Biopsy in emergency gastroscopy does not increase the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding combined with suspected malignant gastric ulcer: a multicenter retrospective cohort study

Dababneh

et al. 2022

Preprint

Background Acute upper gastrointestinal bleeding (UGIB) is a common cause of hospitalization, and esophagogastroduodenoscopy is the gold standard for diagnosis and therapy. Although cold forceps biopsies are considered a low bleeding risk, many defer gastric biopsies in the setting of UGIB. We aimed to conduct a study to assess the safety of gastric ulcer and random gastric biopsies in the setting of an overt UGIB. Methods This is a retrospective study conducted at a tertiary care center. All patients with a diagnosis of an UGIB who had an esophagogastroduodenoscopy performed with gastric ulcer or a random gastric biopsy were included. Primary outcomes were bleeding and mortality within 30 days. Secondary outcomes included procedure length, rate of H. pylori or cancer diagnosis, and patient follow-up rate. Results Our study included 254 UGIB patients, with only 171 undergoing biopsies. Patients with more severe bleeding using Glasgow-Blatchford Bleeding Score and Rockall Score were less likely to have a biopsy (p < 0.001). There was no statistically significant association between both groups in length of procedure and 30-day mortality (p = 0.482 and p = 0.201, respectively). Patients who underwent biopsies were less likely to be associated with 30-day rebleeding (p = 0.04). There was no difference in H. pylori incidence and cancer detection between the two groups (p = 0.32 and p = 1.0, respectively). Conclusions Gastric biopsies were found to be safe to obtain in the setting of an UGIB and did not increase the risk of 30-day rebleeding or 30-day mortality.

Section: Patient Variables In Uencing Outcomessupporting

confidence: 90%

The Safety of Gastric Ulcer Biopsies and Random Gastric Biopsies in the Setting of an Overt Upper Gastrointestinal Bleed

Dababneh

et al. 2022

Preprint

“…As endoscopic biopsy may not performed at the index endoscopy, follow-up endoscopy for these patients is essential because the treatment of benign or malignant gastric ulcers is entirely different [33]. Artificial intelligence systems are being studied for differentiating benign vs. malignant gastric ulcers [34][35][36] and early vs. advanced gastric cancer [35] or for improving endoscopic depth prediction of early cancer [37,38].…”

Section: Application Of Ai For Patient Care After a Bleeding Episodementioning

confidence: 99%

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

Yen

Chen

et al. 2021

JCM

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.

Therap Adv Gastroenterol

Section: Discussionmentioning

confidence: 99%

“…Matching was performed with a 1:1 matching protocol without replacement (nearest-matching algorithm), with a caliper width equal to 0.2 of the standard deviation of the logit of the propensity score. 21 Standardized differences were estimated for all of the baseline covariates before and after matching to assess the pre-match imbalance and post-match balance. Standardized differences of less than 0.1 for a given covariate indicate a relatively small imbalance.…”

Section: Methodsmentioning

confidence: 99%

U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case–control study

Zhao,

Jia,

Chi

2023

Self Cite

Background: The operative link for the gastritis assessment (OLGA) system can objectively reflect the stratification of gastric cancer risk in patients with chronic atrophic gastritis (CAG). Objectives: We developed a real-time video monitoring model for the endoscopic diagnosis of CAG and OLGA staging based on U-Net deep learning (DL). To further validate and improve its performance, we designed a study to evaluate the diagnostic evaluation indices. Design: A prospective nested case–control study Methods: Our cohort consisted of 1306 patients from 31 July 2021 to 31 January 2022. According to the pathological results, patients in the cohort were divided into the CAG group and the chronic non-atrophic gastritis group to evaluate the diagnostic evaluation indices. Each atrophy lesion was automatically labeled and the atrophy severity was assessed by the model. Propensity score matching was used to minimize selection bias. Results: The diagnostic evaluation indices and the consistency between OLGA staging and pathological diagnosis of the model were superior to those of endoscopists [sensitivity (89.31% versus 67.56%), specificity (90.46% versus 70.23%), positive predictive value (90.35% versus 69.41%), negative predictive value (89.43% versus 68.40%), accuracy rate (89.89% versus 68.89%), Youden index (79.77% versus 37.79%), odd product (79.23 versus 4.91), positive likelihood ratio (9.36 versus 2.27), negative likelihood ratio (0.12 versus 0.46)], areas under the curves (AUC) (95% CI) (0.919 (0.893–0.945) versus 0.749 (0.707–0.792), p < 0.001) and kappa (0.816 versus 0.291)]. Conclusion: Our study demonstrated that the DL model can assist endoscopists in real-time diagnosis of CAG during gastroscopy and synchronous identification of high-risk OLGA stage (OLGA stages III and IV) patients. Trial registration: ChiCTR2100044458.