The Effect of Tegoprazan on the Treatment of Endoscopic Resection-Induced Artificial Ulcers: A Multicenter, Randomized, Active-Controlled Study.

Background/Aims: : Tegoprazan is a novel potassium-competitive acid blocker that has beneficial effects on acid-related disorders such as gastroesophageal reflux and peptic ulcer diseases. This study aimed to validate the effect of tegoprazan on endoscopic submucosal dissection (ESD)-induced artificial ulcers. Methods: : Patients from 16 centers in Korea who underwent ESD for gastric neoplasia were enrolled. After ESD, pantoprazole was administered intravenously for 48 hours. The patients were randomly allocated to either the tegoprazan or esomeprazole group. Tegoprazan 50 mg or esomeprazole 40 mg were administered for 4 weeks, after which gastroscopic evaluation was performed. If the artificial ulcer had not healed, the same dose of tegoprazan or esomeprazole was administered for an additional 4 weeks, and a gastroscopic evaluation was performed. Results: : One hundred sixty patients were enrolled in this study. The healing rates of artificial ulcers at 4 weeks were 30.3% (23/76) and 22.1% (15/68) in the tegoprazan and esomeprazole groups, respectively (p=0.006). At 8 weeks after ESD, the cumulative ulcer healing rates were 73.7% (56/76) and 77.9% (53/68) in the tegoprazan and esomeprazole groups, respectively (p=0.210). Delayed bleeding occurred in two patients in the tegoprazan group (2.6%) and in one patient in the esomeprazole group (1.5%). Other adverse events were negligible in both groups. Conclusions: : Tegoprazan showed similar effects on post-ESD artificial ulcer healing in comparison with esomeprazole.

Characteristics of microbiome-derived metabolomics according to the progression of alcoholic liver disease.

BACKGROUND AND AIM: The prevalence and severity of alcoholic liver disease (ALD) are increasing. The incidence of alcohol-related cirrhosis has risen up to 2.5%. This study aimed to identify novel metabolite mechanisms involved in the development of ALD in patients. The use of gut microbiome-derived metabolites is increasing in targeted therapies. Identifying metabolic compounds is challenging due to the complex patterns that have long-term effects on ALD. We investigated the specific metabolite signatures in ALD patients. METHODS: This study included 247 patients (heathy control, HC: n = 62, alcoholic fatty liver, AFL; n = 25, alcoholic hepatitis, AH; n = 80, and alcoholic cirrhosis, AC, n = 80) identified, and stool samples were collected. 16S rRNA sequencing and metabolomics were performed with MiSeq sequencer and liquid chromatography coupled to time-of-flight-mass spectrometry (LC-TOF-MS), respectively. The untargeted metabolites in AFL, AH, and AC samples were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Metabolic network classifiers were used to predict the pathway expression of the AFL, AH, and AC stages. RESULTS: The relative abundance of Proteobacteria was increased and the abundance of Bacteroides was decreased in ALD samples (p = 0.001) compared with that in HC samples. Fusobacteria levels were higher in AH samples (p = 0.0001) than in HC samples. Untargeted metabolomics was applied to quantitatively screen 103 metabolites from each stool sample. Indole-3-propionic acid levels are significantly lower in AH and AC (vs. HC, p = 0.001). Indole-3-lactic acid (ILA: p = 0.04) levels were increased in AC samples. AC group showed an increase in indole-3-lactic acid (vs. HC, p = 0.040) level. Compared with that in HC samples, the levels of short-chain fatty acids (SCFAs: acetic acid, butyric acid, propionic acid, iso-butyric acid, and iso-valeric acid) and bile acids (lithocholic acids) were significantly decreased in AC. The pathways of linoleic acid metabolism, indole compounds, histidine metabolism, fatty acid degradation, and glutamate metabolism were closely associated with ALD metabolism. CONCLUSIONS: This study identified that microbial metabolic dysbiosis is associated with ALD-related metabolic dysfunction. The SCFAs, bile acids, and indole compounds were depleted during ALD progression. CLINICAL TRIAL: Clinicaltrials.gov, number NCT04339725.

Efficacy comparison of 7- and 14-day P-CAB based bismuth-containing quadruple regimen with PPI based bismuth-containing quadruple regimen for Helicobacter pylori infection: rationale and design of an open-label, multicenter, randomized controlled trial.

