Fecal calprotectin: A novel predictor of ulcerated esophageal injury after atrial fibrillation catheter ablation.

BACKGROUND: Atrial esophageal fistula (AEF) is a lethal complication that can occur post atrial fibrillation (AF) ablation. Esophageal injury (EI) is likely to be the initial lesion leading to AEF. Endoscopic examination is the gold standard for a diagnosis of EI but extensive endoscopic screening is invasive and costly. This study was conducted to determine whether fecal calprotectin (Fcal), a marker of inflammation throughout the intestinal tract, may be associated with the existence of esophageal injury. METHODS: This diagnostic study was conducted in a cohort of 166 patients with symptomatic AF undergoing radiofrequency catheter ablation from May 2020 to June 2021. Fcal tests were performed 1-7 days after ablation. All patients underwent endoscopic ultrasonography 1 or 2 days after ablation. RESULTS: The levels of Fcal were significantly different between the EI and non-EI groups (404.9 µg/g (IQR 129.6-723.6) vs. 40.4 µg/g (IQR 15.0-246.2), p < .001). Analysis of ROC curves revealed that a Fcal level of 125 µg/g might be the optimal cut-off value for a diagnosis of EI, giving a 78.8% sensitivity and a 65.4% specificity. The negative predictive value of Fcal was 100% for ulcerated EI. CONCLUSIONS: The level of Fcal is associated with EI post AF catheter ablation. 125 µg/g might be the optimal cut-off value for a diagnosis of EI. Negative Fcal could predict the absence of ulcerated EI, which could be considered a precursor to AEF.

Real-Time Evaluation of Helicobacter pylori Infection by Convolution Neural Network During White-Light Endoscopy: A Prospective, Multicenter Study (With Video).

INTRODUCTION: Convolutional neural network during endoscopy may facilitate evaluation of Helicobacter pylori infection without obtaining gastric biopsies. The aim of the study was to evaluate the diagnosis accuracy of a computer-aided decision support system for H. pylori infection (CADSS-HP) based on convolutional neural network under white-light endoscopy. METHODS: Archived video recordings of upper endoscopy with white-light examinations performed at Sir Run Run Shaw Hospital (January 2019-September 2020) were used to develop CADSS-HP. Patients receiving endoscopy were prospectively enrolled (August 2021-August 2022) from 3 centers to calculate the diagnostic property. Accuracy of CADSS-HP for H. pylori infection was also compared with endoscopic impression, urea breath test (URT), and histopathology. H. pylori infection was defined by positive test on histopathology and/or URT. RESULTS: Video recordings of 599 patients who received endoscopy were used to develop CADSS-HP. Subsequently, 456 patients participated in the prospective evaluation including 189 (41.4%) with H. pylori infection. With a threshold of 0.5, CADSS-HP achieved an area under the curve of 0.95 (95% confidence interval [CI], 0.93-0.97) with sensitivity and specificity of 91.5% (95% CI 86.4%-94.9%) and 88.8% (95% CI 84.2%-92.2%), respectively. CADSS-HP demonstrated higher sensitivity (91.5% vs 78.3%; mean difference = 13.2%, 95% CI 5.7%-20.7%) and accuracy (89.9% vs 83.8%, mean difference = 6.1%, 95% CI 1.6%-10.7%) compared with endoscopic diagnosis by endoscopists. Sensitivity of CADSS-HP in diagnosing H. pylori was comparable with URT (91.5% vs 95.2%; mean difference = 3.7%, 95% CI -1.8% to 9.4%), better than histopathology (91.5% vs 82.0%; mean difference = 9.5%, 95% CI 2.3%-16.8%). DISCUSSION: CADSS-HP achieved high sensitivity in the diagnosis of H. pylori infection in the real-time test, outperforming endoscopic diagnosis by endoscopists and comparable with URT. Clinicaltrials.gov ; ChiCTR2000030724.

Different dosing regimens of Tenecteplase in acute ischemic stroke: A network meta-analysis of the clinical evidence.

