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Related Concept Videos

Barrett Esophagus-I: Introduction01:21

Barrett Esophagus-I: Introduction

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Barrett's esophagus is a medical condition where the esophageal mucosa is significantly damaged by stomach acid or other digestive fluids, often due to long-term exposure associated with gastroesophageal reflux disease (GERD). In GERD, a weakened or abnormally relaxed lower esophageal sphincter allows stomach acid to flow persistently into the esophagus.
This constant acid exposure transforms the esophagus's pink mucosal lining (stratified squamous epithelium) into a type of lining more...
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Barrett Esophagus-II: Clinical Manifestations and Management01:21

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Individuals with Barrett's esophagus are often asymptomatic, but they may experience symptoms commonly associated with GERD, such as heartburn and acid regurgitation. Additional symptoms can include difficulty swallowing, chest pain, unintentional weight loss, blood in the stool (which may appear black, tarry, or bloody), and episodes of vomiting.
To diagnose Barrett's esophagus, healthcare providers often recommend an endoscopy for those showing symptoms of acid reflux. The procedure...
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Using machine learning to predict early readmission following esophagectomy.

Siavash Bolourani1, Mohammad A Tayebi2, Li Diao3

  • 1The Feinstein Institutes for Medical Research, Manhasset, NY; Elmezzi Graduate School of Molecular Medicine, Manhasset, NY; Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY; Department of Cardiovascular and Thoracic Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY.

The Journal of Thoracic and Cardiovascular Surgery
|July 27, 2020
PubMed
Summary

Machine learning models predict early readmission after esophagectomy. Key risk factors include COPD, malnutrition, and postoperative complications, guiding clinical decisions and quality improvement.

Keywords:
decesion treeesophagectomylogistic modelmachine learningprediction modelspyloromyotomy

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Area of Science:

  • Medical Informatics
  • Surgical Oncology
  • Machine Learning in Healthcare

Background:

  • Early readmission after esophagectomy is a significant concern, associated with increased severity and mortality.
  • Identifying predictive factors for early readmission is crucial for improving patient outcomes and healthcare resource allocation.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting 30-day readmission following esophagectomy.
  • To identify key risk factors associated with early readmission after esophagectomy.

Main Methods:

  • Utilized the National Readmission Database (NRD) to identify 2037 patients undergoing esophagectomy in 2016.
  • Applied standard statistical analyses and ML methodologies to identify risk factors and build predictive models.
  • Interpreted two distinct ML models: one for clinical decision-making and another for quality review.

Main Results:

  • Identified chronic obstructive pulmonary disease, malnutrition, prolonged intubation, pneumonia, acute kidney failure, and length of stay as significant risk factors for early readmission.
  • Cardiopulmonary complications, anastomotic leak, and sepsis/infection were primary reasons for readmission.
  • ML models achieved 71.7% sensitivity for clinical decision making and 84.8% accuracy with 98.7% specificity for quality review.

Conclusions:

  • Established ML-based prediction models for early readmission after esophagectomy.
  • ML techniques offer valuable tools for targeted patient support and standardization of quality measures in surgical care.
  • Risk factor identification aids in proactive management and prevention of early readmissions.