Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT03943641 |
| Other study ID # |
AC19049 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
May 8, 2019 |
| Est. completion date |
August 1, 2024 |
Study information
| Verified date |
December 2023 |
| Source |
University of Edinburgh |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
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).
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.
