Browsing by Author "Moshiree, Baha"

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    American Neurogastroenterology and Motility Society (ANMS) Task Force Recommendations for Resumption of Motility Laboratory Operations During the COVID-19 Pandemic
    (American Neurogastro­enterology and Motility Society (ANMS), 2020-05-17) Baker, Jason R.; Moshiree, Baha; Rao, Satish; Neshatian, Leila; Nguyen, Linda; Chey, William D.; Saad, Richard; Garza, Jose; Waseem, Shamaila; Khan, Abraham R.; Pandolfino, John E.; Gyawali, C. Prakash; Department of Pediatrics, IU School of Medicine
    The ANMS organized a Task Force for developing guidance strategies regarding re-opening of motility laboratories. This document describes how to stratify urgency of motility physiologic procedures, screen prior to the procedures, optimize personal protective equipment (PPE) utilization, clean and prepare the motility laboratory space during the COVID-19 pandemic.
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    Predicting Response to Neuromodulators or Prokinetics in Patients With Suspected Gastroparesis Using Machine Learning: The "BMI, Infectious Prodrome, Delayed GES, and No Diabetes" Model
    (Wolters Kluwer, 2024-09-01) Takakura, Will; Surjanhata, Brian; Nguyen, Linda Anh Bui; Parkman, Henry P.; Rao, Satish S. C.; McCallum, Richard W.; Schulman, Michael; Wo, John Man-Ho; Sarosiek, Irene; Moshiree, Baha; Kuo, Braden; Hasler, William L.; Lee, Allen A.; Medicine, School of Medicine
    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. Methods: Subjects with suspected GP underwent simultaneous gastric emptying scintigraphy (GES) and wireless motility capsule and were followed for 6 months. Subjects were included if they were started on neuromodulators and/or prokinetics. Subjects were considered responders if their GP Cardinal Symptom Index at 6 months decreased by ≥1 from baseline. A machine learning model was trained using lasso regression, ridge regression, or random forest. Five-fold cross-validation was used to train the models, and the area under the receiver operator characteristic curve (AUC-ROC) was calculated using the test set. Results: Of the 150 patients enrolled, 123 patients received either a prokinetic and/or a neuromodulator. Of the 123, 45 were considered responders and 78 were nonresponders. A ridge regression model with the variables, such as body mass index, infectious prodrome, delayed gastric emptying scintigraphy, no diabetes, had the highest AUC-ROC of 0.72. The model performed well for subjects on prokinetics without neuromodulators (AUC-ROC of 0.83) but poorly for those on neuromodulators without prokinetics. A separate model with gastric emptying time, duodenal motility index, no diabetes, and functional dyspepsia performed better (AUC-ROC of 0.75). Discussion: This machine learning model has an acceptable accuracy in predicting those who will respond to neuromodulators and/or prokinetics. If validated, our model provides valuable data in predicting treatment outcomes in patients with GP-like symptoms.
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    Response to Richter and Vaezi
    (Wolters Kluwer, 2021-01) Gyawali, C. Prakash; Baker, Jason R.; Moshiree, Baha; Rao, Satish; Neshatian, Leila; Nguyen, Linda; Chey, William D.; Saad, Richard; Garza, Jose M.; Waseem, Shamaila; Khan, Abraham R.; Pandolfino, John E.; Pediatrics, School of Medicine