BACKGROUND: Owing to its strong acid inhibition, potassium-competitive acid blocker (P-CAB) based regimens for Helicobacter pylori (H. pylori) eradication are expected to offer clinical advantages over proton pump inhibitor (PPI) based regimens. This study aims to compare the efficacy and adverse effects of a 7-day and a 14-day P-CAB-based bismuth-containing quadruple regimen (PC-BMT) with those of a 14-day PPI-based bismuth-containing quadruple regimen (P-BMT) in patients with high clarithromycin resistance. METHODS: This randomized multicenter controlled clinical trial will be performed at five teaching hospitals in Korea. Patients with H. pylori infection who are naive to treatment will be randomized into one of three regimens: 7-day or 14-day PC-BMT (tegoprazan 50 mg BID, bismuth subcitrate 300 mg QID, metronidazole 500 mg TID, and tetracycline 500 mg QID) or 14-day P-BMT. The eradication rate, treatment-related adverse events, and drug compliance will be evaluated and compared among the three groups. Antibiotic resistance testing by culture will be conducted during the trial, and these data will be used to interpret the results. A total of 366 patients will be randomized to receive 7-day PC-BMT (n = 122), 14-day PC-BMT (n = 122), or 14-day P-BMT (n = 122). The H. pylori eradication rates in the PC-BMT and P-BMT groups will be compared using intention-to-treat and per-protocol analyses. DISCUSSION: This study will demonstrate that the 7-day or 14-day PC-BMT is well tolerated and achieve similar eradication rates to those of 14-day P-BMT. Additionally, the 7-day PC-BMT will show fewer treatment-related adverse effects and higher drug compliance, owing to its reduced treatment duration. TRIAL REGISTRATION: Korean Clinical Research Information Service registry, KCT0007444. Registered on 28 June 2022, https://cris.nih.go.kr/cris/index/index.do .

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study.

BACKGROUND: Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis. OBJECTIVE: This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM. METHODS: A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model. RESULTS: The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing. CONCLUSIONS: The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

Is it enough to observe less than 2 cm sized gastric SET?

BACKGROUND AND AIMS: The recent surge in demand for screening endoscopy has led to an increased detection of gastric subepithelial tumors (SETs). According to current guideline, SETs less than 2 cm in size are recommended for periodic surveillance. In light of recent advancement in therapeutic endoscopy in resection of small SET, we analyzed the histopathological features and the effectiveness of endoscopic resection for these small SETs. METHODS: Retrospectively study was performed on 74 patients who underwent endoscopic resection of gastric small (≤ 2 cm) upper gastrointestinal tract SETs. The outcomes including histopathology and en bloc resection were analyzed. RESULTS: The mean SET size was 11.69 ± 5.11 mm. The mean procedure time was 81.26 ± 42.53 min. Of the 74 patients, 28 patients had leiomyomas, 26 had gastrointestinal stromal tumors (GISTs), 14 had ectopic pancreas, 4 had lipomas, and 2 had neuroendocrine tumors. Among those with GIST, two patients exhibited high-risk histology. All patients underwent successful and uneventful endoscopy. CONCLUSIONS: Endoscopic resection can be recommended even for the small gastric SETs. In our study, we found that SETs with a size of less than 2 cm have significant proportion of GISTs which harbor malignant transformation potential.

Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study.

BACKGROUND : Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support system (CDSS) for the automated detection and classification (diagnosis and invasion depth prediction) of gastric neoplasms in real-time endoscopy. METHODS : The same 5017 endoscopic images that were employed to establish previous models were used for the training data. The primary outcomes were: (i) the lesion detection rate for the detection model, and (ii) the lesion classification accuracy for the classification model. For performance validation of the lesion detection model, 2524 real-time procedures were tested in a randomized pilot study. Consecutive patients were allocated either to CDSS-assisted or conventional screening endoscopy. The lesion detection rate was compared between the groups. For performance validation of the lesion classification model, a prospective multicenter external test was conducted using 3976 novel images from five institutions. RESULTS : The lesion detection rate was 95.6 % (internal test). On performance validation, CDSS-assisted endoscopy showed a higher lesion detection rate than conventional screening endoscopy, although statistically not significant (2.0 % vs. 1.3 %; P = 0.21) (randomized study). The lesion classification rate was 89.7 % in the four-class classification (advanced gastric cancer, early gastric cancer, dysplasia, and non-neoplastic) and 89.2 % in the invasion depth prediction (mucosa confined or submucosa invaded; internal test). On performance validation, the CDSS reached 81.5 % accuracy in the four-class classification and 86.4 % accuracy in the binary classification (prospective multicenter external test). CONCLUSIONS : The CDSS demonstrated its potential for real-life clinical application and high performance in terms of lesion detection and classification of detected lesions in the stomach.

