Acute Coronary Syndrome

Artificial Intelligence-Enhanced Risk Stratification in Non-ST-Segment Elevation Myocardial Infarction: The Incremental Prognostic Value of the Modified Glasgow Prognostic Score in Predicting 36-Month Mortality.

OBJECTIVE: Current clinical guidelines for non-ST-segment elevation myocardial infarction (NSTEMI) emphasize the duration of dual antiplatelet therapy (DAPT) based on scores such as the Predicting Bleeding Complications in Patients Undergoing Stent Implantation and Subsequent Dual Antiplatelet Therapy (PRECISE-DAPT) score and the Dual Antiplatelet Therapy (DAPT) score. However, these anatomical an...

Beyond Accuracy: Why Replacing Traditional Scores with AI in Thrombosis Care Creates New Ethical and Regulatory Obligations.

Clinical prediction scores have been central to thrombosis and hemostasis practice for decades, providing transparent, interpretable, and validated approaches to risk assessment. Increasingly, artificial intelligence (AI)-based predictive models are being developed to improve risk stratification by integrating large, complex datasets and identifying patterns beyond conventional statistical models....

Multi-Omics based biomarkers in acute coronary syndrome: Towards precision in cardiovascular medicine.

BACKGROUND: Acute coronary syndrome (ACS) remains a leading cause of cardiovascular morbidity and mortality worldwide. High-throughput omics technolog...

Lipoprotein(a) and the C-reactive protein-triglyceride-glucose index in relation to coronary lesion burden in patients with acute coronary syndrome.

BACKGROUND: Lipoprotein(a) [Lp(a)] reflects inherited atherothrombotic risk, whereas the C-reactive protein-triglyceride-glucose index (CTI) integrate...

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation...

Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers.

BACKGROUND: Early diagnosis of acute coronary syndromes (ACS) remains challenging because electrical, mechanical, and biochemical manifestations of my...

Takotsubo syndrome diagnosis: the biomarker-based BioTAK score.

BACKGROUND AND AIMS: Takotsubo syndrome (TTS) closely mimics acute coronary syndrome (ACS). Early differentiation remains challenging because diagnosi...

Correction to: Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.

AI-Powered, Temperature-Resolved Centrifugal Microfluidics for Rapid 3D Phase-Diagram Generation of Biomolecular Condensates.

Biomolecular phase separation drives the formation of membraneless organelles and is implicated in numerous diseases. Mapping multidimensional phase d...

Development and Validation of the GLORIA-AF Stroke Weighted Risk Score for Patients with Atrial Fibrillation.

BACKGROUND AND AIMS: Accurate stroke-risk stratification is central to anticoagulation decision-making in patients with atrial fibrillation (AF), but ...

Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.

Early identification of acute myocardial infarction (AMI) remains challenging, particularly in non-ST-segment elevation presentations and occluded myo...

Preserving Expertise in Vitamin K Antagonists in the Era of Direct Oral Anticoagulants: Avoiding Therapeutic Segregation.

The rapid uptake of direct oral anticoagulants (DOACs) has sharply reduced the prescribing of vitamin K antagonists (VKAs), giving rise to an underapp...

An artificial intelligence and numerical simulation-based strategy for multi-objective optimization of hemodynamics in curved vessels.

Understanding and optimizing blood flow behavior in curved vessels is crucial for improving cardiovascular treatment planning and reducing complicatio...

Recent advances and knowledge gaps in anticoagulation strategy with durable left ventricular assist devices.

Driven by technological advancements, donor shortages, and an aging population, utilization of left ventricular assist devices (LVAD), both as a bridg...

Predictive, preventive, and personalized medicine in statin-treated patients with NSTE-ACS, HFpEF, and type 2 diabetes: a data-driven model for long-term residual risk assessment.

BACKGROUND: Despite significant advances in guideline-directed medical therapy (GDMT), statin-treated patients with non-ST-elevation acute coronary sy...

Small Intracerebral Hemorrhage: Baseline CT Edema-to-Hematoma Ratio as an Independent Predictor of Early Expansion.

BACKGROUND AND PURPOSE: Small intracerebral hemorrhage (ICH), defined as baseline NCCT hematoma volume (HV) <30 mL, is often considered lower risk for...

Patient Stratification for Improving Acute Chest Pain Management and Mitigating Emergency Department Crowding: Machine Learning Model Development and Validation.

BACKGROUND: Acute chest pain (ACP) is one of the most common chief complaints in the emergency department (ED), accounting for approximately 8% of all...

Risk Factors for Pediatric Acute Chest Syndrome Utilizing Machine Learning Techniques.

Acute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric patients with sickle cell disease (SCD). Despite kno...

Decision-making logic for samples of clinically significant EDTA-dependent pseudothrombocytopenia with machine learning.

OBJECTIVES: To establish practical, clinically meaningful criteria for identifying significant platelet aggregation in suspected samples, defined by a...

From In Silico De Novo Generation to In Vitro Functional Validation: Discovery of Multifunctional Antithrombotic Peptides from Food-Derived Proteins via a Hybrid LSTM-GCN and Molecular Simulation Pipeline.

Cardiovascular thromboses bring heavy global health burdens, while traditional screening of food-derived antithrombotic peptides is inefficient. This ...