Machine learning using combined coronary CT angiography and stress cardiac MRI data outperformed existing scores in predicting major adverse cardiovascular events in patients with newly diagnosed coronary artery disease.

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.

A cardiac MRI model incorporating the ischemic late gadolinium enhancement (LGE) parameters of extent, transmurality, location, and midwall LGE significantly outperformed traditional risk factors in predicting all-cause mortality in participants with ischemic cardiomyopathy.

Aortic valve-in-valve (ViV) procedures are increasingly performed for the treatment of surgical bioprosthetic valve failure in patients at intermediate to high surgical risk. Although ViV procedures offer indisputable benefits in terms of procedural time, in-hospital length of stay, and avoidance of surgical complications, they also present unique challenges. Growing awareness of the technical difficulties and potential threats associated with ViV procedures mandates careful preprocedural planning. This review article offers an overview of the current state-of-the-art ViV procedures, with focus on patient and device selection, procedural planning, potential complications, and long-term outcomes. Finally, it discusses current research efforts and future directions aimed at improving ViV procedural success and patient outcomes.

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.

BACKGROUND: Stress perfusion cardiovascular magnetic resonance (CMR) is widely used to detect myocardial ischemia, mostly through visual assessment. Recent studies suggest that strain imaging at rest and during stress can also help in prognostic stratification. However, the additional prognostic value of combining both rest and stress strain imaging has not been fully established. This study examined the incremental benefit of combining these strain measures with traditional risk prognosticators and CMR findings to predict major adverse clinical events (MACE) in a cohort of consecutive patients referred for stress CMR. METHODS: This retrospective, single-center observational study included all consecutive patients with known or suspected coronary artery disease referred for stress CMR between 2016 and 2018. Fully automated machine learning was used to obtain global longitudinal strain at rest (rest-GLS) and global circumferential strain at stress (stress-GCS). The primary outcome was MACE, including cardiovascular death or hospitalization for heart failure. Cox models were used to assess the incremental prognostic value of combining these strain features with traditional prognosticators. RESULTS: Of 2778 patients (age 65±12 years, 68% men), 96% had feasible, fully automated rest-GLS and stress-GCS measurements. After a median follow-up of 5.2 (4.8–5.5) years, 316 (11.1%) patients experienced MACE. After adjustment for traditional prognosticators, both rest-GLS (hazard ratio, 1.09 [95% CI, 1.05–1.13]; P <0.001) and stress-GCS (hazard ratio, 1.08 [95% CI, 1.03–1.12]; P –10% for rest-GLS and stress-GCS, with a C-index improvement of 0.02, continuous net reclassification improvement of 15.6%, and integrative discrimination index of 2.2% (all P –10% provided an incremental prognostic value over and above traditional prognosticators, including CMR parameters, for predicting MACE in patients undergoing stress CMR.