Comparison Trial between I-SCAN-Optical Enhancement and Chromoendoscopy for Evaluating the Horizontal Margins of Gastric Epithelial Neoplasms.

Background/Aims: Endoscopic submucosal dissection is a widely used treatment for gastric epithelial neoplasms. Accurate delineation of the horizontal margins is necessary for the complete resection of gastric epithelial neoplasms. Recently, image-enhanced endoscopy has been used to evaluate horizontal margins of gastric epithelial neoplasms. The aim of this study was to investigate whether I-SCAN-optical enhancement (I-SCAN-OE) is superior to chromoendoscopy in evaluating the horizontal margin of gastric epithelial neoplasms. Methods: This was a multicenter, prospective, and randomized trial. The participants were divided into two groups: I-SCAN-OE and chromoendoscopy. For both groups, we first evaluated the horizontal margins of early gastric cancer or high-grade dysplasia using white-light imaging, and then evaluated, the horizontal margins using I-SCAN-OE or chromoendoscopy. We devised a unique scoring method based on the pathological results obtained after endoscopic submucosal dissection to accurately evaluate the horizontal margins of gastric epithelial neoplasms. The delineation scores of both groups were compared, as were the ratios of positive/negative horizontal margins. Results: In total, 124 patients were evaluated for gastric epithelial neoplasms, of whom 112 were enrolled in the study. A total of 112 patients participated in the study, and 56 were assigned to each group (1:1). There was no statistically significant difference in the delineation scores between the groups (chromoendoscopy, 7.80±1.94; I-SCAN-OE, 8.23±2.24; p=0.342). Conclusions: I-SCAN-OE did not show superiority over chromoendoscopy in delineating horizontal margins of gastric epithelial neoplasms.

A Randomized, Double-Blind, Active-Control, Noninferiority, Multicenter, Phase 4 Study to Evaluate the Efficacy and Safety of Esomeprazole/Sodium Bicarbonate 20/800 mg in Patients with Nonerosive Gastroesophageal Reflux Disease.

Background/Aims: Efficacy of proton pump inhibitors is limited in patients with nonerosive reflux disease (NERD). The aim of this study was to comparatively evaluate the efficacy and safety of esomeprazole with sodium bicarbonate and esomeprazole alone. Methods: This was a multicenter, randomized, double-blind, active-controlled, noninferiority comparative study. A total of 379 patients with NERD were randomly allocated to receive either EsoduoⓇ (esomeprazole 20 mg with sodium bicarbonate 800 mg) or NexiumⓇ (esomeprazole 20 mg) once daily for 4 weeks from January 2019 to December 2019. The patients had a history of heartburn for at least 2 days in the week before randomization as well as in the last 3 months and no esophageal mucosal breaks on endoscopy. The primary endpoint was a complete cure of heartburn at week 4. The secondary and exploratory endpoints as well as the safety profiles were compared in the groups at weeks 2 and 4. Results: A total of 355 patients completed the study (180 in the EsoduoⓇ group and 175 in the NexiumⓇ group). The proportions of patients without heartburn in the entire 4th week of treatment were not different between the two groups (33.33% in the EsoduoⓇ group and 35% in the NexiumⓇ group, p=0.737). There were no significant differences in most of the secondary and exploratory endpoints as well as the safety profiles. Conclusions: EsoduoⓇ is as effective and safe as NexiumⓇ for managing typical symptoms in patients with NERD (ClinicalTrial.gov identifier: NCT03928470).

Use of Proton Pump Inhibitors and the Risk for the Development of Gastric Cancers: A Nationwide Population-Based Cohort Study Using Balanced Operational Definitions.

