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

Authors :
Ido Veisman
Amit Oppenheim
Ronny Maman
Nadav Kofman
Ilan Edri
Lior Dar
Eyal Klang
Sigal Sina
Daniel Gabriely
Idan Levy
Dmitry Beylin
Ortal Beylin
Efrat Shekel
Nir Horesh
Uri Kopylov
Source :
Journal of Clinical Medicine; Volume 11; Issue 19; Pages: 5893
Publication Year :
2022
Publisher :
Multidisciplinary Digital Publishing Institute, 2022.

Abstract

(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.

Details

Language :
English
ISSN :
20770383
Database :
OpenAIRE
Journal :
Journal of Clinical Medicine; Volume 11; Issue 19; Pages: 5893
Accession number :
edsair.doi.dedup.....db584106d4dd2dc2390ba7c5cd825b24
Full Text :
https://doi.org/10.3390/jcm11195893