Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model.

Journal: Clinical and translational gastroenterology
PMID: 39320959

Abstract

INTRODUCTION: Pharmacologic therapies for symptoms of gastroparesis (GP) have limited efficacy, and it is difficult to predict which patients will respond. In this study, we implemented a machine learning model to predict the response to prokinetics and/or neuromodulators in patients with GP-like symptoms.

Authors

  • Will Takakura
    Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA.
  • Brian Surjanhata
    Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Linda Anh Bui Nguyen
    Division of Gastroenterology, Stanford Medicine, Stanford, California, USA.
  • Henry P Parkman
    Division of Gastroenterology, Temple University Health System Inc, Philadelphia, Pennsylvania, USA.
  • Satish S C Rao
    Division of Gastroenterology, Augusta University, Augusta, Georgia, USA.
  • Richard W McCallum
    Division of Gastroenterology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA.
  • Michael Schulman
    Suncoast GI Associates LLC, Bradenton, Florida, USA.
  • John Man-Ho Wo
    Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
  • Irene Sarosiek
    Division of Gastroenterology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA.
  • Baha Moshiree
    Gastroenterology and Hepatology, Atrium Health, Charlotte, North Carolina, USA.
  • Braden Kuo
    Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • William L Hasler
    Division of Gastroenterology, Mayo Clinic, Scottsdale, Arizona, USA.
  • Allen A Lee
    Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA.

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