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Venous Thrombosis III: Interprofessional Care01:29

Venous Thrombosis III: Interprofessional Care

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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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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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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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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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Derivation and Validation of a Machine Learning Algorithm for Predicting Venous Thromboembolism in Injured Children.

Stephanie C Papillon1, Christopher P Pennell1, Sahal A Master1

  • 1St. Christopher's Hospital for Children, Department of Pediatric General Thoracic, and Minimally Invasive Surgery, Philadelphia, PA 19134, USA.

Journal of Pediatric Surgery
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict venous thromboembolism (VTE) risk in pediatric trauma patients. This tool can help guide VTE prophylaxis decisions in injured children.

Keywords:
Machine learningPediatric traumaTQIPVenous thromboembolism

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Area of Science:

  • Pediatric Trauma Care
  • Medical Informatics
  • Clinical Prediction Models

Background:

  • Venous thromboembolism (VTE) is a significant cause of morbidity in pediatric trauma patients.
  • Accurate risk stratification is crucial for appropriate VTE prophylaxis in this population.

Purpose of the Study:

  • To develop and validate a machine learning-based risk prediction model for VTE in injured children.
  • To identify key predictors of VTE in pediatric trauma patients.

Main Methods:

  • Utilized the Trauma Quality Improvement Program (TQIP) database (2017-2019) including 383,814 patients ≤18 years.
  • Identified 15 predictors including intubation, oxygen requirement, spinal/pelvic/long bone fractures, major surgery, age, transfusion, ICP monitor, and Glasgow Coma Scale score.
  • Trained and tested machine learning algorithms on split data subsets (training: 251,409; testing: 118,175).

Main Results:

  • All developed models significantly outperformed the baseline VTE rate (0.15%) in the testing subset.
  • Predicted VTE rates were low (0.01-0.02%), with 88.4-89.4% of patients classified as low risk.
  • High-risk prediction models also outperformed baseline, with no significant difference in performance among the three models tested.

Conclusions:

  • A validated predictive model effectively differentiates injured children at risk for VTE.
  • The model demonstrates high discrimination and can inform clinical decisions regarding VTE prophylaxis.