AIMC Topic: Acute Coronary Syndrome

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Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.

Scientific reports
Accurate identification of acute coronary syndrome (ACS) in the prehospital setting is important for timely treatments that reduce damage to the compromised myocardium. Current machine learning approaches lack sufficient performance to safely rule-in...

Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning-Based Time-to-Event Analysis.

JMIR medical informatics
BACKGROUND: Patients with acute coronary syndrome (ACS) who undergo percutaneous coronary intervention (PCI) remain at high risk for major adverse cardiovascular events (MACE). Conventional risk scores may not capture dynamic or nonlinear changes in ...

Mortality risk prediction in NSTE-ACS following PCI: Insights from a real-world cohort.

PloS one
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...

Gemini SERS for Cross-Category Biomarker Detection and Early Warning of Sudden Cardiac Death in Acute Coronary Syndrome.

ACS nano
Simultaneous detection of metabolites and proteins─two chemically and functionally distinct classes of biomolecules─in complex biofluids remains a significant analytical hurdle, yet is critical for early and accurate disease diagnosis. These difficul...

Association between the COVID-19 pandemic and cardiopulmonary function in acute coronary syndrome patients without SARS-CoV-2 infection.

Scientific reports
The COVID-19 pandemic disrupted cardiovascular disease management. This single-center cross-sectional cohort study evaluated cardiopulmonary function changes in acute coronary syndrome (ACS) patients post-percutaneous coronary intervention (PCI) with...

Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.

BMJ health & care informatics
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting are suspected of non-ST-elevation acute coronary syndrome (NSTE-ACS). Distinguishing true NSTE-ACS from non-cardiac chest pain based solely on the EC...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Scientific reports
Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) score assesses cardiovascular risk post-ACS. This study evaluated whether incorporating the STOP-BANG sc...

Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets.

Nature communications
Coronary artery disease (CAD) is a leading cause of death globally. Antiplatelet therapy remains crucial in preventing and treating CAD-associated thrombotic complications, but it concurrently amplifies the risk of bleeding. Unfortunately, traditiona...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...