due to its practical and explicit advantages. However, its adoption into practice is slow because researchers and industries have to face various challenges.

OBJECTIVE

As this field, there is a lack of a comprehensive platform for systematic identification for removing development limitations. This paper deals with a comprehensive classification of challenges in drug repurposing.

METHODS

Initially, a classification of various existing repurposing models is propounded. Next, the benefits of drug repurposing are summarized. Further, a categorization for computational drug repurposing shortcomings is presented. Finally, the methods are evaluated based on their strength to addressing the drawbacks.

RESULTS

This work can offer a desirable platform for comparing the computational repurposing methods by measuring the methods in light of these challenges.

CONCLUSION

A proper comparison could prepare guidance for a genuine understanding of methods. Accordingly, this comprehension of the methods will help researchers eliminate the barriers thereby developing and improving methods. Furthermore, in this study, we conclude why despite all the benefits of drug repurposing, it is not being done anymore.

Motor signs in Alzheimer's disease and vascular dementia: Detection through natural language processing, co-morbid features and relationship to adverse outcomes

BACKGROUND

Motor signs in patients with dementia are associated with a higher risk of cognitive decline, institutionalisation, death and increased health care costs, but prevalences differ between studies. The aims of this study were to employ a natural language processing pipeline to detect motor signs in a patient cohort in routine care; to explore which other difficulties occur co-morbid to motor signs; and whether these, as a group and individually, predict adverse outcomes.

METHODS

A cohort of 11,106 patients with dementia in Alzheimer's disease, vascular dementia or a combination was assembled from a large dementia care health records database in Southeast London. A natural language processing algorithm was devised in order to establish the presence of motor signs (bradykinesia, Parkinsonian gait, rigidity, tremor) recorded around the time of dementia diagnosis. We examined the co-morbidity profile of patients with these symptoms and used Cox regression models to analyse associations with survival and hospitalisation, adjusting for twenty-four potential confounders.

RESULTS

Less than 10% of patients were recorded to display any motor sign, and tremor was most frequently detected. Presence of motor signs was associated with younger age at diagnosis, neuropsychiatric symptoms, poor physical health and higher prescribing of psychotropics. Rigidity was independently associated with a 23% increased mortality risk after adjustment for confounders (p = 0.014). A non-significant trend for a 15% higher risk of hospitalisation was detected in those with a recorded Parkinsonian gait (p = 0.094).

CONCLUSIONS

With the exception of tremor, motor signs appear to be under-recorded in routine care. They are part of a complex clinical picture and often accompanied by neuropsychiatric and functional difficulties, and thereby associated with adverse outcomes. This underlines the need to establish structured examinations in routine clinical practice via easy-to-use tools.

Protocol for the development of a repository of individual participant data from randomised controlled trials conducted in adult care homes (the Virtual International Care Homes Trials Archive (VICHTA))

Background

Approximately 418,000 people live in care homes in the UK, yet accessible, robust data on care home populations and organisation are lacking. This hampers our ability to plan, allocate resources or prevent risk. Large randomised controlled trials (RCTs) conducted in care homes offer a potential solution. The value of detailed data on residents’ demographics, outcomes and contextual information captured in RCTs has yet to be fully realised. Irrespective of the intervention tested, much of the trial data collected overlaps in terms of structured assessments and descriptive information. Given the time and costs required to prospectively collect data in these populations, pooling anonymised RCT data into a structured repository offers benefit; secondary analyses of pooled RCT data can improve understanding of this under-researched population and enhance the future trial design. This protocol describes the creation of a project-specific repository of individual participant data (IPD) from trials conducted in care homes and subsequent expansion into a legacy dataset for wider use, to address the need for accurate, high-quality IPD on this vulnerable population.

Methods

Informed by scoping of relevant literature, the principal investigators of RCTs conducted in adult care homes in the UK since 2010 will be invited to contribute trial IPD. Contributing trialists will form a Steering Committee who will oversee data sharing and remain gatekeepers of their own trial’s data. IPD will be cleaned and standardised in consultation with the Steering Committee for accuracy. Planned analyses include a comparison of pooled IPD with point estimates from administrative sources, to assess generalisability of RCT data to the wider care home population. We will also identify key resident characteristics and outcomes from within the trial repository, which will inform the development of a national minimum dataset for care homes. Following project completion, management will migrate to the Virtual Trials Archives, forming a legacy dataset which will be expanded to include international RCTs, and will be accessible to the wider research community for analyses.

Discussion

Analysis of pooled IPD has the potential to inform and direct future practice, research and policy at low cost, enhancing the value of existing data and reducing research waste. We aim to create a permanent archive for care home trial data and welcome the contribution of emerging trial datasets.

