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Related Concept Videos

Varicose Veins I: Introduction01:26

Varicose Veins I: Introduction

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Varicose veins, or varicosities, are abnormally dilated and twisted superficial veins caused by venous valve incompetence. This condition commonly affects the lower extremities, especially the saphenous veins, due to the higher pressure from prolonged standing and walking. However, varicosities can also occur in other areas, such as the esophagus, vulva, spermatic cords, and anorectal region.Etiology and typesPrimary varicose veins, often idiopathic, are more common in women due to inherent...
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Varicose Veins II: Diagnostic Studies and Interprofessional Care01:26

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Varicose veins, or varicosities, develop when the valves in the veins, which control blood flow, weaken or damage. It causes blood to pool and the veins to enlarge. Understanding the clinical manifestations, diagnostic approaches, and management options for varicose veins is crucial for effective treatment and relief.Clinical manifestationsClinical manifestations of varicose veins include a heavy, achy feeling or pain after prolonged standing or sitting. This discomfort can often be relieved by...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Veins are an integral part of our circulatory system, serving as the blood vessels that transport blood from all body regions to the heart. They are a network of hollow tubes that carry blood low in oxygen from the body's cells back to the heart for reoxygenation. Veins are crucial for maintaining the body's overall fluid balance and the continuous circulation of blood.
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Related Experiment Video

Updated: Feb 1, 2026

Synergizing Antegrade Endoscopic with Bridging Vein Harvesting for Improvement of Great Saphenous Vein Graft Quality from the Lower Leg
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Analysis of Clinical Improvement at 90 Days After Varicose Vein Surgery Using Machine Learning.

Amel Bakhouche1, Olivier Creton2, Fabien Lareyre3,4

  • 1Epione Team, INRIA Center, University Côte d'Azur, Sophia Antipolis, France.

Angiology
|January 31, 2026
PubMed
Summary

Machine learning models can predict clinical improvement after varicose vein surgery for chronic venous insufficiency (CVI). Key factors like baseline scores and BMI help identify patients at risk of poor outcomes.

Keywords:
artificial intelligencechronic venous insufficiencymachine learningvaricose vein

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Area of Science:

  • Vascular Surgery
  • Medical Informatics
  • Biostatistics

Background:

  • Predicting outcomes after chronic venous insufficiency (CVI) intervention remains challenging.
  • Varicose vein surgery is a common treatment for CVI, but patient response varies.
  • Identifying factors influencing surgical outcomes is crucial for patient management.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting 90-day clinical improvement after varicose vein surgery.
  • To identify key predictors associated with treatment outcomes in CVI patients.
  • To enhance the prediction of treatment success and identify individuals at higher risk of suboptimal results.

Main Methods:

  • Retrospective multicenter study of 4015 patients undergoing first-time varicose vein surgery (2014-2024).
  • Classification using Clinical-Etiologic-Anatomic-Pathophysiologic (CEAP) and Venous Clinical Severity Score (VCSS).
  • Training of Logistic Regression, Random Forest, and XGBoost models with cross-validation and undersampling.

Main Results:

  • 87.6% of patients achieved clinical improvement (decreased CEAP stage) at 90 days.
  • Random Forest model showed the best performance in predicting lack of improvement (accuracy 80%, recall 75%).
  • Predictors of poor outcomes included older age, higher BMI, and elevated baseline CEAP and VCSS scores.

Conclusions:

  • Machine learning models offer modest but valuable predictive performance for varicose vein surgery outcomes.
  • Models effectively highlight patients at risk of not improving post-surgery.
  • Baseline patient characteristics and disease severity scores are key predictors of treatment response.