Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05055960 |
| Other study ID # |
0839 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
November 12, 2021 |
| Est. completion date |
September 1, 2024 |
Study information
| Verified date |
May 2023 |
| Source |
University of Leicester |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Stroke is a common condition which results in significant disability for patients. There are
different causes of stroke, but around one quarter are as a result of clots or other material
from the heart lodging in blood vessels in the brain, stopping the blood supply to that area.
Atrial fibrillation is a common cause of blood clots which go to the brain and can be easily
treated with blood thinning medications, which significantly reduce the risk of further
strokes. However, at the moment, atrial fibrillation is difficult to identify, and heart
monitoring can be needed for up to one year. This significantly delays starting blood
thinning medications and leaves patients at risk of stroke during this time. Therefore,
better ways of picking up strokes caused by atrial fibrillation are needed. One such method
may be to use brain scans which are routinely taken at the time a patient presents with an
acute stroke. By using mathematical models to work out the source of stroke, we may be able
to determine which strokes are caused by atrial fibrillation at the time the patient presents
with their stroke. This would reduce the number of investigations patients under-go, saving
money for the NHS, and reducing the number of tests patients have. Therefore, the aim of this
project is to create an anonymised database of brain scans from patients who have presented
to hospital with a stroke to develop and test these recently developed models to see if they
can accurately identify which strokes are caused by atrial fibrillation, and which ones are
not. This project has the potential to improve patient outcomes by reducing treatment delays
and improving the accuracy of the diagnosis of the stroke source.
Description:
Stroke affects more than 100 000 people each year in the UK, and up to 50% of patients are
left with significant disability [1, 2]. 85% of strokes result from occluded blood vessels in
the brain (acute ischaemic stroke [AIS]), interrupting the blood supply and resulting in
tissue infarction and functional impairment [1]. Stroke is responsible for significant
morbidity and mortality, with considerable social and economic implications, particularly for
working age individuals [1, 2]. The cause of AIS can be broadly classified as embolic and
non-embolic stroke [3]. Approximately one quarter of AIS is cardioembolic, due to embolism
from the left atrium (atrial fibrillation - AF), valves (valvular heart disease or prosthetic
valves), recent myocardial infarction, patent foramen ovale, infective endocarditis, or
aortic arch atheroma [3-5]. This figure is higher amongst older adults due to rising
prevalence of AF with age [6]. Importantly, cardioembolic strokes are more severe, resulting
in greater disability than other stroke sub-types [4]. Due to improving standards in vascular
risk factor management, the proportion of strokes resulting from cardioembolism is rising,
having tripled in incidence over the last few decades [4]. Correct identification and
management of an embolic source can reduce recurrence by up to 70% [4]. AF remains the
leading cause of cardioembolic stroke [4, 5]. However, correctly identifying and treating AF
is thought to occur in only 50% of eligible patients [3]. This may be due to clinician
reluctance to anticoagulate patients, particularly in the frail older population at high risk
of falls [3]. Up to 25% of stroke is embolic of undetermined source (ESUS), which may be due
to paroxysmal AF requiring prolonged cardiac monitoring to successfully identify and treat
patients [3, 7]. AF is asymptomatic in up to 40% of patients, and the first presentation may
be with a significant infarction [8]. Anticoagulation is the cornerstone of reducing stroke
risk in AF, but with up to a two-fold increase in bleeding risk this treatment is reserved
only for proven cases [9, 10]. Anticoagulation in ESUS remains an ongoing debate, and is
currently limited to high risk groups (e.g. multiple infarcts in different territories) [11,
12]. Thus, identifying and treating AF is a key priority to reduce future stroke risk, but
minimise complications where anticoagulation is not indicated.
Given the differing aetiologies, investigation, and management of embolic and non-embolic
stroke, early differentiation facilitates clinical decision making, and allows focussed
investigation and management for patients. Radiological features of stroke can support this
process as the location, size, and pattern of the infarction, can indicate the likely
aetiology and stroke sub-type. However, at present there are no criteria by which this is
assessed, relying on a qualitative and thus subjective, opinion of the treating clinician or
radiologist. Inaccurate diagnosis has been reported in up to one third of lacunar strokes
relying on clinical and CT findings alone [13]. The majority of false positive findings were
due to cardioembolic or large artery stroke, potentially resulting in missed opportunity to
identify and treat AF [13]. Diffusion weighted imaging can improve the diagnosis of lacunar
infarction but its use is limited by cost and availability [13-15]. In a recent study of 133
patients with ESUS, 22.6% were found to have AF after three months of cardiac monitoring, and
an 8-point risk score could predict this with reasonable accuracy (area under the curve=0.70)
[16]. However this accuracy falls considerably where the aetiology of stroke remains
undetermined [16]. The true proportion of AF that goes undetected remains unknown, and
earlier detection through predictive modelling could potentially reduce investigation and
treatment delays.
Therefore, improving the processes by which embolic stroke, particularly AF, may help guide
clinical decision making. This would allow better tailoring of investigations to patients,
removing unnecessary tests that could have potential economic benefits to the NHS, and
reducing investigation burden to patients. The development of stroke models that can
integrate clinical information from patients, and scan findings, may be able to provide
improved diagnostic accuracy for stroke-sub type classification and facilitate timely, and
more targeted investigation and treatment. In particular, models capable of differentiating
between different types of embolism may be particularly valuable and prompt extended
monitoring for patients who are most likely to have an AF source.
This study proposes a Monte Carlo method developed by JH and EC to simulate strokes [17-20].
This method has recently been adapted using an in silico cerebral vasculature [21] and tested
against stroke images from the Anatomical Tracings of Lesions After Stroke (ATLAS) database
(unpublished work). The advantage of using a computationally generated vasculature over
imaging is that there is no lower bound on vessel size and we are able to include vessels in
our model down to the capillary bed. Within the simulations, we can estimate lesion volume as
well as incorporate the differing metabolic demands of grey and white matter. Recently, we
have been able to reproduce stroke images in the anterior, middle, and posterior (ACA, MCA,
PCA respectively) circulations using images from the ATLAS dataset. However, the majority of
lesions in ATLAS were chronic in nature and this does not provide information on how this
model would perform at the "front door" where treatment decisions could be made in a more
timely fashion.
Therefore, this project seeks to develop an acute stroke scan database to validate these
recently developed simulations against and to determine the accuracy at predicting the source
of embolic stroke in the acute setting. Given the growing application of AI techniques to
clinical medicine, we will compare the ability of this recently developed stroke simulation
model to AI techniques to predict the source of embolic stroke. We will compare the ability
of the models to clinical or radiological opinion. Finally, we will test the combined ability
of the two techniques to determine the source of embolic stroke. Specifically, we will
evaluate the following sources: lacunar, cardioembolic, large vessel atherosclerotic emboli,
and watershed.
