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A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding

(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic interven...

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Autores principales: Veisman, Ido, Oppenheim, Amit, Maman, Ronny, Kofman, Nadav, Edri, Ilan, Dar, Lior, Klang, Eyal, Sina, Sigal, Gabriely, Daniel, Levy, Idan, Beylin, Dmitry, Beylin, Ortal, Shekel, Efrat, Horesh, Nir, Kopylov, Uri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573673/
https://www.ncbi.nlm.nih.gov/pubmed/36233760
http://dx.doi.org/10.3390/jcm11195893
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author Veisman, Ido
Oppenheim, Amit
Maman, Ronny
Kofman, Nadav
Edri, Ilan
Dar, Lior
Klang, Eyal
Sina, Sigal
Gabriely, Daniel
Levy, Idan
Beylin, Dmitry
Beylin, Ortal
Shekel, Efrat
Horesh, Nir
Kopylov, Uri
author_facet Veisman, Ido
Oppenheim, Amit
Maman, Ronny
Kofman, Nadav
Edri, Ilan
Dar, Lior
Klang, Eyal
Sina, Sigal
Gabriely, Daniel
Levy, Idan
Beylin, Dmitry
Beylin, Ortal
Shekel, Efrat
Horesh, Nir
Kopylov, Uri
author_sort Veisman, Ido
collection PubMed
description (1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012–2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the prediction performance of the Glasgow-Blatchford score (GBS) and the pre-endoscopic Rockall score for endoscopic intervention. We also trained and tested a new machine learning-based model for the prediction of endoscopic intervention. (3) Results: A total of 883 patients were included. Risk factors for endoscopic intervention included cirrhosis (9.0% vs. 3.8%, p = 0.01), syncope at presentation (19.3% vs. 5.4%, p < 0.01), early EGD (6.8 h vs. 17.0 h, p < 0.01), pre-endoscopic administration of tranexamic acid (TXA) (43.4% vs. 31.0%, p < 0.01) and erythromycin (17.2% vs. 5.6%, p < 0.01). Higher GBS (11 vs. 9, p < 0.01) and pre-endoscopy Rockall score (4.7 vs. 4.1, p < 0.01) were significantly associated with endoscopic intervention; however, the predictive performance of the scores was low (AUC of 0.54, and 0.56, respectively). A combined machine learning-developed model demonstrated improved predictive ability (AUC 0.68) using parameters not included in standard GBS. (4) Conclusions: The GBS and pre-endoscopic Rockall score performed poorly in endoscopic intervention prediction. An improved predictive tool has been proposed here. Further studies are needed to examine if predicting this important triaging decision can be further optimized.
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spelling pubmed-95736732022-10-17 A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding Veisman, Ido Oppenheim, Amit Maman, Ronny Kofman, Nadav Edri, Ilan Dar, Lior Klang, Eyal Sina, Sigal Gabriely, Daniel Levy, Idan Beylin, Dmitry Beylin, Ortal Shekel, Efrat Horesh, Nir Kopylov, Uri J Clin Med Article (1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012–2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the prediction performance of the Glasgow-Blatchford score (GBS) and the pre-endoscopic Rockall score for endoscopic intervention. We also trained and tested a new machine learning-based model for the prediction of endoscopic intervention. (3) Results: A total of 883 patients were included. Risk factors for endoscopic intervention included cirrhosis (9.0% vs. 3.8%, p = 0.01), syncope at presentation (19.3% vs. 5.4%, p < 0.01), early EGD (6.8 h vs. 17.0 h, p < 0.01), pre-endoscopic administration of tranexamic acid (TXA) (43.4% vs. 31.0%, p < 0.01) and erythromycin (17.2% vs. 5.6%, p < 0.01). Higher GBS (11 vs. 9, p < 0.01) and pre-endoscopy Rockall score (4.7 vs. 4.1, p < 0.01) were significantly associated with endoscopic intervention; however, the predictive performance of the scores was low (AUC of 0.54, and 0.56, respectively). A combined machine learning-developed model demonstrated improved predictive ability (AUC 0.68) using parameters not included in standard GBS. (4) Conclusions: The GBS and pre-endoscopic Rockall score performed poorly in endoscopic intervention prediction. An improved predictive tool has been proposed here. Further studies are needed to examine if predicting this important triaging decision can be further optimized. MDPI 2022-10-05 /pmc/articles/PMC9573673/ /pubmed/36233760 http://dx.doi.org/10.3390/jcm11195893 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Veisman, Ido
Oppenheim, Amit
Maman, Ronny
Kofman, Nadav
Edri, Ilan
Dar, Lior
Klang, Eyal
Sina, Sigal
Gabriely, Daniel
Levy, Idan
Beylin, Dmitry
Beylin, Ortal
Shekel, Efrat
Horesh, Nir
Kopylov, Uri
A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title_full A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title_fullStr A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title_full_unstemmed A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title_short A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding
title_sort novel prediction tool for endoscopic intervention in patients with acute upper gastro-intestinal bleeding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573673/
https://www.ncbi.nlm.nih.gov/pubmed/36233760
http://dx.doi.org/10.3390/jcm11195893
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