OBJECTIVES: Previous cohort studies using national claim data in Korea have shown conflicting results about the association between the use of proton pump inhibitors (PPIs) and the risk of gastric cancer. This may be due to differences in the inclusion criteria or index dates of each study. This study aims to evaluate the association between PPI use and the risk of gastric cancer using balanced operational definitions. DESIGN: A population-based cohort analysis was conducted using the Korean National Health Insurance Service database. Subjects who used PPIs or histamine-2 receptor antagonist (H2RA) for more than 60 days after Helicobacter pylori eradication were included. The study subjects were those who had never used H2RAs (PPI users) and controls were those who had never used PPIs (H2RA users). For comparison, the index dates of previous studies were adopted and analyzed. The subjects were followed until the development of gastric cancer, death, or study end. RESULTS: A total of 10,012 subjects were included after propensity score matching. During a median follow-up of 6.56 years, PPI was not associated with an increased risk of gastric cancer (Hazard ratio: 1.30, 95% confidence interval: 0.75-2.27). This was consistent if the cumulative daily dose was adjusted (90/120/180 days), or if the index date was changed to the first day of PPI prescription or the last day of Helicobacter pylori eradication. There was no significant difference in mortality between both groups. CONCLUSION: PPI use was not associated with an increased risk of gastric cancer.

Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images.

BACKGROUND: Establishment of an artificial intelligence model in gastrointestinal endoscopy has no standardized dataset. The optimal volume or class distribution of training datasets has not been evaluated. An artificial intelligence model was previously created by the authors to classify endoscopic images of colorectal polyps into four categories, including advanced colorectal cancer, early cancers/high-grade dysplasia, tubular adenoma, and nonneoplasm. The aim of this study was to evaluate the impact of the volume and distribution of training dataset classes in the development of deep-learning models for colorectal polyp histopathology prediction from endoscopic images. METHODS: The same 3828 endoscopic images that were used to create earlier models were used. An additional 6838 images were used to find the optimal volume and class distribution for a deep-learning model. Various amounts of data volume and class distributions were tried to establish deep-learning models. The training of deep-learning models uniformly used no-code platform Neuro-T. Accuracy was the primary outcome on four-class prediction. RESULTS: The highest internal-test classification accuracy in the original dataset, doubled dataset, and tripled dataset was commonly shown by doubling the proportion of data for fewer categories (2:2:1:1 for advanced colorectal cancer: early cancers/high-grade dysplasia: tubular adenoma: non-neoplasm). Doubling the proportion of data for fewer categories in the original dataset showed the highest accuracy (86.4%, 95% confidence interval: 85.0-97.8%) compared to that of the doubled or tripled dataset. The total required number of images in this performance was only 2418 images. Gradient-weighted class activation mapping confirmed that the part that the deep-learning model pays attention to coincides with the part that the endoscopist pays attention to. CONCLUSION: As a result of a data-volume-dependent performance plateau in the classification model of colonoscopy, a dataset that has been doubled or tripled is not always beneficial to training. Deep-learning models would be more accurate if the proportion of fewer category lesions was increased.

Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study.

BACKGROUND: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. OBJECTIVES: To establish a deep-learning model to diagnose esophageal cancers, precursor lesions, and non-neoplasms using endoscopic images. Additionally, a nationwide prospective multicenter performance verification was conducted to confirm the possibility of real-clinic application. METHODS: A total of 5162 white-light endoscopic images were used for the training and internal test of the model classifying esophageal cancers, dysplasias, and non-neoplasms. A no-code deep-learning tool was used for the establishment of the deep-learning model. Prospective multicenter external tests using 836 novel images from five hospitals were conducted. The primary performance metric was the external-test accuracy. An attention map was generated and analyzed to gain the explainability. RESULTS: The established model reached 95.6% (95% confidence interval: 94.2-97.0%) internal-test accuracy (precision: 78.0%, recall: 93.9%, F1 score: 85.2%). Regarding the external tests, the accuracy ranged from 90.0% to 95.8% (overall accuracy: 93.9%). There was no statistical difference in the number of correctly identified the region of interest for the external tests between the expert endoscopist and the established model using attention map analysis (P = 0.11). In terms of the dysplasia subgroup, the number of correctly identified regions of interest was higher in the deep-learning model than in the endoscopist group, although statistically insignificant (P = 0.48). CONCLUSIONS: We established a deep-learning model that accurately classifies esophageal cancers, precursor lesions, and non-neoplasms. This model confirmed the potential for generalizability through multicenter external tests and explainability through the attention map analysis.

Triple Therapy-Based on Tegoprazan, a New Potassium-Competitive Acid Blocker, for First-Line Treatment of Helicobacter pylori Infection: A Randomized, Double-Blind, Phase III, Clinical Trial.