Pattern Recognition to Objectively Differentiate the Etiology of Cognitive Decline: Analysis of the Impact of Stroke and Alzheimer's Disease

BACKGROUND

Undetected Alzheimer's disease (AD) and stroke neuropathology is believed to account for a large proportion of decline in cognitive performance that is attributed to normal aging. This study examined the amount of variance in age-related cognitive change that is accounted for by AD and stroke using a novel pattern recognition protocol.

METHOD

Secondary analyses of data collected for the Health and Retirement Study (N = 17,579) were used to objectively characterize patterns of cognitive decline associated with AD and stroke. The rate of decline in episodic memory and orientation was the outcome of interest, while algorithms indicative of AD and stroke pathology were the predictors of interest.

RESULTS

The average age of the sample was 67.54 ± 10.45 years at baseline, and they completed, on average, 14.20 ± 3.56 years of follow-up. After adjusting for demographics, AD and stroke accounted for approximately half of age-associated decline in cognition (51.07-55.6% for orientation and episodic memory, respectively) and explained variance attributed to random slopes in longitudinal multilevel models.

DISCUSSION

The results of this study suggested that approximately half of the cognitive decline usually attributed to normal aging are more characteristic of AD and stroke.

UK consensus on pre-clinical vascular cognitive impairment functional outcomes assessment: Questionnaire and workshop proceedings

Assessment of outcome in preclinical studies of vascular cognitive impairment (VCI) is heterogenous. Through an ARUK Scottish Network supported questionnaire and workshop (mostly UK-based researchers), we aimed to determine underlying variability and what could be implemented to overcome identified challenges. Twelve UK VCI research centres were identified and invited to complete a questionnaire and attend a one-day workshop. Questionnaire responses demonstrated agreement that outcome assessments in VCI preclinical research vary by group and even those common across groups, may be performed differently. From the workshop, six themes were discussed: issues with preclinical models, reasons for choosing functional assessments, issues in interpretation of functional assessments, describing and reporting functional outcome assessments, sharing resources and expertise, and standardization of outcomes. Eight consensus points emerged demonstrating broadly that the chosen assessment should reflect the deficit being measured, and therefore that one assessment does not suit all models; guidance/standardisation on recording VCI outcome reporting is needed and that uniformity would be aided by a platform to share expertise, material, protocols and procedures thus reducing heterogeneity and so increasing potential for collaboration, comparison and replication. As a result of the workshop, UK wide consensus statements were agreed and future priorities for preclinical research identified.

Validity of a computerised population registry of dementia based on clinical databases

INTRODUCTION

The handling of information through digital media allows innovative approaches for identifying cases of dementia through computerised searches within the clinical databases that include systems for coding diagnoses. The aim of this study was to analyse the validity of a dementia registry in Gipuzkoa based on the administrative and clinical databases existing in the Basque Health Service.

METHODS

This is a descriptive study based on the evaluation of available data sources. First, through review of medical records, the diagnostic validity was evaluated in two samples of cases identified and not identified as dementia. The sensitivity, specificity and positive and negative predictive value of the diagnosis of dementia were measured. Subsequently, the cases of living dementia in December 31, 2016 were searched in the entire Gipuzkoa population to collect sociodemographic and clinical variables.

RESULTS

The validation samples included 986 cases and 327 no cases. The calculated sensitivity was 80.2% and the specificity was 99.9%. The negative predictive value was 99.4% and positive value was 95.1%. The cases in Gipuzkoa were 10 551, representing 65% of the cases predicted according to the literature. Antipsychotic medication were taken by a 40% and a 25% of the cases were institutionalised.

CONCLUSIONS

A registry of dementias based on clinical and administrative databases is valid and feasible. Its main contribution is to show the dimension of dementia in the health system.

Home-Time Is a Feasible and Valid Stroke Outcome Measure in National Datasets

Background and Purpose- Home-time (HT) is a stroke outcome measure based on time spent at home after stroke. We hypothesized that HT assessment would be feasible and valid using national data. Methods- We linked the Scottish Stroke Care Audit to routine healthcare data and calculated 90-day HT for all strokes, 2005 to 2017. We described prognostic validity (Spearman rank correlation) of HT to baseline factors. Results- We were able to calculate HT for 101 969 strokes (99.3% of total Scottish strokes). Mean HT was 46 days (95% CI, 45.8-46.2; range, 0-90). HT showed consistent correlation with our prespecified prognostic factors: age: ρ, -0.35 (95% CI, -0.35 to -0.36); National Institutes of Health Stroke Scale score, -0.54 (95% CI, -0.52 to -0.55); and 6 simple variables (ordinal), -0.61 (95% CI, -0.61 to -0.62). Conclusions- HT can be derived at scale using routine clinical data and appears to be a valid proxy measure of functional recovery. Other national databases could use HT as a time and cost efficient measure of medium and longer-term outcomes.

