To undertake an economic evaluation of rivaroxaban relative to the standard of care for stroke prevention in patients with non-valvular atrial fibrillation (AF) in Greece.

Background: In the setting of ST-segment elevation myocardial infarction (STEMI), the faster and stronger antiplatelet action of ticagrelor compared to clopidogrel, as well as its pleiotropic effects, could result in a greater degree of cardioprotection and final infarct size (FIS) limitation. The aim of our study was to comparatively evaluate the effect of ticagrelor and clopidogrel on myocardial salvage index (MSI) in STEMI patients undergoing thrombolysis. Methods: Forty-two STEMI patients treated with thrombolysis were randomized to receive clopidogrel (n = 21) or ticagrelor (n = 21), along with aspirin. Myocardial area at risk (AAR) was calculated according to the BARI and the APPROACH jeopardy scores. FIS was quantified by cardiac magnetic resonance imaging (CMR) performed 5–6 months post-randomization. MSI was calculated as (AAR-FIS)/AAR × 100%. Primary endpoint of our study was MSI. Secondary endpoints were FIS and CMR-derived left ventricular ejection fraction (LVEF) at 5 –6 months post-randomization. Results: By using the BARI score for AAR calculation, mean MSI was 52.25 ± 30.5 for the clopidogrel group and 54.29 ± 31.08 for the ticagrelor group (p = 0.83), while mean MSI using the APPROACH score was calculated at 51.94 ± 30 and 53.09 ± 32.39 (p = 0.9), respectively. Median CMR-derived FIS—as a percentage of LV—was 10.7% ± 8.25 in the clopidogrel group and 12.09% ± 8.72 in the ticagrelor group (p = 0.6). Mean LVEF at 5–6 months post-randomization did not differ significantly between randomization groups. Conclusions: Our results suggest that the administration of ticagrelor in STEMI patients undergoing thrombolysis offer a similar degree of myocardial salvage, compared to clopidogrel.

Artificial intelligence (AI) is transforming cardiovascular medicine through applications in disease detection, diagnosis, risk prediction, and clinical decision support. However, the clinical implementation of these technologies remains poorly characterised. This systematic review evaluated the current landscape of AI applications in cardiovascular medicine, focusing on implementation maturity and clinical translation. The review followed PRISMA guidelines and a prospectively registered PROSPERO protocol. Data extraction included study characteristics, cardiovascular domain, AI methodology, clinical application, validation strategy and implementation maturity, assessed using a predefined five-level framework. AI methodologies were classified as conventional machine learning, deep learning, hybrid ML/deep learning, multimodal AI, or large language models/generative AI. Seventy-four studies met the eligibility criteria. Conventional machine learning was the most frequently used methodology (47.3%), followed by deep learning (41.9%), whereas multimodal AI (5.4%), hybrid ML/deep learning (2.7%), and large language models/generative AI (2.7%) were uncommon. Applications focused mainly on screening and early detection (31.1%), risk stratification and prognosis (25.7%), treatment planning (16.2%), diagnosis (13.5%), and monitoring (12.2%). Most studies reached implementation maturity Level 3 (clinical validation, 47.3%) or Level 2 (technical validation, 35.1%), while only 13.5% achieved routine clinical implementation (Level 5) and 4.1% reached clinical deployment (Level 4). Although AI demonstrated promising diagnostic and prognostic performance across multiple cardiovascular conditions, most applications remain at the validation stage. Future research should prioritise implementation science, pragmatic evaluation, and real-world evidence to facilitate routine adoption and maximise patient benefit and healthcare value.