- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03943641
Dementia Risk Prediction Model: Development and Validation
Development and Validation of a Multivariable Dementia Risk Prediction Model in UK Adults Using Routinely Available Predictors
At present, there is no treatment for dementia that changes the course of the disease. However, it is now understood that the proteins in dementias such as Alzheimer's disease are present years before someone develops symptoms of dementia. Studies may therefore need to give potential treatments to patients before they develop symptoms of dementia. To do this, researchers need a way of predicting who will go on to develop dementia in the future.
There are several ways of doing this, however, many of these methods are costly and difficult to implement at a population level - such as brain imaging, lumbar punctures or psychological tests. In this study, the investigators aim to develop a method of predicting who will go on to develop dementia (and dementia due to Alzheimer's disease) using only the sort of information that a general practitioner would have available to them.
To do this, the investigators will develop a dementia prediction model using data from the Secure Anonymised Information Linkage (SAIL) Databank, which contains anonymised primary care, hospital admissions and mortality data for the population of Wales, United Kingdom (UK). They will then go on to test how well it performs in an external dataset, such as the UK's Clinical Practice Research Datalink (CPRD).
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
To date, no dementia drugs have shown a disease-modifying effect in clinical trials. It is now understood that the pathology underlying Alzheimer's disease is present decades before symptoms become apparent. Starting an intervention only when a patient develops cognitive symptoms, and therefore when there is substantial disease burden, may reduce the chance of any disease-modifying effect. Instead, targeting interventions earlier, when the pathological burden is lower, may increase the likelihood of preventing or delaying dementia onset.
Consequently, there is a need for a method that identifies patients who are at an increased risk of developing dementia. This requires the development of a risk prediction model, which utilises multiple predictors in combination to produce individualised estimates of the risk of developing dementia risk over time.
An ideal risk prediction model for a population-based application would need to use predictors that are already available to, or readily obtainable by, general practitioners (GPs). Such a predictive tool could be used as a low cost, scalable method of recruiting an 'at risk' group of participants to future trials of risk modification strategies or preventative therapies. Once an effective disease-modifying intervention is identified, clinicians could use the same model to identify at-risk patients who may benefit most from undergoing the intervention.
An ideal dementia risk prediction tool would contain only information that is readily available to, or easily obtainable by, clinicians such as General Practitioners (GPs).
The investigators aim to develop two 10-year risk prediction models: one to predict all-cause dementia and one to predict Alzheimer's disease dementia, in UK adults aged 60-79 years, using only predictors that are routinely available to GPs. They will develop the model using data from the Secure Anonymised Information Linkage (SAIL) Databank, which is composed of anonymised, linked primary care, hospital admissions and mortality data for the population of Wales, UK.
The investigators will then go on to externally validate their dementia risk prediction models in an external dataset, such as the UK's Clinical Practice Research Datalink (CPRD). They will also validate an existing, published study using data from the The Health Improvement Network (THIN) (Walters et al. 2016) using this external dataset, allowing us to compare the performance of the models.
Study Type
Enrollment (Anticipated)
Contacts and Locations
Study Locations
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Midlothian
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Edinburgh, Midlothian, United Kingdom
- Usher Institute, University of Edinburgh
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Registered with a SAIL practice before 2008
- Aged between 60-79 during the study window (1st January 2008 to 31st December 2017)
- Aged 60-79 years by January 2008
Exclusion Criteria:
- Deprivation quintile missing for the start of follow-up (deprivation scores will probably not be missing at random)
- All-cause dementia code in any dataset prior to 1st January 2008 (i.e. dementia diagnosis at baseline)
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Population-based
Population-based cohort of participants registered with a SAIL-contributing practice.
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This study is based on retrospective analysis of linked routinely-collected healthcare data
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Dementia
Time Frame: 10 years
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Development of dementia during follow-up
|
10 years
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Alzheimer's disease
Time Frame: 10 years
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Development of Alzheimer's disease dementia during follow-up
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10 years
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Collaborators and Investigators
Sponsor
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AC19049
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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