Aluminium and fluoride in drinking water in relation to later dementia risk

BACKGROUND

Environmental risk factors for dementia are poorly understood. Aluminium and fluorine in drinking water have been linked with dementia but uncertainties remain about this relationship.

AIMS

In the largest longitudinal study in this context, we set out to explore the individual effect of aluminium and fluoride in drinking water on dementia risk and, as fluorine can increase absorption of aluminium, we also examine any synergistic influence on dementia.

METHOD

We used Cox models to investigate the association between mean aluminium and fluoride levels in drinking water at their residential location (collected 2005-2012 by the Drinking Water Quality Regulator for Scotland) with dementia in members of the Scottish Mental Survey 1932 cohort who were alive in 2005.

RESULTS

A total of 1972 out of 6990 individuals developed dementia by the linkage date in 2012. Dementia risk was raised with increasing mean aluminium levels in women (hazard ratio per s.d. increase 1.09, 95% CI 1.03-1.15, P < 0.001) and men (1.12, 95% CI 1.03-1.21, P = 0.004). A dose-response pattern of association was observed between mean fluoride levels and dementia in women (1.34, 95% CI 1.28-1.41, P < 0.001) and men (1.30, 95% CI 1.22-1.39, P < 0.001), with dementia risk more than doubled in the highest quartile compared with the lowest. There was no statistical interaction between aluminium and fluoride levels in relation with dementia.

CONCLUSIONS

Higher levels of aluminium and fluoride were related to dementia risk in a population of men and women who consumed relatively low drinking-water levels of both.

Implementing the National Heart, Lung, and Blood Institute's Strategic Vision in the Division of Cardiovascular Sciences

As we commemorate the 70^th Anniversary of the National Heart, Lung, and Blood Institute (NHLBI) and celebrate important milestones that have been achieved by the Division of Cardiovascular Sciences (DCVS), it is imperative that DCVS and the Extramural Research community at-large continue to address critical public health challenges that persist within the area of Cardiovascular Diseases (CVD). The NHLBI's Strategic Vision, developed with extensive input from the extramural research community and published in 2016, included overarching goals and strategic objectives that serve to provide a general blueprint for sustaining the legacy of the Institute by leveraging opportunities in emerging scientific areas (e.g., regenerative medicine, omics technology, data science, precision medicine, and mobile health), finding new ways to address enduring challenges (e.g., social determinants of health, health inequities, prevention, and health promotion), and training the next generation of heart, lung, blood, and sleep researchers. DCVS has developed a strategic vision implementation plan to provide a cardiovascular framing for the pursuit of the Institute's overarching goals and strategic objectives garnered from the input of the broader NHLBI community. This plan highlights six scientific focus areas that demonstrate a cross-cutting and multifaceted approach to addressing cardiovascular sciences, including 1) addressing social determinants of cardiovascular health (CVH) and health inequities, 2) enhancing resilience, 3) promoting CVH and preventing CVD Across the lifespan, 4) eliminating hypertension-related CVD, 5) reducing the burden of heart failure, and 6) preventing vascular dementia. These priorities will guide our efforts in Institute-driven activities in the coming years but will not exclude development of other novel ideas or the support of investigator-initiated grant awards. The DCVS Strategic Vision implementation plan is a living document that will evolve with iterative dialogue with the NHLBI community and adapt as the dynamic scientific landscape changes to seize emerging opportunities.

Effect of low-dose Levamlodipine Besylate in the treatment of vascular dementia

Vascular dementia (VaD) is a complex disorder caused by reduced blood flow in the brain. However, there is no effective pharmacological treatment option available until now. Here, we reported that low-dose levamlodipine besylate could reverse the cognitive impairment in VaD mice model of right unilateral common carotid arteries occlusion (rUCCAO). Oral administration of levamlodipine besylate (0.1 mg/kg) could reduce the latency to find the hidden platform in the MWM test as compared to the vehicle group. Furthermore, vehicle-treated mice revealed reduced phospho-CaMKII (Thr286) levels in the hippocampus, which can be partially restored by levamlodipine besylate (0.1 mg/kg and 0.5 mg/kg) treatment. No significant outcome on microglia and astrocytes were observed following levamlodipine besylate treatment. This data reveal novel findings of the therapeutic potential of low-dose levamlodipine besylate that could considerably enhance the cognitive function in VaD mice.

Pattern Recognition to Identify Stroke in the Cognitive Profile: Secondary Analyses of a Prospective Cohort Study

Background

Stroke can produce subtle changes in the brain that may produce symptoms that are too small to lead to a diagnosis. Noting that a lack of diagnosis may bias research estimates, the current study sought to examine the utility of pattern recognition relying on serial assessments of cognition to objectively identify stroke-like patterns of cognitive decline (pattern-detected stroke, p-stroke).

