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.

Purpose: To compare the procedure and safety outcomes of the transradial approach (TRA) with the femoral approach (FA) for treating aortoiliac and femoropopliteal stenoses and occlusions. Methods: A single-center retrospective study was conducted involving 188 patients (mean age 66.4±10.8 years; 116 men) with lower limb claudication or critical limb ischemia who underwent aortoiliac (131, 62.4%) or femoropopliteal (79, 37.6%) interventions on 210 lesions over a 3-year period. Operator discretion determined TRA suitability; exclusions included Raynaud’s disease, upper limb occlusive disease, previous TRA difficulties, or planned hemodialysis. Lesion characteristics, clinical endpoints, and access site complications were compared. Results: FA was used primarily in 123 patients and the TRA (12 left and 53 right radial arteries) in 65 procedures. Eleven (16.9%) TRAs failed vs 9 (7.3%) FAs (p=0.42). Crossover to FA was due to occlusive lesions requiring alternative equipment in 9 cases and to tortuosity of the aortic arch vessels in 2 patients. The 134 FA interventions (balloon angioplasty, stents) were retrograde (112, 83.6%) or antegrade (22, 16.4%). There were significantly more TASC C/D lesions in the FA group (p=0.02). Sheath sizes (5-F to 8-F) did not differ between groups, and no significant differences were found between FA vs TRA in terms of procedure time (50.0±28.9 vs 46.8±25.1 minutes, p=0.50) or length of stay (2.2±0.6 vs 2.1±0.3 days, p=0.24). While there were no strokes, access site complications occurred in 6.0% of the FA patients vs 3.7% of the TRA patients (p=0.12). Conclusion: The transradial approach for aortoiliac and femoropopliteal interventions is safe and efficacious compared with the transfemoral approach for a range of lesion subtypes. Nevertheless, there remains a need for improvements in peripheral device and catheter technology to decrease transradial failure rates.

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.

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.