Importance:Patent foramen ovale (PFO)-associated strokes comprise approximately 10% of ischemic strokes in adults aged 18 to 60 years. While device closure decreases stroke recurrence risk overall, the best treatment for any individual is often unclear. Objective:To evaluate heterogeneity of treatment effect of PFO closure on stroke recurrence based on previously developed scoring systems. Design, Setting, and Participants:Investigators for the Systematic, Collaborative, PFO Closure Evaluation (SCOPE) Consortium pooled individual patient data from all 6 randomized clinical trials that compared PFO closure plus medical therapy vs medical therapy alone in patients with PFO-associated stroke, and included a total of 3740 participants. The trials were conducted worldwide from 2000 to 2017. Exposures:PFO closure plus medical therapy vs medical therapy alone. Subgroup analyses used the Risk of Paradoxical Embolism (RoPE) Score (a 10-point scoring system in which higher scores reflect younger age and the absence of vascular risk factors) and the PFO-Associated Stroke Causal Likelihood (PASCAL) Classification System, which combines the RoPE Score with high-risk PFO features (either an atrial septal aneurysm or a large-sized shunt) to classify patients into 3 categories of causal relatedness: unlikely, possible, and probable. Main Outcomes and Measures:Ischemic stroke. Results:Over a median follow-up of 57 months (IQR, 24-64), 121 outcomes occurred in 3740 patients. The annualized incidence of stroke with medical therapy was 1.09% (95% CI, 0.88%-1.36%) and with device closure was 0.47% (95% CI, 0.35%-0.65%) (adjusted hazard ratio [HR], 0.41 [95% CI, 0.28-0.60]). The subgroup analyses showed statistically significant interaction effects. Patients with low vs high RoPE Score had HRs of 0.61 (95% CI, 0.37-1.00) and 0.21 (95% CI, 0.11-0.42), respectively (P for interaction = .02). Patients classified as unlikely, possible, and probable using the PASCAL Classification System had HRs of 1.14 (95% CI, 0.53-2.46), 0.38 (95% CI, 0.22-0.65), and 0.10 (95% CI, 0.03-0.35), respectively (P for interaction = .003). The 2-year absolute risk reduction was -0.7% (95% CI, -4.0% to 2.6%), 2.1% (95% CI, 0.6%-3.6%), and 2.1% (95% CI, 0.9%-3.4%) in the unlikely, possible, and probable PASCAL categories, respectively. Device-associated adverse events were generally higher among patients classified as unlikely; the absolute risk increases in atrial fibrillation beyond day 45 after randomization with a device were 4.41% (95% CI, 1.02% to 7.80%), 1.53% (95% CI, 0.33% to 2.72%), and 0.65% (95% CI, -0.41% to 1.71%) in the unlikely, possible, and probable PASCAL categories, respectively. Conclusions and Relevance:Among patients aged 18 to 60 years with PFO-associated stroke, risk reduction for recurrent stroke with device closure varied across groups classified by their probabilities that the stroke was causally related to the PFO. Application of this classification system has the potential to guide individualized decision-making.

OBJECTIVE:Patent foramen ovale (PFO) is often found in stroke patients with determined etiologies. PFO may be the actual cause of stroke in some of them. We determined whether the risk of recurrent ischemic stroke differs with PFO status in stroke patients with determined etiologies. METHODS:This study included consecutive patients with stroke of determined etiology who underwent transesophageal echocardiography. We compared the rates of recurrent cerebral infarction in patients with versus without PFO, and according to PFO-Associated Stroke Causal Likelihood (PASCAL) classification. RESULTS:Of 2,314 included patients, 827 (35.7%) had PFO. During a median follow-up of 4.4 years, cerebral infarction recurred in 202 (8.7%). In multivariate modified Cox regression analyses, recurrence of infarction did not significantly differ between patients with PFO and those without PFO (hazard ratio [HR] = 0.86, 95% confidence interval [CI] = 0.64-1.17, p = 0.339). Interaction analysis showed a significant effect of PFO in patients aged <65 years (adjusted p for interaction = 0.090). PFO was independently associated with a decreased risk of recurrent infarction in patients younger than 65 years (HR = 0.41, 95% CI = 0.20-0.85, adjusted p = 0.016). Patients with probable PFO-associated stroke on the PASCAL classification had a significantly lower risk of recurrent infarction than those without PFO (HR = 0.31, 95% CI = 0.10-0.97, p = 0.044). INTERPRETATION:Considering the generally low risk of recurrence in PFO-associated stroke, PFO may be the actual cause of stroke in some patients with determined etiologies, especially younger patients or those with PFO features of probable PFO-associated stroke. ANN NEUROL 2022;92:596-606.

The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine. © 2021 American Physiological Society. Compr Physiol 11:1-12, 2021.

Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare systems including hospitals and clinics has many possible advantages and future prospects. Applications of AI in cardiovascular medicine are machine learning techniques for diagnostic procedures including imaging modalities and biomarkers and predictive analytics for personalized therapies and improved outcomes. In cardiovascular medicine, AI-based systems have found new applications in risk prediction for cardiovascular diseases, in cardiovascular imaging, in predicting outcomes after revascularization procedures, and in newer drug targets. AI such as machine learning has partially resolved and provided possible solutions to unmet requirements in interventional cardiology. Predicting economically vital endpoints, predictive models with a wide range of health factors including comorbidities, socioeconomic factors, and angiographic factors comprising of the size of stents, the volume of contrast agent which was infused during angiography, stent malposition, and so on have been possible owing to machine learning and AI. Nowadays, machine learning techniques might possibly help in the identification of patients at risk, with higher morbidity and mortality following acute coronary syndrome (ACS). AI through machine learning has shown several potential benefits in patients with ACS. From diagnosis to treatment effects to predicting adverse events and mortality in patients with ACS, machine learning should find an essential place in clinical medicine and in interventional cardiology for the treatment and management of patients with ACS. This paper is a review of the literature which will focus on the application of AI in ACS.