Venous Thrombosis

Novel, Precise, Accurate Ion-Pairing Method to Determine the Related Substances of the Fondaparinux Sodium Drug Substance: Low-Molecular-Weight Heparin.

Fondaparinux sodium is a synthetic low-molecular-weight heparin (LMWH). This medication is an anticoagulant or a blood thinner, prescribed for the treatment of pulmonary embolism and prevention and treatment of deep vein thrombosis. Its determination in the presence of related impurities was studied and validated by a novel ion-pair HPLC method. The separation of the drug and its degradation produ...

Revisiting Warfarin Dosing Using Machine Learning Techniques.

Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for warfarin dosing is propos...

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mort...

Beyond Padua and IMPROVE: Machine Learning Outperforms Guideline Risk Scores for Prediction of Radiologically Confirmed Hospital-Acquired Venous Thromboembolism

*Background:** Hospital-acquired venous thromboembolism (VTE) is a leading preventable cause of in-hospital morbidity and mortality. Guideline-endorse...

Predicting VHH-Fc Developability from Large-Scale IgG Data

The VHH-Fc antibody scaffold is an emerging therapeutic modality. No public large-scale, standardized developability VHH-Fc dataset exists. Filling th...

Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation

Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultr...

Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark

RGB-Thermal (RGBT) Video Object Detection (VOD) has gained significant traction due to its ability to overcome the limitations of conventional RGB-bas...

Multisite Real-World Validation of an Electronic Health Record-Integrated Generative Artificial Intelligence Tool for Venous Thromboembolism Risk Stratification

Background: Guiding risk-appropriate inpatient thromboprophylaxis requires venous thromboembolism (VTE) risk stratification; however, reliable risk de...

White Matter Hyperintensity Burden Modifies the Association Between Atrial Fibrillation and Cerebral Microbleeds

Background: In atrial fibrillation (AF), cerebral microbleed (CMB) burden guides anticoagulation decisions, yet AF is itself inconsistently associated...

Systems Pharmacology Reveals Type I Interferon and Myeloid-Like B Cell Reprogramming as Druggable Axes in Antiphospholipid Syndrome

Antiphospholipid syndrome (APS) lacks targeted therapies beyond anticoagulation, and its molecular heterogeneity remains poorly characterized. We empl...

AI-driven selection of patients with non-valvular atrial fibrillation for oral anticoagulation therapy: a multi-cohort validation and impact evaluation study

Background: Current risk assessment tools for guiding direct oral anticoagulant (DOAC) therapy for patients with atrial fibrillation (AF) based on cli...

Agentic Trial Emulation to Learn Health System-specific Drug Effects At Scale

Objective: Electronic Health Record (EHR)-based trial emulation can support translation of randomized clinical trial (RCT) evidence into practice, yet...

ESUS-AI:a machine learning framework to estimate the most likely embolic source in embolic stroke of undetermined source

Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challenge in vascular neurology, as a substantial propor...

Bleeding risk assessment tools in patients with atrial fibrillation taking anticoagulants: a comparative review and clinical implications.

INTRODUCTION: Bleeding risk assessment plays a critical role in the anticoagulation management for atrial fibrillation (AF), to balance stroke prevent...

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) poses a substantial risk of morbidity and mortality,...

Novel AI Guided Non-Expert Compression Ultrasound DVT Diagnostic Pathway May Reduce Vascular Laboratory Venous Testing .

OBJECTIVE: Ultrasonography and D-dimer testing are established modalities for evaluating potential lower extremity deep venous thrombosis (DVT). The T...

Machine Learning for Predicting Thrombotic Recurrence in Antiphospholipid Syndrome

Thrombotic Antiphospholipid Syndrome (TAPS) is an autoimmune disorder associated with a high risk of recurrent thromboembolic events. Despite advances...

Emulating Clinical Trials with the Mayo Clinic Platform: Cardiovascular Research Perspective

Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emula...

Optimal adherence thresholds for oral anticoagulants in patients with atrial fibrillation using machine learning and population administrative data

Adherence to oral anticoagulants (OACs) for atrial fibrillation (AF) stroke prevention is traditionally defined as taking 80% of doses as prescribed, ...

Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model

Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...