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Prediction model for bleeding after endoscopic submucosal dissection of gastric neoplasms from a high‐volume center
Author(s) -
Choe Yeon Hwa,
Jung Da Hyun,
Park Jun Chul,
Kim Ha Yan,
Shin Sung Kwan,
Lee Sang Kil,
Lee Yong Chan
Publication year - 2021
Publication title -
journal of gastroenterology and hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.214
H-Index - 130
eISSN - 1440-1746
pISSN - 0815-9319
DOI - 10.1111/jgh.15478
Subject(s) - medicine , cart , logistic regression , endoscopic submucosal dissection , cohort , decision tree , surgery , proportional hazards model , artificial intelligence , mechanical engineering , computer science , engineering
Background and Aim Bleeding after endoscopic submucosal dissection (ESD) is a main adverse event. To date, although there have been several studies about risk factors for post‐ESD bleeding, there has been few predictive model for post‐ESD bleeding with large volume cases. We aimed to design a prediction model for post‐ESD bleeding using a classification tree model. Methods We analyzed a prospectively established cohort of patients with gastric neoplasms treated with ESD from 2007 to 2016. Baseline characteristics were collected for a total of 5080 patients, and the bleeding risk was estimated using variable statistical methods such as logistic regression, AdaBoost, and random forest. To investigate how bleeding was affected by independent predictors, the classification and regression tree (CART) method was used. The prediction tree developed for the cohort was internally validated. Results Post‐ESD bleeding occurred in 262 of 5080 patients (5.1%). In multivariate logistic regression, ongoing antithrombotic use during the procedure, cancer pathology, and piecemeal resection were significant risk factors for post‐ESD bleeding. In the CART model, the decisive variables were ongoing antithrombotic agent use, resected specimen size ≥49 mm, and patient age <62 years. The CART model accuracy was 94.9%, and the cross‐validation accuracy was 94.8%. Conclusions We developed a simple and easy‐to‐apply predictive tree model based on three risk factors that could help endoscopists identify patients at a high risk of bleeding. This model will enable clinicians to establish precise management strategies for patients at a high risk of bleeding and to prevent post‐ESD bleeding.