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Managements of Venous Thromboembolism

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Vascular Medicine".

Deadline for manuscript submissions: 29 December 2025 | Viewed by 85

Special Issue Editor


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Guest Editor
Venous Thromboembolism Unit, Internal Medicine Department, Hospital Universitario Infanta Leonor, Madrid, Spain
Interests: venous thromboembolism; deep vein thrombosis; pulmonary embolism; anticoagulation therapy; direct oral anticoagulants; risk stratification; bleeding risk; hematology; medical informatics; cardiac and cardiovascular; peripheral vascular diseases
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Special Issue Information

Dear Colleagues,

Venous thromboembolism (VTE), encompassing deep vein thrombosis and pulmonary embolism, remains a major cause of morbidity and mortality worldwide. Despite significant advances in diagnostic and therapeutic strategies, VTE continues to present major clinical challenges. In recent years, the introduction of direct oral anticoagulants (DOACs), the development of individualized risk assessment tools, and advances in imaging techniques have substantially reshaped the management landscape. Moreover, the application of artificial intelligence (AI) and machine learning (ML) algorithms is emerging as a promising approach to enhance prediction, diagnosis, and clinical decision making in VTE. Nevertheless, unresolved questions persist, including the optimal duration of anticoagulation, strategies for recurrence risk stratification, bleeding risk mitigation, and the management of special populations such as the elderly, pregnant individuals, or patients with thrombosis in unusual locations.

This Special Issue aims to provide an updated overview of current evidence and emerging research in the management of VTE, with a focus on advances in pharmacologic therapies, predictive models—including AI-driven tools—and personalized approaches. We welcome original research articles, clinical studies, systematic reviews, and expert commentaries addressing clinical decision making, biomarkers, prevention strategies, and novel therapeutic interventions. Contributions from multidisciplinary perspectives are strongly encouraged to foster a comprehensive understanding of this complex and evolving field.

Dr. Anabel Franco Moreno
Guest Editor

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Keywords

  • venous thromboembolism
  • deep vein thrombosis
  • pulmonary embolism
  • anticoagulation therapy
  • direct oral anticoagulants
  • risk stratification
  • bleeding risk
  • artificial intelligence
  • personalized medicine

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Published Papers (1 paper)

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Research

17 pages, 1076 KiB  
Article
A Digital Twin Strategy to Predict Thrombotic Recurrence in Antiphospholipid Syndrome Patients Treated with Direct Oral Anticoagulants vs. Vitamin K Antagonists Using Data from Real-World Populations
by Miguel Ángel Casado-Suela, Juan Torres-Macho, Aida Izquierdo-Martínez, Cristina Lucía Ancos-Aracil, Luis Ferreira-Burguillos, Elena Madroñal-Cerezo, Tamar Talaván-Zañón, Adela Castañeda-Mata, Luis Escobar-Curbelo, Ana Martínez de la Casa-Muñoz, Eva Ruiz-Navío, Ana Bustamante-Fermosel and Anabel Franco-Moreno
J. Clin. Med. 2025, 14(16), 5716; https://doi.org/10.3390/jcm14165716 - 12 Aug 2025
Abstract
Background/Objectives: The role of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in preventing recurrent thrombosis in patients with antiphospholipid syndrome (APS) remains uncertain. Using real-world data, we aimed to evaluate the effectiveness and internal validity of a digital twin (DT) approach [...] Read more.
Background/Objectives: The role of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in preventing recurrent thrombosis in patients with antiphospholipid syndrome (APS) remains uncertain. Using real-world data, we aimed to evaluate the effectiveness and internal validity of a digital twin (DT) approach for modeling thrombotic recurrence risk in APS patients treated with DOACs or VKAs. Methods: We conducted a multicenter observational study that included thrombotic APS patients treated with DOACs or VKAs. Clinical data were used to generate DT via conditional generative adversarial networks (CGANs), incorporating a directed acyclic graph (DAG) to preserve causal relationships. Validation metrics included absolute standardized mean differences (ASMD), mean ASMD (MASMD), and Spearman correlation matrices to assess structural fidelity. Treatment effects were estimated in a CGAN-conditioned cohort matched on key covariates. Results: Eighty-nine thrombotic APS patients were included: 70 (78.7%) received VKAs and 19 (21.3%) received DOACs. Thrombotic recurrences occurred in 5 DOAC patients (26.3%) and 17 AVK patients (24.3%). The CGAN-generated synthetic cohort closely mirrored the original data (MASMD = 0.073 ± 0.041), with 85.4% of pairwise correlations differing by <0.1 in absolute value. In the conditioned DT cohort, predicted recurrence was 24.2% for DOACs and 19.9% for VKAs. Recurrence risk increased with antibody burden, reaching 41.3% in triple-positive patients and 46.8% in those with index arterial thrombosis treated with DOACs. Conclusions: DT technology accurately replicated the clinical structure of APS patients, supporting its application for simulating counterfactual scenarios and estimating individualized treatment effects. Full article
(This article belongs to the Special Issue Managements of Venous Thromboembolism)
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