Prediction Model For Bleeding Research Articles

External validation of the BEST-J score and a new risk prediction model for ESD delayed bleeding in patients with early gastric cancer

BackgroundDelayed bleeding is an important adverse event after gastric endoscopic submucosal dissection (ESD). We aimed to externally validate the Bleeding after ESD Trend from Japan (BEST-J) score and subsequently develop a risk prediction model for bleeding in Chinese patients with early gastric cancer (EGC) after ESD.MethodsThe clinical data of patients who underwent ESD for EGC in Beijing Friendship Hospital from June 2013 to December 2019 were collected retrospectively. The BEST-J score was evaluated according to the clinical data. Through univariate and multivariate logistic regression analyses of the clinical data, the factors affecting delayed bleeding were identified, and a new risk prediction model for bleeding was established. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the two prediction models.ResultsA total of 444 patients with EGC undergoing ESD were included, of whom 27 patients had delayed bleeding (6.1%). Multivariate logistic regression analysis showed that a history of smoking (P = 0.029), tumor size > 20 mm (P = 0.022), intraoperative use of hemoclips (P = 0.025), resection of multiple tumors (P = 0.027), and prolongation of activated partial thromboplastin time (APTT) (P = 0.020) were independent influencing factors for delayed bleeding. ROC curve analysis showed that the areas under the curves (AUCs) were different between the BEST-J score and the newly built prediction model (0.624 vs. 0.749, P = 0.012).ConclusionsThe BEST-J score has moderately good discrimination for Chinese patients with EGC. However, for patients with EGC without severe comorbidities, the new risk prediction model may predict delayed bleeding better than the BEST-J score.

Validation of the BEST-J score, a prediction model for bleeding after endoscopic submucosal dissection for early gastric cancer: a multicenter retrospective observational study.

A new scoring system, the BEST-J score, using ten risk factors to assign cases to different post-endoscopic submucosal dissection (ESD) risk groups for bleeding, has been shown to be accurate for risk stratification. We first aimed to validate the BEST-J score at four hospitals not specialized in performing ESD and then aimed to identify other risk factors for post-ESD bleeding. We evaluated the incidence of post-ESD bleeding in 791 cases of early gastric cancer (EGC) between October 2013 and December 2020 as a retrospective, multi-center observational study conducted at four hospitals. Multivariate logistic regression models to examine the effect of independent variables on post-ESD bleeding firstly included ten possible factors raised by the BEST-J score and secondly included statistically significant (p < 0.01) in univariate analysis. The prediction accuracy of the model was evaluated by receiver-operating characteristic analysis and the areas under the curve (AUC). The incidence of post-ESD bleeding was 4.8% (38/791, 95% confidence interval [CI] 3.4-6.5%). On multivariate analysis, the risk factors were P2Y12 receptor antagonist (odds ratio [OR]: 5.870, 95% CI 1.624-21.219), warfarin (8.382, 1.658-42.322), direct oral anticoagulant (DOAC) (8.980, 1.603-50.322), and tumor location in lower third of stomach (2.151, 1.012-4.571), respectively. When we categorized cases into low-risk by BEST-J score, intermediate-risk, high-risk, and very high-risk groups, the bleeding rates were 2.8%, 7.3%, 12.8%, and 19.0%, respectively. The AUC for our cohort was 0.713 (95% CI 0.625-0.802) for the BEST-J score. In the multivariate analysis in our cohort, the risks were age, body mass index, P2Y12 receptor antagonist, warfarin, DOAC, respectively. The BEST-J score is equally accurate in risk stratification of patients with EGC for post-ESD bleeding at non-specialized facilities for ESD as in specialized hospitals. BMI and age may be helpful additional risk factors at hospitals not specialized.

Prediction model for bleeding after endoscopic submucosal dissection of gastric neoplasms from a high-volume center.

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. 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. 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 ≥49mm, and patient age <62years. The CART model accuracy was 94.9%, and the cross-validation accuracy was 94.8%. 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.