Modified BEST-J Score Model Predicts Bleeding after Endoscopic Submucosal Dissection with Fewer Factors
This study constructed a simplified post-endoscopic submucosal dissection (ESD) prediction model with a prognostic nutritional index (PNI). A total of 449 patients who underwent gastric ESD was included, divided with a ratio of 2:1, and assigned to the model or validation cohort. A prediction model...
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Published in | Cancers Vol. 14; no. 22; p. 5555 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
01.11.2022
MDPI |
Subjects | |
Online Access | Get full text |
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Summary: | This study constructed a simplified post-endoscopic submucosal dissection (ESD) prediction model with a prognostic nutritional index (PNI). A total of 449 patients who underwent gastric ESD was included, divided with a ratio of 2:1, and assigned to the model or validation cohort. A prediction model of post-ESD (modified BEST-J score) was constructed using the model cohort. The modified BEST-J score was evaluated by comparing its accuracy to the BEST-J score in the validation cohort. Within 4 weeks of ESD, melena, hematemesis, or a 2 g/dL or greater decrease in hemoglobin level that required esophagogastroduodenoscopy was defined as post-ESD bleeding. In the model cohort, 299 patients were enrolled and 25 (8.4%) had post-ESD bleeding. Independent risk factors for post-ESD bleeding were use of P2Y12RA, tumor size > 30 mm, location of lesion at lower one-third of the stomach, and PNI ≤ 47.9. Constructing the modified BEST-J score based on these variables, the sensitivity, specificity, and positive likelihood ratio were 73.9%, 78.1%, and 3.37. When comparing the modified BEST-J score to the BEST-J score in the validation cohort, no significant difference was observed by ROC-AUC (0.77 vs. 0.75, p = 0.81). Modified BEST-J score can predict post-ESD bleeding more simply, with the same accuracy as the BEST-J score. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 2072-6694 2072-6694 |
DOI: | 10.3390/cancers14225555 |