Welcome to the WSC 2022 Interactive Program
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*Please note that all sessions in halls Summit 1, Summit 2 & Hall 406 will be live streamed in addition to the onsite presentation
ASK THE SPEAKER
Sessions in Halls 406, Summit 1 and Summit 2 have a Q&A component, through the congress App called “Ask the Speaker”
LEFT ATRIAL FUNCTIONAL REMODELING AND LONG-TERM RISK OF ISCHEMIC STROKE IN POST-STEMI PATIENTS
Abstract
Background and Aims
Patients with ST-segment elevation myocardial infarction (STEMI) have increased risk of ischemic stroke. In this study, we investigated the association between left atrial (LA) functional remodeling pattern and long-term risk of ischemic stroke in post-STEMI patients.
Methods
Speckle-tracking echocardiography derived late systolic strain rate (SRa) was used to evaluate LA function, while LA volume index (LAVi) was used to estimate LA dilatation. Four distinct LA functional remodeling groups were defined: 1) no LA dysfunction and dilatation, 2) LA dysfunctional but no LA dilatation, 3) no LA dysfunction but LA dilated and 4) LA dysfunctional and dilated.
Results
A total of 524 patients were selected (mean age 60±14, male gender 84.7%). During a median follow-up of 27 months (IQR 13; 41), ischemic stroke occurred for 6 patients (1.1%). The SRa 0.5 (s-1) and LAVi 35 ml/m2, represents the values where the hazard ratio (HR) is greater than unity, were chosen as an optimal threshold to define LA functional remodeling groups (Figure 1A and 1B). Analysis of interaction between SRa and LAVi showed that the risk of ischemic stroke was increasing when LA dilatation was accompanied by LA dysfunction (Figure 1C). Those with LA dysfunction accompanied LA dilatation had a significantly lower event-free survival rate (Log-rank p=0.0024) (Figure 1D). The LA functional remodeling group 4 was significantly associated with increased risk of ischemic stroke (HR=13.6, 95% CI 2.26-82.1, p=0.004).
Conclusions
The LA dysfunction accompanied with LA dilatation is significantly associated with increased risk of ischemic stroke in post-STEMI patients.
RIGHT-TO-LEFT SHUNTS AND WHITE MATTER LESIONS IN PATIENTS WITH MIGRAINE: A SYSTEMATIC REVIEW AND META-ANALYSIS
Abstract
Background and Aims
White matter hyperintensities (WMHs) have been observed with greater frequency in patients with migraine and are thought to be associated with impaired cognition and function. The relationship between WMHs and right-to-left shunt (RLS) in migraine patients is unknown. We performed a systematic review to determine if there is an association between RLS and WMHs in patients with migraine.
Methods
A systematic search of the literature was performed in PubMed and Embase using a suitable keyword search strategy from inception up to 16th June 2021. All studies that included patients with migraine and studied RLS and WMHs were included.
Results
A total of 8 non-randomized observational studies comprising 1125 patients with migraine were included; 576 had an RLS, compared to 549 patients with no RLS. The mean age of the study populations ranged from 28.4–43 years, while the average duration from migraine diagnosis ranged from 5.1–19 years. The proportion of female to male patients was consistently higher in all studies (60.0%–94.4%). Amongst migraine patients with RLS, 338 patients (58.7%) had WMHs. In contrast, 256 (46.6%) of migraine patients without RLS had WMHs. RLS was significantly associated with the presence of WMHs in migraine patients (OR: 1.56, 95% CI: 1.05-2.34, p = 0.03).
Conclusions
In migraine patients, RLS was significantly associated with the presence of WMHs. Longitudinal studies are warranted to establish RLS as a risk factor for WMHs in patients with migraine, and to establish the significance of these changes.
POTENTIAL ROLE OF BOLUS-TRACKING DATA OF CAROTID CT ANGIOGRAPHY FOR ATRIAL FIBRILLATION SCREENING
Abstract
Background and Aims
Atrial fibrillation (AF), a significant cause of ischemic stroke, often goes undetected because of its asymptomatic nature. This study investigated whether the total bolus-tracking time (TTT) and average slope (AS) of a bolus-tracking graph could be used to screen patients with AF.
Methods
This single-center retrospective study included patients who underwent carotid computed tomography angiography (CTA) and a 24-h Holter test. TTT and the average degree of enhancement during bolus-tracking derived from carotid CTA were defined as variables of interest. All patients underwent transthoracic echocardiography. Left ventricular diastolic dysfunction and elevated left atrial pressure (LAP) were identified according to the guidelines of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging.
