The optimal management of patients with asymptomatic carotid stenosis (ACS) is the subject of extensive debate. According to the 2017 European Society for Vascular Surgery guidelines, carotid endarterectomy should (Class IIa; Level of Evidence: B) or carotid artery stenting may be considered (Class IIb; Level of Evidence: B) in the presence of one or more clinical/imaging characteristics that may be associated with an increased risk of late ipsilateral stroke (e.g., silent embolic infarcts on brain computed tomography/magnetic resonance imaging, progression in the severity of ACS, a history of contralateral transient ischemic attack/stroke, microemboli detection on transcranial Doppler, etc.), provided documented perioperative stroke/death rates are <3% and the patient’s life expectancy is >5 years. Besides these clinical/imaging characteristics, there are additional individual, ethnic/racial or social factors that should probably be evaluated in the decision process regarding the optimal management of these patients, such as individual patient needs/patient choice, patient compliance with best medical treatment, patient sex, culture, race/ethnicity, age and comorbidities, as well as improvements in imaging/operative techniques/outcomes. The present multispecialty position paper will present the rationale why the management of patients with ACS may need to be individualized.

The optimal management of patients with asymptomatic carotid stenosis (ACS) is the subject of extensive debate. According to the 2017 European Society for Vascular Surgery guidelines, carotid endarterectomy should (Class IIa; Level of Evidence: B) or carotid artery stenting may be considered (Class IIb; Level of Evidence: B) in the presence of one or more clinical/imaging characteristics that may be associated with an increased risk of late ipsilateral stroke (e.g., silent embolic infarcts on brain computed tomography/magnetic resonance imaging, progression in the severity of ACS, a history of contralateral transient ischemic attack/stroke, microemboli detection on transcranial Doppler, etc.), provided documented perioperative stroke/death rates are <3% and the patient’s life expectancy is >5 years. Besides these clinical/imaging characteristics, there are additional individual, ethnic/racial or social factors that should probably be evaluated in the decision process regarding the optimal management of these patients, such as individual patient needs/patient choice, patient compliance with best medical treatment, patient sex, culture, race/ethnicity, age and comorbidities, as well as improvements in imaging/operative techniques/outcomes. The present multispecialty position paper will present the rationale why the management of patients with ACS may need to be individualized.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established.It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction.Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.