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A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
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Development and validation of deep vein thrombosis diagnostic model based on machine learning methods.

Bin Yan1,2,3, Haijun Guo4, Tianxi Hu1,2,3

  • 1Department of Clinical Laboratory Medicine, Nanyang Central Hospital, Nanyang, China.

Annals of Hematology
|September 26, 2025
PubMed
Summary

New research shows that combining D-Dimer testing with the von Willebrand factor (VWF: GPIbM) assay improves deep venous thrombosis (DVT) diagnosis, especially for patients with normal D-Dimer levels.

Keywords:
D-DimerDeep venous thrombosisVon willebrand factor

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Area of Science:

  • Medical Diagnostics
  • Hematology
  • Vascular Medicine

Background:

  • D-Dimer testing has limited diagnostic value for deep venous thrombosis (DVT).
  • Normal D-Dimer levels do not exclude DVT diagnosis.
  • Improved diagnostic markers are needed, particularly for patients with normal D-Dimer results.

Purpose of the Study:

  • To evaluate the diagnostic performance of a combined model using D-Dimer and a new marker.
  • To assess the utility of the von Willebrand factor Gain-of-function mutant GPIb binding assay (VWF: GPIbM) in DVT diagnosis.
  • To determine if the combined model improves DVT prediction in patients with normal D-Dimer levels.

Main Methods:

  • Collected demographic and laboratory data from Nanyang Central Hospital patients.
  • Employed multiple models to rank marker importance.
  • Utilized multivariate logistic regression to develop a DVT diagnostic model incorporating VWF: GPIbM.

Main Results:

  • The combined model demonstrated superior performance compared to D-Dimer alone.
  • VWF: GPIbM significantly enhanced the diagnostic capability of D-Dimer.
  • The combined model maintained good diagnostic accuracy in patients with normal D-Dimer levels.

Conclusions:

  • A combined diagnostic model, including VWF: GPIbM, offers improved performance over D-Dimer for DVT diagnosis.
  • This model is valuable for patients where clinical prediction rules are difficult to apply.
  • VWF: GPIbM shows promise as an assistive tool for future DVT diagnosis.