Background/Aims: We examined the efficacy and safety of tegoprazan as a part of first-line triple therapy for Helicobacter pylori eradication. Methods: A randomized, double-blind, controlled, multicenter study was performed to evaluate whether tegoprazan (50 mg)-based triple therapy (TPZ) was noninferior to lansoprazole (30 mg)- based triple therapy (LPZ) (with amoxicillin 1 g and clarithromycin 500 mg; all administered twice daily for 7 days) for treating H. pylori. The primary endpoint was the H. pylori eradication rate. Subgroup analyses were performed according to the cytochrome P450 (CYP) 2C19 genotype, the minimum inhibitory concentration (MIC) of amoxicillin and clarithromycin, and underlying gastric diseases. Results: In total, 350 H. pylori-positive patients were randomly allocated to the TPZ or LPZ group. The H. pylori eradication rates in the TPZ and LPZ groups were 62.86% (110/175) and 60.57% (106/175) in an intention-to-treat analysis and 69.33% (104/150) and 67.33% (101/150) in a per-protocol analysis (non-inferiority test, p=0.009 and p=0.013), respectively. Subgroup analyses according to MICs or CYP2C19 did not show remarkable differences in eradication rate. Both first-line triple therapies were well-tolerated with no notable differences. Conclusions: TPZ is as effective as proton pump inhibitor-based triple therapy and is as safe as first-line H. pylori eradication therapy but does not overcome the clarithromycin resistance of H. pylori in Korea (ClinicalTrials.gov identifier NCT03317223).

No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification.

BACKGROUND: The authors previously developed deep-learning models for the prediction of colorectal polyp histology (advanced colorectal cancer, early cancer/high-grade dysplasia, tubular adenoma with or without low-grade dysplasia, or non-neoplasm) from endoscopic images. While the model achieved 67.3% internal-test accuracy and 79.2% external-test accuracy, model development was labour-intensive and required specialised programming expertise. Moreover, the 240-image external-test dataset included only three advanced and eight early cancers, so it was difficult to generalise model performance. These limitations may be mitigated by deep-learning models developed using no-code platforms. OBJECTIVE: To establish no-code platform-based deep-learning models for the prediction of colorectal polyp histology from white-light endoscopy images and compare their diagnostic performance with traditional models. METHODS: The same 3828 endoscopic images used to establish previous models were used to establish new models based on no-code platforms Neuro-T, VLAD, and Create ML-Image Classifier. A prospective multicentre validation study was then conducted using 3818 novel images. The primary outcome was the accuracy of four-category prediction. RESULTS: The model established using Neuro-T achieved the highest internal-test accuracy (75.3%, 95% confidence interval: 71.0-79.6%) and external-test accuracy (80.2%, 76.9-83.5%) but required the longest training time. In contrast, the model established using Create ML-Image Classifier required only 3 min for training and still achieved 72.7% (70.8-74.6%) external-test accuracy. Attention map analysis revealed that the imaging features used by the no-code deep-learning models were similar to those used by endoscopists during visual inspection. CONCLUSION: No-code deep-learning tools allow for the rapid development of models with high accuracy for predicting colorectal polyp histology.

Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.

BACKGROUND: Wireless capsule endoscopy allows the identification of small intestinal protruded lesions, such as polyps, tumors, or venous structures. However, reading wireless capsule endoscopy images or movies is time-consuming, and minute lesions are easy to miss. Computer-aided diagnosis (CAD) has been applied to improve the efficacy of the reading process of wireless capsule endoscopy images or movies. However, there are no studies that systematically determine the performance of CAD models in diagnosing gastrointestinal protruded lesions. OBJECTIVE: The aim of this study was to evaluate the diagnostic performance of CAD models for gastrointestinal protruded lesions using wireless capsule endoscopic images. METHODS: Core databases were searched for studies based on CAD models for the diagnosis of gastrointestinal protruded lesions using wireless capsule endoscopy, and data on diagnostic performance were presented. A systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Twelve studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of protruded lesions were 0.95 (95% confidence interval, 0.93-0.97), 0.89 (0.84-0.92), 0.91 (0.86-0.94), and 74 (43-126), respectively. Subgroup analyses showed robust results. Meta-regression found no source of heterogeneity. Publication bias was not detected. CONCLUSION: CAD models showed high performance for the optical diagnosis of gastrointestinal protruded lesions based on wireless capsule endoscopy.

