Venous Thrombosis

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...

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 high-quality clinical evidence. AIM: To identify and validate a machine learning model to predict thrombus regression in postpartum VTE patients. METHOD: This study constructed three cohorts of postpartum VTE patients receiving low-molecular-weight he...

Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation.

Atrial fibrillation (AF) is the most common sustained arrhythmia and a leading cause of ischemic stroke. Existing risk scores, such as CHA₂DS₂-VASc, o...

Integrated Strategies for Nursing and Rehabilitation in Lung Cancer Patients.

This review delineates the pivotal role of nursing and rehabilitation in perioperative management and chemotherapy support for lung cancer patients, w...

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 (...

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...

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 ...

Artificial Intelligence Applications in COVID-19-Associated Coagulopathy: Lessons Learned.

Coronavirus disease 2019 (COVID-19)-associated coagulopathy (CAC) is a thromboinflammatory syndrome marked by endothelial injury, micro- and macrovasc...

Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a significant complication in surgical patients. Ar...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a p...

Evaluation of the impact of NOAC underdosing and exploration of bleeding risk factors in elderly patients with atrial fibrillation: artificial intelligence-based approach.

OBJECTIVE: Atrial fibrillation in elderly patients increases the risk of thromboembolism, necessitating long-term anticoagulation. While non-vitamin K...

Multicentre development and validation of a risk model integrating immunotherapy and coagulation biomarkers for thrombosis in autoimmune neurological disorders.

PURPOSE: Patients with Autoimmune Neurological Disorders (ANDs) require routine immunomodulatory therapy, which inherently increases thrombosis risk. ...

Assessment of Predictive Factors That Shorten Duration of Treatment in Patients With Multiple Myeloma Using AI: Real-World Longitudinal Study Using Data From Medical Data Vision Claims Database.

BACKGROUND: With the availability of newer therapies, the duration of therapy (DoT) shortens with each increasing line of treatment in Japanese patien...

Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation.

Cardiac tamponade is a rare yet catastrophic complication during atrial fibrillation (AF) catheter ablation. Influenced by multiple procedural and pat...

Prediction model for deep vein thrombosis stability based on multiple machine learning methods.

BackgroundThis study aimed to develop multiple machine learning (ML) models to predict DVT stability based on clinical and computed tomography (CT) te...

Deep Learning and Machine Learning for Differentiation Between Contrast Extravasation and Hemorrhagic Transformation in Post-Thrombectomy Stroke CT.

BACKGROUND: Mechanical thrombectomy (MT) improves outcomes in acute ischemic stroke (AIS) but often results in hyperdensities on non-contrast CT (NCCT...

[Cardiology : what's new in 2025].

In 2025, significant progress has been made in the management of heart failure and cardiovascular diseases, driven by the emergence of new treatments ...

Artificial intelligence and machine learning for precision warfarin dosing: a comprehensive narrative review.

BACKGROUND: Warfarin remains one of the most widely used anticoagulants; however, its narrow therapeutic index means that even small dosing deviations...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...