Comparison between the SAPIEN S3 and the SAPIEN XT transcatheter heart valves: A single-center experience

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks.

Background: Epidemiological characteristics and prognostic profiles of patients with newly diagnosed coronary artery disease (CAD) are heterogeneous. Therefore, providing individualized cardiovascular (CV) risk stratification and tailored prevention is crucial.Objective: Phenotypic unsupervised clustering integrating clinical, coronary computed tomography angiography (CCTA), and cardiac magnetic resonance (CMR) data were used to unveil pathophysiological differences between subgroups of patients with newly diagnosed CAD.Materials and Methods: Between 2008 and 2020, consecutive patients with newly diagnosed obstructive CAD on CCTA and further referred for vasodilator stress CMR were followed for the occurrence of major adverse cardiovascular events (MACE), defined by cardiovascular death or non-fatal myocardial infarction. For this exploratory work, a cluster analysis was performed on clinical, CCTA, and CMR variables, and associations between phenogroups and outcomes were assessed.Results: Among 2,210 patients who underwent both CCTA and CMR, 2,015 (46% men, mean 70 ± 12 years) completed follow-up [median 6.8 (IQR 5.9–9.2) years], in which 277 experienced a MACE (13.7%). Three mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: (PG1) CAD in elderly patients with few traditional risk factors; (PG2) women with metabolic syndrome, calcified plaques on CCTA, and preserved left ventricular ejection fraction (LVEF); (PG3) younger men smokers with proximal non-calcified plaques on CCTA, myocardial scar, and reduced LVEF. Using survival analysis, the occurrence of MACE, cardiovascular mortality, and all-cause mortality (all p < 0.001) differed among the three PG, in which PG3 had the worse prognosis. In each PG, inducible ischemia was associated with MACE [PG1, Hazards Ratio (HR) = 3.09, 95% CI, 1.70–5.62; PG2, HR = 3.62, 95% CI, 2.31–5.7; PG3, HR = 3.55, 95% CI, 2.3–5.49; all p < 0.001]. The study presented some key limitations that may impact generalizability.Conclusions: Cluster analysis of clinical, CCTA, and CMR variables identified three phenogroups of patients with newly diagnosed CAD that were associated with distinct clinical and prognostic profiles. Inducible ischemia assessed by stress CMR remained associated with the occurrence of MACE within each phenogroup. Whether automated unsupervised phenogrouping of CAD patients may improve clinical decision-making should be further explored in prospective studies.

Background: Mobile health applications have emerged as a novel tool to support secondary prevention after myocardial infarction (MI) or percutaneous coronary intervention (PCI). However, the impact of app-based interventions on clinically meaningful outcomes such as hospital readmissions remains uncertain. Objective: To systematically evaluate the effectiveness of smartphone app-based interventions in reducing unplanned hospital readmissions among post-MI/PCI patients. Methods: A systematic search of PubMed was conducted for randomized controlled trials published between January 2020 and April 2025. Eligible studies evaluated smartphone apps designed for secondary cardiovascular prevention and reported on unplanned hospital readmissions. Risk ratios (RRs) and 95% confidence intervals (CIs) were pooled using a random-effects model. Subgroup analyses were performed based on follow-up duration and user adherence. Results: Four trials encompassing 827 patients met inclusion criteria. App-based interventions were associated with a significant reduction in unplanned hospital readmissions compared to standard care (RR 0.45; 95% CI: 0.23–0.89; p = 0.0219). Greater benefits were observed in studies with longer follow-up durations and higher adherence rates. Improvements in patient-reported outcomes, including health-related quality of life, were also documented. Heterogeneity was moderate. Major adverse cardiovascular events (MACEs) were reported in only two studies and were not analyzed due to inconsistent definitions and low event rates. Conclusions: Smartphone applications for post-MI/PCI care are associated with reduced unplanned hospital readmissions and improved patient-reported outcomes. These tools may play a meaningful role in future cardiovascular care models, especially when sustained engagement and personalized features are prioritized.

Stroke following transcatheter aortic valve replacement (TAVR) is a significant and life-threatening adverse event. The vast majority of these incidents occur during the TAVR procedure or within the first 24 h following TAVR, with a notable prevalence of cerebral embolic events. In response to this concern, cerebral embolic protection devices (CEPDs) have been designed to mitigate the risk of peri-procedural ischemic stroke during TAVR. The primary objective of CEPDs is to diminish the intraprocedural burden associated with new silent ischemic brain injuries. Despite the development of several CEPDs, their clinical efficacy remains uncertain. In this review, we delve into a comprehensive analysis of the utilization of CEPDs in patients undergoing TAVR, exploring insights from the existing literature. Additionally, we aim to present future perspectives and discuss the clinical implications associated with the incorporation of CEPDs in TAVR procedures.

Coronary computed tomography angiography (CCTA) is increasingly recognized as a comprehensive tool for planning percutaneous coronary intervention (PCI). By integrating plaque morphology, calcium burden, and CT-derived coronary physiology, CCTA enables non-invasive assessment of lesion complexity and supports precision-guided revascularization. This narrative review synthesizes current evidence on CT-guided PCI from original studies, registries, expert consensus documents, and international guideline recommendations. The literature was identified through PubMed, Embase, and Google Scholar, focusing on CCTA-based plaque characterization, calcium assessment, bifurcation and ostial lesions, chronic total occlusions (CTO), FFR-CT, virtual PCI simulation, and fusion imaging. Particular attention was given to contemporary investigations such as SYNTAX III, P3, and the ongoing P4 trial. CCTA reliably characterizes stenosis severity, plaque distribution, and calcification, demonstrating strong concordance with intravascular imaging. CT-based measurements support accurate stent sizing, prediction of calcium modification requirements, and identification of high-risk features in bifurcation and ostial disease. In CTO PCI, CCTA enhances visualization of proximal cap morphology, occlusion length, tortuosity, and distal vessel quality, outperforming angiographic scoring systems. CT-derived physiology and virtual PCI planning improve lesion selection and allow prediction of post-PCI hemodynamics. Emerging technologies—including photon-counting CT, artificial intelligence-assisted plaque analysis, and CT–fluoroscopy fusion—further expand the applicability of CT-guided PCI. The ongoing P4 trial is expected to provide definitive validation of CT-guided PCI and may support its incorporation into routine clinical workflows.