Acute Coronary Syndrome

Development of a nomogram with machine learning-assisted feature selection for predicting left atrial appendage thrombosis and severe spontaneous echo contrast in patients with non-paroxysmal atrial fibrillation.

Thrombosis after surgical splenectomy - why, in whom and can we prevent it?

Splenectomy remains a common operation performed in the setting of trauma, haematological disease, malignancy and diagnostic purposes. Contemporary evidence indicates an increased risk of thromboembolism after splenectomy. This includes both systemic venous thromboembolism (deep vein thrombosis and pulmonary embolism) and splanchnic thrombosis involving the portal-splenic-mesenteric axis. Comparat...

Can an artificial intelligence electrocardiogram algorithm improve diagnostic accuracy for acute coronary occlusion in the difficult subset of canceled catheterization lab activations?

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) and its equivalents describe the electrocardiogram (ECG) findings of acute coronary occ...

Ischaemia in tuberculous meningitis: update on pathophysiology, clinical predictors and management.

PURPOSE OF REVIEW: Tuberculous meningitis (TBM) is a severe manifestation of Mycobacterium tuberculosis infection, associated with high mortality and ...

"Unpatients" in haemostasis and thrombosis. A yardstick of current paths in healthy carriers of a genetic susceptibility.

"Unpatients" are health-conscious individuals -carrying a genetic susceptibility whose impact is unclear with respect to the risk of a clinical event-...

Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases.

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failu...

Equiflow: An open-source software package for evaluating changes in cohort composition.

Clinical research studies routinely apply exclusion criteria and data preprocessing steps that can substantially alter dataset composition, potentiall...

Magnetically controlled capsule endoscopy: Innovations, standardized practice, and emerging clinical roles.

Capsule endoscopy has transformed small bowel evaluation but remains limited for gastric examination because of passive, peristalsis dependent movemen...

A novel predictive model for evaluating thrombus regression in postpartum venous thromboembolism patients.

INTRODUCTION: Antithrombotic evaluation of postpartum venous thromboembolism (VTE) after anticoagulant therapy is challenging because of the lack of h...

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Exploratory prediction model for deep vein thrombosis in intensive care patients after anticoagulation therapy: An observational study.

BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (...

Development and validation of an interpretable machine learning model for predicting portal vein system thrombosis following splenectomy in Wilson's disease.

Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional Radiology.

PURPOSE: This study evaluates Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) frameworks for generating accurate, gu...

Early Versus Delayed Anticoagulation in Acute Ischaemic Stroke with Atrial Fibrillation According to Infarct Volume and Location: a Prespecified Subgroup Analysis of the OPTIMAS Randomised Controlled Trial.

BackgroundRandomised trials have demonstrated that early anticoagulation after acute atrial fibrillation-associated ischaemic stroke is safe and non-i...

Assessing the Predictive Value of Kolmogorov-Arnold Networks for the No-Reflow Phenomenon in ST-Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study.

OBJECTIVE: The no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) is a significant clinical issue associated with poor cardiov...

Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients.

BACKGROUND: Central venous catheters for drug delivery introduce catheter-related thrombosis (CRT) and influence the survival of cancer patients. The ...

Interpretable Machine Learning for Feature-Based Classification of Platelet Activation in Rotary Blood Pumps.

BACKGROUND: Thrombosis in rotary blood pumps arises from complex flow conditions that remain difficult to translate into reliable and interpretable ri...

Multiphysics learning with graph neural networks for thrombosis prediction in intracranial aneurysms.

Intracranial aneurysms are pathological dilations of cerebral arteries affecting 3-5% of the population. Their rupture can cause subarachnoid hemorrha...

Machine learning-based prediction of myocardial ischemia-reperfusion injury in patients with ST-segment elevation myocardial infarction: the STEMI-MIRI prediction model.

Beyond Percutaneous Coronary Intervention - Targeted Molecular Therapies for the Next Era of Coronary Care.

Coronary artery disease (CAD) remains the leading global cause of cardiovascular morbidity and mortality, driven by dysregulated lipid metabolism, chr...