Research Article | DOI: https://doi.org/-

Integrated Hemostatic Profiling and Predictive Modeling for Early Detection of Thrombosis in High-Risk Populations: A Multicenter Prospective Study

Integrated Hemostatic Profiling and Predictive Modeling for Early Detection of Thrombosis in High-Risk Populations: A Multicenter Prospective Study

  • Dr. Michael Andersson *

Department of Hematology, Karolinska Institute, Sweden

*Corresponding Author: Dr. Michael Andersson

Citation: M Andersson, Integrated Hemostatic Profiling and Predictive Modeling for Early Detection of Thrombosis in High-Risk Populations: A Multicenter Prospective Study, Journal of Cardiology and Cardiovascular Diseases, ACP Publishers, 1(1).

Copyright : © Michael Andersson, this is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Received: 07 September 2026 | Accepted: 12 September 2026 | Published: 18 September 2026

Keywords: Thrombosis, hemostasis, D-dimer, predictive modeling, vascular disease, biomarkers, machine learning

Dr. Integrated Hemostatic Profiling and Predictive Modeling for Early Detection of Thrombosis in High-Risk Populations: A Multicenter Prospective Study

Thrombosis remains a major contributor to global morbidity and mortality, often presenting without early clinical symptoms. This study aimed to evaluate the effectiveness of integrated hemostatic profiling combined with predictive modeling in identifying individuals at risk of thrombotic events. A prospective multicenter cohort study involving 1,050 participants with known risk factors was conducted over three years. Biomarkers including D-dimer, fibrinogen, platelet activity indices, and inflammatory markers were assessed alongside clinical variables. A machine learning–based model was developed to predict thrombotic risk. The model demonstrated superior predictive performance compared to conventional risk assessment methods, enabling early detection of thrombosis before clinical manifestation. These findings suggest that combining biological markers with computational approaches may significantly improve preventive strategies in thrombosis management

Keywords:
Thrombosis, hemostasis, D-dimer, predictive modeling, vascular disease, biomarkers, machine learning

References

1. Mackman N. Triggers. (2020). targets and treatments for thrombosis. Nature.
📘 View at Publisher 🎓 View at Google Scholar
1. Weitz JI, Fredenburgh JC. (2017). Factors influencing thrombosis. Blood.
📘 View at Publisher 🎓 View at Google Scholar
1.   Kearon C. (2016). Natural history of venous thromboembolism. Circulation.
📘 View at Publisher 🎓 View at Google Scholar
1. Di Nisio M et al. (2019). D-dimer testing in thrombosis. J Thromb Haemost.
📘 View at Publisher 🎓 View at Google Scholar
1.   Libby P. (2021). Inflammation and thrombosis. Arterioscler Thromb Vasc Biol.
📘 View at Publisher 🎓 View at Google Scholar
1. Furie B, Furie BC. (2008). Mechanisms of thrombus formation. N Engl J Med.
📘 View at Publisher 🎓 View at Google Scholar
1. Cohen AT et al. (2018). Venous thromboembolism risk assessment. Lancet.
📘 View at Publisher 🎓 View at Google Scholar
1. Kahn SR et al. (2016). Prevention of thrombosis. Chest.
📘 View at Publisher 🎓 View at Google Scholar
1. Attia ZI et al. (2019). Machine learning in medicine. Lancet.
📘 View at Publisher 🎓 View at Google Scholar
1. Topol EJ. (2020). AI in healthcare. Nat Med
📘 View at Publisher 🎓 View at Google Scholar
Share this
Submit Research Paper Join as Reviewer
Article Access
Download View
40 Views