Results
The final cohort comprised 716 patients, 80 of whom presented with AF. TTT of the AF group was significantly longer (23.8 ± 5.2 s) than that of the non-AF group (18.7 ± 2.8 s); P <0.001. The AS of the bolus-tracking graph of the AF group was 0.80 ± 0.24, which was significantly lower than that of the non-AF group 1.38 ± 0.21 (P <0.001). TTT was associated with a significantly higher risk of AF (odds ratio [OR]: 1.36; P <0.001) and elevated LAP (OR: 1.46; P <0.001). In contrast, the AS of the bolus-tracking graph was not significantly associated with either AF or an elevated LAP.
Conclusions
TTT derived from bolus-tracking carotid CTA is an effective adjuvant tool for screening AF related to left ventricular diastolic dysfunction and elevated LAP confirmed using echocardiography.
RISK OF CORONARY HEART DISEASE FOLLOWING TRANSIENT ISCHEMIC ATTACKS IN THE COMMUNITY-THE FRAMINGHAM STUDY
Abstract
Background and Aims
The association between Transient ischemic attack (TIA) and stroke is well-established; the risk of non-cerebral cardiovascular events is less well described. We report the risk ,timing and time-trends for coronary heart disease (CHD) post-TIA in the community-based Framingham Study (FHS) cohort.
Methods
Longitudinal follow-up (1948-2017) of all FHS participants with TIA, free of CHD at age 35-94. Each TIA case was randomly assigned 3 age- and gender- matched CHD-free controls. Cox models were used to examine the risk of subsequent CHD, defined as coronary death or myocardial infarction. Risk was assessed in 3 epochs: 1954-1985, 1986-1999, 2000-2017.
Results
286 participants (170 women, 73.1±10.8 years) with a first-ever TIA were identified; over 9.5±8.1 [VL1] [HJJ2] years of follow-up 68 (23.8%) had CHD. The majority (85%) occurred beyond 12 months from TIA. Compared to controls, persons with TIA had a 54% higher 10-year risk of CHD, adjusting for age, sex, education level hypertension, diabetes, atrial fibrillation, and current smoking (Hazard Ratio 1.54, 95% CI 1.05-2.27, p<0.03). The risk of post-TIA CHD decreased significantly over time, from 37% between 1954-1986 to 12% in the epoch of 2000-2017; CHD risk in controls in the last epoch was 6%.
Conclusions
Compared to the general population, TIA patients are at significantly higher risk of subsequent CHD. Our findings suggest that TIA should not be viewed as a strictly cerebrovascular problem, but as herald of subsequent overall cardiovascular risk beyond stroke and highlight the need for both early intervention but also continued surveillance in this high-risk patient population.
ISCHAEMIC STROKE AND CARDIOEMBOLISM: DIAGNOSTIC UTILITY OF A PREDICTIVE DEEP LEARNING MODEL
Abstract
Background and Aims
Cardioembolism, including atrial fibrillation (AF), is a common cause of stroke. AF is often occult and can go undiagnosed despite extensive investigation. There is evidence that cardioembolism is associated with specific topographical patterns on MRI. Human observation however is inadequate to diagnose cardioembolic stroke based on neuroimaging alone. We aimed to develop a predictive deep learning model that can accurately differentiate stroke infarct patterns on MRI, due to a cardioembolic or non-cardioembolic source.
Methods
A random sample of 605 patients with acute ischaemic stroke was analysed from the Royal Melbourne Hospital database. After detailed analysis of patient records and investigations, stroke aetiology was determined as being due to cardioembolism (200) or large vessel atherosclerosis (190). Patients with a mixed or unknown aetiology were excluded. Diffusion-weighted MRI images from patients were de-identified, normalised and labelled. A 3D convolutional neural network (CNN) was employed with 320 patients in the training cohort and 70 in the validation cohort. Various data uniforming and augmentation echniques were then utilised to improve model accuracy.
Results
The median age of the patient cohort was 74 (IQR 64-83). Forty-three percent of patients were female. The best training accuracy obtained by the model was 80% at epoch 28.
Conclusions
In patients with acute ischaemic stroke, a deep learning model has good accuracy in classifying infarct patterns on MRI as due to cardioembolism or other cause. Future studies should be performed with additional clinical context on larger, prospective datasets.