Validation of a Machine Learning Algorithm, EVendo, for Predicting Esophageal Varices in Hepatocellular Carcinoma.

Journal: Digestive diseases and sciences
PMID: 38896359

Abstract

BACKGROUND: Treatment with atezolizumab and bevacizumab has become standard of care for advanced unresectable hepatocellular carcinoma (HCC) but carries an increased gastrointestinal bleeding risk. Therefore, patients are often required to undergo esophagogastroduodenoscopy (EGD) to rule out esophageal varices (EV) prior to initiating therapy, which can delay care and lead to unnecessary procedural risks and health care costs. In 2019, the EVendo score was created and validated as a noninvasive tool to accurately screen out patients who were at low risk for having EV that required treatment. We sought to validate whether the EVendo score could be used to accurately predict the presence of EV and varices needing treatment (VNT) in patients with HCC.

Authors

  • Jamie O Yang
    UCLA Department of Medicine, Los Angeles, CA, USA.
  • Punya Chittajallu
    Greater Los Angeles Veterans Affairs Healthcare System, Los Angeles, USA.
  • Jihane N Benhammou
    UCLA Department of Medicine, Los Angeles, CA, USA.
  • Arpan Patel
    Greater Los Angeles Veterans Affairs Healthcare System, Los Angeles, USA.
  • Joseph R Pisegna
    Greater Los Angeles Veterans Affairs Healthcare System, Los Angeles, USA.
  • James Tabibian
    Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA David Geffen School of Medicine, Los Angeles, USA.
  • Tien S Dong
    The Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California.

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