Venous Thrombosis

Precision Medicine for Anticoagulation Strategies in the Cath Lab: Part 2.

PURPOSE OF REVIEW: Intraprocedural anticoagulation during percutaneous coronary intervention (PCI) remains particularly challenging in high-risk and underrepresented populations, where the balance between thrombotic and bleeding risk is complex and often unpredictable. This review summarizes contemporary evidence and remaining knowledge gaps regarding anticoagulant selection, dosing, and monitorin...

Prognosis Prediction and Shared Decision Making for Personalised Anticoagulant Therapy after Unprovoked Venous Thromboembolism: The MORPHEUS project.

Background In patients with unprovoked venous thromboembolism (VTE), indefinite anticoagulation is recommended to prevent recurrence but may expose some patients to unnecessary long-term bleeding risk. Current clinical scores and biomarkers have limited discriminative performance, do not capture the time-dependent nature of VTE, and do not incorporate patients' perspectives or support shared decis...

Predictive analytics and risk stratification models in internal medicine: from risk scores to real-time machine learning.

Internal medicine involves high-stakes, time-sensitive decisions (such as triaging acute illnesses, escalating care, providing thromboprophylaxis, pla...

Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression.

Current pharmacometrics (PMX) model development is a manual process with iterative model building, fitting, and evaluation, which can be resource-inte...

Approaches to transcatheter aortic valve replacement failure: is more focus on prevention required?

INTRODUCTION: Transcatheter aortic valve replacement (TAVR) has transformed the management of severe aortic stenosis across all surgical risk strata. ...

Secondary prevention of recurrent ischemic stroke: from guidelines to precision medicine.

Recurrent ischemic stroke remains a major global health challenge, accounting for substantial disability and mortality despite advances in acute manag...

Integrated NEK7-inflammasome and platelet transcriptomic signature generates mechanistic hypotheses in heart failure.

BACKGROUND: The relationship between NEK7-NLRP3 inflammasome activation and elevated platelet activity in the progression of heart failure (HF) is not...

Zero-shot design of drug-binding proteins via neural iterative selection-expansion.

The design of proteins that bind to small molecules has been challenging because it requires simultaneous optimization of the protein sequence, protei...

Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.

Left ventricular thrombus (LVT) in ischemic heart failure carries embolic risk; tools to anticipate persistence are limited. We studied 190 consecutiv...

Artificial intelligence in the diagnosis of deep vein thrombosis: A scoping review.

Deep vein thrombosis (DVT) is the formation of thrombi in the deep venous system, most often in the lower extremities. Although usually not life-threa...

Gut microbiome signatures discriminate deep vein thrombosis through machine learning and metabolic analysis.

Deep vein thrombosis (DVT) remains difficult to distinguish because of its often silent presentation and the limited specificity of current diagnostic...

Applying machine learning to pharmacovigilance data: A proof-of-concept study.

AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance d...

Multimodal Deep Learning for Predicting Deep Vein Thrombosis Risk After Total Knee Arthroplasty: A Clinical Study.

BACKGROUND: Deep vein thrombosis (DVT) following total knee arthroplasty (TKA) remains a major postoperative complication. Conventional risk assessmen...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

Combinations of single gene predictors with AI-driven polygenic precision therapies in cardiovascular diseases.

Pharmacogenomics (PGx) is transforming how we treat cardiovascular disease (CVD) by enabling us to select and dose drugs based on our genetic profiles...

Impact of middle meningeal artery embolization on neurological complications and discharge outcomes in chronic subdural hematoma: A machine learning-based comparative study.

INTRODUCTION: Chronic subdural hematoma (cSDH) predominantly affects older adults, often those with prior head trauma, anticoagulation therapy, or chr...

End to End Proximal Anastomosis is the Sole Modifiable Variable in Risk Score for Loss of Patency of Aortobifemoral Artery Bypass After Index Operative Hospitalization.

OBJECTIVE: The purpose of this study was to create a risk score for loss of aorto-bifemoral artery bypass (ABF) patency utilizing preoperative, periop...

Development, internal testing, and external validation of an interpretable machine-learning model for predicting imaging-confirmed in-hospital postoperative deep vein thrombosis in older patients with patellar fractures.

BACKGROUND: Older patients with patellar fractures may be at increased risk of postoperative deep vein thrombosis (DVT) because of trauma, perioperati...

Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives.

Previously, we reported a dual combination based on 4-hydroxycoumarin and dodecanedioic acid that could synergistically bind to human serum albumin (H...

Predictors of warfarin therapeutic dose in cancer patients: a preliminary comparative study using machine learning-integrated explainable artificial intelligence and Bayesian models.

BACKGROUND: Warfarin dosing in cancer patients is challenging due to altered pathophysiology and variable responses. While machine learning offers pre...