Introduction: Acute ischemic stroke remains the major cause of death and disability and conclusive evidence of Tenecteplase in treating stroke is lacking. Objective: To conduct a meta-analysis to determine whether Tenecteplase produces better outcomes than Alteplase and a network meta-analysis comparing the different dosing regimens of Tenecteplase. Methods: Searches were made in MEDLINE, CENTRAL, and ClinicalTrials.gov. The outcome measures are recanalization, early neurological improvement, functional outcomes at 90 days (modified Rankin Scale 0-1 and 0-2), intracranial hemorrhage, symptomatic intracranial hemorrhage, and mortality within 90 days from treatment. Results: Fourteen studies are included in the meta-analyses and 18 studies in the network meta-analyses. In the meta-analysis, Tenecteplase 0.25 mg/kg has significant results in early neurological improvement (OR = 2.35, and 95% CI = 1.16-4.72) and excellent functional outcome (OR = 1.20, and 95% CI = 1.02-1.42). In the network meta-analysis, Tenecteplase 0.25 mg/kg produces significant results in early neurological improvement (OR = 1.52 [95% CI = 1.13-2.05], p-value = 0.01), functional outcomes (mRS 0-1 and 0-2) (OR = 1.19 [95% CI = 1.03-1.37], p-value = 0.02; OR = 1.21 [95% CI = 1.05-1.39], p-value = 0.01; respectively) and mortality (OR = 0.78 [95% CI = 0.64-0.96], p-value = 0.02) whereas Tenecteplase 0.40 mg/kg increases the chances of symptomatic intracranial hemorrhage (OR = 2.35 [95% CI = 1.19-4.64], p-value = 0.01). Conclusion: While not conclusive, our study lends evidence to 0.25 mg/kg Tenecteplase dose for ischemic stroke treatment. Further randomized trials need to be done to validate this finding. Registration: International prospective register of systematic reviews (PROSPERO) - CRD42022339774URL: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=339774.

Disease Burden of Total and Early-Onset Colorectal Cancer in China from 1990 to 2019 and Predictions of Cancer Incidence and Mortality.

Background: Understanding the temporal trends in the epidemiology of colorectal cancer (CRC) and early-onset CRC (EOCRC) in China is essential for policymakers to develop appropriate strategies to reduce the CRC burden. Methods: The prevalence, incidence, mortality, years of life lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life years (DALYs) of CRC were obtained from the Global Burden of Disease (GBD) Study 2019. The incidence and mortality of CRC over the next 25 years were predicted. Results: From 1990 to 2019, the prevalence, incidence, and mortality of total CRC and EOCRC significantly increased in males, with milder trends in females. In 2019, the number of people living with CRC (or EOCRC) in China was approximately 3.4 (0.59) million, which was over seven (five) times higher than that in 1990. The DALYs, YLDs, and YLLs moderately increased from 1990 to 2019 in both sexes. The age-standardized mortality rate (ASMR) for females has shown a stable trend in total CRC, and a downward trend in EOCRC since 2000. While the ASMR for males showed increasing trends in total CRC and EOCRC. In 2019, the highest incidence, prevalence, YLDs, YLLs, and DALYs were all observed in the 65 to 69 age group, while the highest mortality was in the 70 to 74. By 2044, the incidence and deaths of CRC are expected to reach 1310 thousand and 484 thousand, respectively. For EOCRC, the incidence will peak at about 101 thousand around 2034, and the mortality will continuously decrease to a nadir at about 18 thousand around 2044. Conclusion: Although the age-standardized incidence and mortality of total CRC and EOCRC in China will reach a plateau, the number of incident cases and deaths of CRC have been increasing in the last three decades and will continue to increase in the next 25 years.

An end-to-end tracking method for polyp detectors in colonoscopy videos.

Deep learning based computer-aided diagnosis technology demonstrates an encouraging performance in aspect of polyp lesion detection on reducing the miss rate of polyps during colonoscopies. However, to date, few studies have been conducted for tracking polyps that have been detected in colonoscopy videos, which is an essential and intuitive issue in clinical intelligent video analysis task (e.g. lesion counting, lesion retrieval, report generation). In the paradigm of conventional tracking-by-detection system, detection task for lesion localization is separated from the tracking task for cropped lesions re-identification. In the multi object tracking problem, each target is supposed to be tracked by invoking a tracker after the detector, which introduces multiple inferences and leads to external resource and time consumption. To tackle these problems, we proposed a plug-in module named instance tracking head (ITH) for synchronous polyp detection and tracking, which can be simply inserted into object detection frameworks. It embeds a feature-based polyp tracking procedure into the detector frameworks to achieve multi-task model training. ITH and detection head share the model backbone for low level feature extraction, and then low level feature flows into the separate branches for task-driven model training. For feature maps from the same receptive field, the region of interest head assigns these features to the detection head and the ITH, respectively, and outputs the object category, bounding box coordinates, and instance feature embedding simultaneously for each specific polyp target. We also proposed a method based on similarity metric learning. The method makes full use of the prior boxes in the object detector to provide richer and denser instance training pairs, to improve the performance of the model evaluation on the tracking task. Compared with advanced tracking-by-detection paradigm methods, detectors with proposed ITH can obtain comparative tracking performance but approximate 30% faster speed. Optimized model based on Scaled-YOLOv4 detector with ITH illustrates good trade-off between detection (mAP 91.70%) and tracking (MOTA 92.50% and Rank-1 Acc 88.31%) task at the frame rate of 66 FPS. The proposed structure demonstrates the potential to aid clinicians in real-time detection with online tracking or offline retargeting of polyp instances during colonoscopies.