Clinical Features and Long-term Prognosis of Crohn's Disease in Korea: Results from the Prospective CONNECT Study.

Background/Aims: The prospective Crohn's Disease Clinical Network and Cohort Study is a nationwide multicenter cohort study of patients with Crohn's disease (CD) in Korea, aiming to prospectively investigate the clinical features and long-term prognosis associated with CD. Methods: Patients diagnosed with CD between January 2009 and September 2019 were prospectively enrolled. They were divided into two cohorts according to the year of diagnosis: cohort 1 (diagnosed between 2009 and 2011) versus cohort 2 (between 2012 and 2019). Results: A total of 1,175 patients were included, and the median follow-up duration was 68 months (interquartile range, 39.0 to 91.0 months). The treatment-free durations for thiopurines (p<0.001) and anti-tumor necrosis factor agents (p=0.018) of cohort 2 were shorter than those of cohort 1. Among 887 patients with B1 behavior at diagnosis, 149 patients (16.8%) progressed to either B2 or B3 behavior during follow-up. Early use of thiopurine was associated with a reduced risk of behavioral progression (adjusted hazard ratio [aHR], 0.69; 95% confidence interval [CI], 0.50 to 0.90), and family history of inflammatory bowel disease was associated with an increased risk of behavioral progression (aHR, 2.29; 95% CI, 1.16 to 4.50). One hundred forty-one patients (12.0%) underwent intestinal resection, and the intestinal resection-free survival time was significantly longer in cohort 2 than in cohort 1 (p=0.003). The early use of thiopurines (aHR, 0.35; 95% CI, 0.23 to 0.51) was independently associated with a reduced risk of intestinal resection. Conclusions: The prognosis of CD in Korea appears to have improved over time, as evidenced by the decreasing intestinal resection rate. Early use of thiopurines was associated with an improved prognosis represented by a reduced risk of intestinal resection.

Bismuth-Based Quadruple Therapy versus Metronidazole-Intensified Triple Therapy as a First-Line Treatment for Clarithromycin-Resistant Helicobacter pylori Infection: A Multicenter Randomized Controlled Trial.

Background/Aims: Clarithromycin resistance is a main factor for treatment failure in the context of Helicobacter pylori infection. However, the treatment regimen for clarithromycin-resistant H. pylori infection has not yet been determined. We aimed to compare the efficacy and cost-effectiveness of 14-day bismuth-based quadruple therapy versus 14-day metronidazole-intensified triple therapy for clarithromycin-resistant H. pylori infection with genotypic resistance. Methods: This was a multicenter, randomized, controlled trial. A total of 782 patients with H. pylori infection examined using sequencing-based clarithromycin resistance point mutation tests were recruited between December 2018 and October 2020 in four institutions in Korea. Patients with significant point mutations (A2142G, A2142C, A2143G, A2143C, and A2144G) were randomly assigned to receive either 14-day bismuth-based quadruple therapy (n=102) or 14-day metronidazole-intensified triple therapy (n=99). Results: The overall genotypic clarithromycin resistance rate was 25.7% according to the sequencing method. The eradication rate of 14-day bismuth-based quadruple therapy was not significantly different in the intention-to-treat analysis (80.4% vs 69.7%, p=0.079), but was significantly higher than that of 14-day metronidazole-intensified triple therapy in the per-protocol analysis (95.1% vs 76.4%, p=0.001). There were no significant differences in the incidence of side effects. In addition, the 14-day bismuth-based quadruple therapy was more cost-effective than the 14-day metronidazole-intensified triple therapy. Conclusions: Fourteen-day bismuth-based quadruple therapy showed comparable efficacy with 14-day metronidazole-intensified triple therapy, and it was more cost-effective in the context of clarithromycin-resistant H. pylori infection.

Correction: Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.

[This corrects the article DOI: 10.2196/33267.].

Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.

BACKGROUND: Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. OBJECTIVE: To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS: We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95-.98), .93 (.89-.95), .92 (.89-.94), and 138 (79-243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98-.99), .96 (.94-0.97), .97 (.95-.99), and 888 (343-2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs erosions, hemorrhage vs angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CONCLUSIONS: CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy.

Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy.

BACKGROUND: Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. OBJECTIVE: This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. METHODS: Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS: Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. CONCLUSIONS: CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. TRIAL REGISTRATION: PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189.