Endoscopic Cyanoacrylate Injection Treatment for Refractory High-risk Peptic Ulcer Bleeding by Conventional Endoscopic Therapy

Bleeding is a major and potentially life-threatening complication of peptic ulcer. Despite endoscopic hemostatic therapy advance, conventional endoscopic hemostatic modalities remain refractory for peptic ulcer bleeding with big size, fibrous base or in difficult-to-access anatomical locations. In this study, we attempted to evaluate the efficacy and safety of endoscopic cyanoacrylate injection treatment (ECIT) for refractory high-risk peptic ulcer bleeding by conventional endoscopic therapy. The patients with refractory high-risk peptic ulcer bleeding by conventional endoscopic therapy were carried out ECIT. The data were retrospectively collected. A total of 119 patients accepted ECIT. 74 patients (62.18%) obtained successful intravascular injection and perivascular injection was performed in 45 patients (37.82%). Immediate hemostatic rate for active bleeding achieved 90.91%. Rebleeding rate within 30 days was 12.07%. Overall successful hemostasis rate achieved 87.93%. Immediate hemostatic rate and overall successful hemostasis rate in intravascular injection patients were markedly superior over perivascular injection. Rebleeding rate in intravascular injection patients was markedly lower than that in perivascular injection patients. 11 patients complicated abdominal pain and no other complication occurred. In conclusion, ECIT, especial intravascular injection, was effective and safe, with high successful hemostasis rate for refractory high-risk peptic ulcer bleeding by conventional endoscopic therapy.

Development and Validation of a Model to Predict Rebleeding Within Three Days After Endoscopic Hemostasis for Peptic Ulcer Bleeding

Background: Peptic ulcer bleeding remains a typical medical emergency with significant morbidity and mortality. Peptic ulcer rebleeding often occurs within three days after emergency endoscopic hemostasis. Our study aims to develop a nomogram to predict rebleeding within three days after emergency endoscopic hemostasis for peptic ulcer bleedingMethods: We retrospectively reviewed the data of 386 patients with bleeding ulcers who underwent emergency endoscopic hemostasis between March 2014 and October 2018. The least absolute shrinkage and selection operator method were used to identified predictors. The model was displayed as a nomogram. Internal validation was carried out using bootstrapping. The model was evaluated using the calibration plot, decision-curve analyses and clinical impact curve. Results: Overall, 386 patients meeting the inclusion criteria were enrolled, with 48 patients developed rebleeding within three days after initial endoscopic hemostasis. Predictors contained in the nomogram included albumin, prothrombin time, shock, haematemesis/melena and Forrest classification. The model showed good discrimination and good calibration with a C-index of 0.854 (C-index: 0.830 via bootstrapping validation). Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable.Conclusion: This study presents a nomogram that incorporates clinical, laboratory, and endoscopic features, effectively predicting rebleeding within three days after emergency endoscopic hemostasis and identifying high-risk rebleeding patients with peptic ulcer bleeding.Trial registration: This clinical trial has been registered in the ClinicalTrials.gov (ID: NCT04895904) approved by the International Committee of Medical Journal Editors (ICMJE).

Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature

With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people’s lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists’ clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.

Performance Comparison of the Deep Learning and the Human Endoscopist for Bleeding Peptic Ulcer Disease

Purpose Management of peptic ulcer bleeding is clinically challenging. Accurate characterization of the bleeding during endoscopy is key for endoscopic therapy. This study aimed to assess whether a deep learning model can aid in the classification of bleeding peptic ulcer disease. Methods Endoscopic still images of patients (n = 1694) with peptic ulcer bleeding for the last 5 years were retrieved and reviewed. Overall, 2289 images were collected for deep learning model training, and 449 images were validated for the performance test. Two expert endoscopists classified the images into different classes based on their appearance. Four deep learning models, including Mobile Net V2, VGG16, Inception V4, and ResNet50, were proposed and pre-trained by ImageNet with the established convolutional neural network algorithm. A comparison of the endoscopists and trained deep learning model was performed to evaluate the model’s performance on a dataset of 449 testing images. Results The results first presented the performance comparisons of four deep learning models. The Mobile Net V2 presented the optimal performance of the proposal models. The Mobile Net V2 was chosen for further comparing the performance with the diagnostic results obtained by one senior and one novice endoscopists. The sensitivity and specificity were acceptable for the prediction of “normal” lesions in both 3-class and 4-class classifications. For the 3-class category, the sensitivity and specificity were 94.83% and 92.36%, respectively. For the 4-class category, the sensitivity and specificity were 95.40% and 92.70%, respectively. The interobserver agreement of the testing dataset of the model was moderate to substantial with the senior endoscopist. The accuracy of the determination of endoscopic therapy required and high-risk endoscopic therapy of the deep learning model was higher than that of the novice endoscopist. Conclusions In this study, the deep learning model performed better than inexperienced endoscopists. Further improvement of the model may aid in clinical decision-making during clinical practice, especially for trainee endoscopist.