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Venous thrombosis requires effective prevention and treatment strategies to improve patient outcomes and reduce potential complications.Prevention StrategiesHealthcare providers must prioritize preventing venous thromboembolism (VTE) for all adult patients upon admission. Interventions depend on bleeding and thrombosis risk, medical history, current medications, diagnoses, planned procedures, and patient preferences. Patients on bed rest should change positions every two hours and, if not...
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Venous Thrombosis II: Clinical Manifestations and Diagnostic Studies01:20

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The key difference between Superficial Vein Thrombosis (SVT) and Deep Vein Thrombosis (DVT) lies in their location and severity.Clinical ManifestationsSVT typically presents with localized pain, tenderness, and redness along the course of a superficial vein, often accompanied by a palpable, cord-like structure under the skin. This condition is usually less dangerous than DVT but can be uncomfortable and may lead to complications such as cellulitis or, rarely, a clot extension into the deep...
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Venous thrombosis, the most common disorder of the veins, involves the formation of a thrombus or blood clot associated with vein inflammation. It can be classified as either superficial vein thrombosis or deep vein thrombosis.Superficial Vein Thrombosis: This involves the formation of a thrombus in a superficial vein, usually the greater or lesser saphenous vein. Though less severe than deep vein thrombosis (DVT), SVT can lead to complications if untreated.Deep Vein Thrombosis (DVT): This...
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Nursing management begins with a thorough assessment of the patient's health history. Key factors include trauma to veins, peripherally inserted central catheters, varicose veins, recent pregnancy or childbirth, surgery, bacteremia, prolonged bed rest, atrial fibrillation, COPD, heart failure, cancer, coagulation disorders, myocardial infarction, spinal cord injury, stroke, prolonged travel, recent bone fractures, and dehydration. Review medication intake, particularly oral contraceptives,...
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Varicose Veins II: Diagnostic Studies and Interprofessional Care01:26

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Machine learning predicts cancer-associated deep vein thrombosis using clinically available variables.

Shuai Jin1, Dan Qin1, Bao-Sheng Liang2

  • 1Division of Medical & Surgical Nursing, School of Nursing, Peking University, Beijing, China.

International Journal of Medical Informatics
|March 17, 2022
PubMed
Summary

Machine learning models effectively predict cancer-associated deep vein thrombosis (DVT), outperforming the Khorana score. A user-friendly web calculator aids clinical decision-making for DVT risk assessment in cancer patients.

Keywords:
Decision makingDeep vein thrombosisMachine learningNeoplasmsRisk stratification

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Area of Science:

  • Oncology
  • Medical Informatics
  • Thrombosis Research

Background:

  • Deep vein thrombosis (DVT) is a significant complication in cancer patients.
  • Accurate risk prediction is crucial for timely intervention and management.
  • Existing risk assessment tools may have limitations in predicting cancer-associated DVT.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting cancer-associated DVT.
  • To compare the predictive performance of ML models against the established Khorana score (KS).
  • To provide a practical tool for clinical risk assessment.

Main Methods:

  • Retrospective analysis of 2100 cancer patients' data.
  • Application of five ML algorithms: LDA, LR, CT, RF, and SVM.
  • Validation using cross-validation and comparison with KS based on AUC, sensitivity, specificity, and accuracy.

Main Results:

  • Cancer-associated DVT incidence was 22.3%.
  • Linear Discriminant Analysis (LDA) and Logistic Regression (LR) models showed superior performance (AUC=0.773 and 0.772, respectively) compared to KS (AUC=0.642).
  • Key predictors included D-dimer level, age, Charlson Comorbidity Index, length of stay, and VTE history. A nomogram and web calculator were developed.

Conclusions:

  • ML models, particularly LDA and LR, demonstrate strong potential for predicting cancer-associated DVT.
  • The developed nomogram and web calculator offer a valuable tool for individualized risk assessment.
  • External validation through prospective studies is recommended to confirm the model's generalizability.