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Updated: May 10, 2026

A New Hybrid Quantitative Evaluation Model for Axillary Junctional Hemorrhage in Swine
Published on: December 6, 2024
Bleeders, bleeding rates, and bleeding score
A Tosetto1, G Castaman, F Rodeghiero
1Department of Cell Therapy and Hematology, Hemophilia and Thrombosis Center, San Bortolo Hospital, Vicenza, Italy. tosetto@hemato.ven.it
The bleeding rate model unifies bleeding risk across various disorders, aiding differentiation between healthy individuals and those with mild bleeding disorders (MBDs). This framework could improve diagnostic tools for hemorrhagic conditions.
Area of Science:
- Hematology
- Clinical Medicine
- Medical Diagnostics
Background:
- Differentiating healthy individuals from those with mild bleeding disorders (MBDs) is clinically challenging due to overlapping bleeding symptoms.
- Existing frameworks struggle to unify bleeding risk across diverse congenital and acquired coagulopathies.
Purpose of the Study:
- To introduce and validate the concept of 'bleeding rate' as a unifying metric for bleeding risk.
- To demonstrate how bleeding rate can reconcile bleeding observations in various hemorrhagic conditions.
Main Methods:
- Analysis of incidence data from primary prevention trials and observational studies on hemorrhagic disorders.
- Comparison of non-major bleeding rates in healthy subjects, MBD patients (e.g., type 1 VWD), and patients on antiplatelet drugs.
- Comparison of bleeding rates in moderately severe bleeding disorders (e.g., type 2 VWD) with patients on vitamin K antagonists.
Main Results:
- The incidence of non-major bleeding in healthy subjects is approximately five per 100 person-years.
- Non-major bleeding rates in MBD patients (e.g., type 1 VWD) are comparable to those on antiplatelet therapy.
- Bleeding rates in more severe disorders (e.g., type 2 VWD) parallel those in patients taking vitamin K antagonists.
Conclusions:
- The bleeding rate model offers a unified framework to understand and quantify bleeding risk across different coagulopathies.
- This model explains common clinical observations and has the potential to guide the development of improved diagnostic and predictive tools for bleeding disorders.
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