Methods

Secondary data analysis was conducted using participants with no reported history of stroke in the Health and Retirement Study, a large (n = 16,113) epidemiological study of cognitive aging among respondents aged 50 years and older that measured episodic memory consistently biennially between 1996 and 2014. Analyses were limited to participants with at least 4 serial measures of episodic memory. Occurrence and date of p-stroke events were identified utilizing pattern recognition to identify stepwise declines in cognition consistent with stroke. Descriptive statistics included the percentage of the population with p-stroke, the mean change in episodic memory resulting in stroke-positive testing, and the mean time between p-stroke and first major diagnosed stroke. Statistical analyses comparing cases of p-stroke with reported major stroke relied on the area under the receiver-operating curve (AUC). Longitudinal modeling was utilized to examine rates of change in those with/without major stroke after adjusting for demographics.

Results

The pattern recognition protocol identified 7,499 p-strokes that went unreported. On average, individuals with p-stroke declined in episodic memory by 1.986 (SD = 0.023) words at the inferred time of stroke. The resulting pattern recognition protocol was able to identify self-­reported major stroke (AUC = 0.58, 95% CI = 0.57-0.59, p < 0.001). In those with a reported major stroke, p-stroke events were detectable on average 4.963 (4.650–5.275) years (p < 0.001) before diagnosis was first reported. The incidence of p-stroke was 40.23/1,000 (95% CI = 39.40–41.08) person-years. After adjusting for sex, age was associated with the incidence of p-stroke and major stroke at similar rates.

Conclusions

This is the first study to propose utilizing pattern recognition to identify the incidence and timing of p-stroke. Further work is warranted examining the clinical utility of pattern recognition in identifying p-stroke in longitudinal cognitive profiles.

Statin Use is Not Associated with Future Long-Term Care Admission: Extended Follow-Up of Two Randomised Controlled Trials

BACKGROUND

Statins have been associated with later life, long-term care admission in observational studies. However, by preventing vascular events, statins may also prevent or delay admission. We wished to determine statin and long-term care admission associations in a randomised controlled trial context, and describe associations between long-term care admission and other clinical and demographic factors.

METHODS

We used extended follow-up of two randomised trial populations, using national data to assign the long-term care admission outcome, and included individuals screened or recruited to two large randomised trials of pravastatin 40 mg daily-the West of Scotland Coronary Prevention Study (WOSCOPS) and the pravastatin in elderly individuals at risk of vascular disease (PROSPER) study. We described univariable and multivariable analyses of potential predictors of long-term care admission with corresponding survival curves of incident long-term care admission and analyses adjusted for competing risk.

RESULTS

In total 11,015 (10%) of the trial participants were admitted to long-term care. There was no difference between participants in the statin or placebo arms of either trial in regard to admissions to long-term care. On multivariable analyses, independent associations with incident long-term care admission in the PROSPER trial were age (hazard ratio [HR] 1.06 per year, 95% confidence interval [CI] 1.03-1.09) and male sex (HR 0.72, 95% CI 0.53-0.99). In the WOSCOPS, age (HR 1.12 per year, 95% CI 1.10-1.13) and increasing social deprivation (HR 1.05, 95% CI 1.03-1.08) were associated with incident long-term care admission.

CONCLUSION

We did not demonstrate an association between historical statin use and future long-term care admission. The strongest associations with incident long-term care admission were non-modifiable factors of age, sex and socioeconomic deprivation.

Validity of a computerized population registry of dementia based on clinical databases

INTRODUCTION

The handling of information through digital media allows innovative approaches for identifying cases of dementia through computerized searches within the clinical databases that include systems for coding diagnoses. The aim of this study was to analyze the validity of a dementia registry in Gipuzkoa based on the administrative and clinical databases existing in the Basque Health Service.

METHODS

This is a descriptive study based on the evaluation of available data sources. First, through review of medical records, the diagnostic validity was evaluated in 2 samples of cases identified and not identified as dementia. The sensitivity, specificity and positive and negative predictive value of the diagnosis of dementia were measured. Subsequently, the cases of living dementia in December 31, 2016 were searched in the entire Gipuzkoa population to collect sociodemographic and clinical variables.

RESULTS

The validation samples included 986 cases and 327 no cases. The calculated sensitivity was 80.2% and the specificity was 99.9%. The negative predictive value was 99.4% and positive value was 95.1%. The cases in Gipuzkoa were 10,551, representing 65% of the cases predicted according to the literature. Antipsychotic medication were taken by a 40% and a 25% of the cases were institutionalized.

CONCLUSIONS

A registry of dementias based on clinical and administrative databases is valid and feasible. Its main contribution is to show the dimension of dementia in the health system.

Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.