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Clinical trial information and results are updated daily from ClinicalTrials.gov. The latest data update was conducted on 06/15/2021.

Dementia Risk Prediction Model: Development and Validation

Clinicaltrials.gov identifier NCT03943641

Recruitment Status Not yet recruiting

First Posted May 9, 2019

Last update posted May 10, 2019

Study Description

Brief summary:

At present, there is no treatment for dementia that changes the course of the disease. However, it is now understoodd 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, 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).

  • Condition or Disease:Dementia Alzheimers
  • Intervention/Treatment: Other: This is not an intervention study
  • Phase: N/A
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 Design
  • Study Type: Observational
  • Estimated Enrollment: 400000 participants
  • Observational Model: Cohort
  • Time Perspective: Retrospective
  • Official Title: Development and Validation of a Multivariable Dementia Risk Prediction Model in UK Adults Using Routinely Available Predictors
  • Estimated Study Start Date: May 2019
  • Estimated Primary Completion Date: May 2019
  • Actual Study Completion Date: May 2020
Groups and Cohorts
Groups/Cohorts Intervention/treatment
: Population-based
Population-based cohort of participants registered with a SAIL-contributing practice.
Other: This is not an intervention study
This study is based on retrospective analysis of linked routinely-collected healthcare data
Outcome Measures
  • Primary Outcome Measures: 1. Dementia [ Time Frame: 10 years ]
    Development of dementia during follow-up
  • 2. Alzheimer's disease [ Time Frame: 10 years ]
    Development of Alzheimer's disease dementia during follow-up
Eligibility Criteria
  • Ages Eligible for Study: 60 to 79 Years (Adult, Older Adult)
  • Sexes Eligible for Study: All
  • Accepts Healthy Volunteers: Yes
  • Sampling Method: Non-Probability Sample
  • Study Population: We will use data from the Secure Anonymised Information Linkage Databank Dementia electronic Cohort (SAIL-DeC). SAIL-DeC was created as a flexible, national electronic cohort ('e-cohort') to facilitate dementia research using routinely-collected healthcare data. SAIL participants were included in SAIL-DeC based on date of birth (1/1/1900 to 1/4/1958) and if linked primary care data were available (1.2 million individuals).

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)

Contacts and Locations

Contact: Tim Wilkinson 0131 650 3195 tim.wilkinson@ed.ac.uk

Sponsors and Collaborators

University of Edinburgh

Keele University

More Information
  • Responsible Party: University of Edinburgh
  • ClinicalTrials.gov Identifier: NCT03943641 History of Changes
  • Other Study ID Numbers: AC19049
  • First Posted: May 9, 2019 Key Record Dates
  • Last Update Posted: May 10, 2019
  • Last Verified: May 2019
  • Individual Participant
    Data (IPD) Sharing
  • Plan to Share IPD: No
  • Plan Description: Researchers can apply to the SAIL databank (saildatabank.com) to access the individual participant data.
  • Studies a U.S. FDA-regulated Drug Product: No
  • Studies a U.S. FDA-regulated Device Product: No
  • Additional relevant MeSH terms: Dementia Alzheimer Disease