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Objectives: Accurately predicting short-term MACE (major adverse cardiac events) following primary percutaneous coronary intervention (PCI) remains a clinical challenge. This study aims to assess the effectiveness of four established risk scores in predicting short-term MACE after primary PCI.

Design: Prospective observational study.

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Prevalence of recreational drug use in myocardial infarction patients without standard modifiable risk factors: Insights from a multicenter prospective cohort study.

Int J Cardiol

March 2025

Université Paris Cité, Department of Cardiology, University Hospital of Lariboisiere, (Assistance Publique des Hôpitaux de Paris, AP-HP), 75010 Paris, France; Inserm MASCOT - UMRS 942, University Hospital of Lariboisiere, 75010 Paris, France; MIRACL.ai laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere (AP-HP), 75010 Paris, France.

Background: The prevalence of recreational drug use in myocardial infarction (MI) patients without standard modifiable cardiovascular risk factors (SMuRF) namely hypercholesterolemia, hypertension, diabetes and smoking, remains unknown.

Methods: All patients enrolled in The Addiction in Intensive Cardiac Care Units (ADDICT-ICCU) study, a French multicenter prospective observational study which systematically assessed the use of recreational drug within 2 h of admission, and presenting with MI but without known coronary artery disease were included. We compared patients with and without SMuRF.

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Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, with platelet reactivity playing a central role in its pathogenesis. Recent research has identified microRNAs (miRNAs; miRs) as potential biomarkers for CAD, due to their ability to regulate platelet function and reactivity. This review focuses on four key miRNAs-miR-223, miR-126, miR-21, and miR-150-known to influence platelet reactivity and their implications in CAD.

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Background: Rural areas have higher cardiovascular disease (CVD) incidence and age-adjusted mortality rates in the general population. However, the impact of rurality on CVD development and outcomes in patients with prostate cancer (PC) remains unclear.

Patients And Methods: This retrospective cohort study used the SEER-Medicare database to analyze males aged ≥65 years diagnosed with PC between 2009 and 2017.

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Background: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.

Objective: This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools.

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