Antiplatelets or anticoagulants? Secondary prevention in cervical artery dissection: an updated meta-analysis.

BACKGROUND: Extracranial artery dissection involving either internal carotid artery or vertebral artery is a major cause of stroke in adults under 50 years of age. There is no conclusive evidence whether antiplatelets or anticoagulants are better suited in the treatment of extracranial artery dissection. OBJECTIVES: To determine whether antiplatelets or anticoagulants have advantage over the other in the treatment of extracranial artery dissection for secondary prevention of recurrent ischemic events or death. METHODS: Present meta-analysis followed Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement. Database search was done in Medline, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to May 2021 using pre-defined search strategy. Additional studies were identified from reference lists from included studies, reviews and previous meta-analyses. Outcome measures were ischaemic stroke, ischaemic stroke or transient ischaemic attack (TIA), and death. RESULTS: Two RCTs and 64 observational studies were included in the meta-analysis. While the outcome measures of stroke, stroke or TIA and death were numerically higher with antiplatelet use, there were no statistically significant differences between antiplatelets and anticoagulants. CONCLUSION: We found no significant difference between antiplatelet and anticoagulation treatment after extracranial artery dissection. The choice of treatment should be tailored to individual cases.

Multi-label recognition of cancer-related lesions with clinical priors on white-light endoscopy.

Deep learning-based computer-aided diagnosis techniques have demonstrated encouraging performance in endoscopic lesion identification and detection, and have reduced the rate of missed and false detections of disease during endoscopy. However, the interpretability of the model-based results has not been adequately addressed by existing methods. This phenomenon is directly manifested by a significant bias in the representation of feature localization. Good recognition models experience severe feature localization errors, particularly for lesions with subtle morphological features, and such unsatisfactory performance hinders the clinical deployment of models. To effectively alleviate this problem, we proposed a solution to optimize the localization bias in feature representations of cancer-related recognition models that is difficult to accurately label and identify in clinical practice. Optimization was performed in the training phase of the model through the proposed data augmentation method and auxiliary loss function based on clinical priors. The data augmentation method, called partial jigsaw, can "break" the spatial structure of lesion-independent image blocks and enrich the data feature space to decouple the interference of background features on the space and focus on fine-grained lesion features. The annotation-based auxiliary loss function used class activation maps for sample distribution correction and led the model to present localization representation converging on the gold standard annotation of visualization maps. The results show that with the improvement of our method, the precision of model recognition reached an average of 92.79%, an F1-score of 92.61%, and accuracy of 95.56% based on a dataset constructed from 23 hospitals. In addition, we quantified the evaluation representation of visualization feature maps. The improved model yielded significant offset correction results for visualized feature maps compared with the baseline model. The average visualization-weighted positive coverage improved from 51.85% to 83.76%. The proposed approach did not change the deployment capability and inference speed of the original model and can be incorporated into any state-of-the-art neural network. It also shows the potential to provide more accurate localization inference results and assist in clinical examinations during endoscopies.

Simultaneous Recognition of Atrophic Gastritis and Intestinal Metaplasia on White Light Endoscopic Images Based on Convolutional Neural Networks: A Multicenter Study.

INTRODUCTION: Patients with atrophic gastritis (AG) or gastric intestinal metaplasia (GIM) have elevated risk of gastric adenocarcinoma. Endoscopic screening and surveillance have been implemented in high incidence countries. The study aimed to evaluate the accuracy of a deep convolutional neural network (CNN) for simultaneous recognition of AG and GIM. METHODS: Archived endoscopic white light images with corresponding gastric biopsies were collected from 14 hospitals located in different regions of China. Corresponding images by anatomic sites containing AG, GIM, and chronic non-AG were categorized using pathology reports. The participants were randomly assigned (8:1:1) to the training cohort for developing the CNN model (TResNet), the validation cohort for fine-tuning, and the test cohort for evaluating the diagnostic accuracy. The area under the curve (AUC), sensitivity, specificity, and accuracy with 95% confidence interval (CI) were calculated. RESULTS: A total of 7,037 endoscopic images from 2,741 participants were used to develop the CNN for recognition of AG and/or GIM. The AUC for recognizing AG was 0.98 (95% CI 0.97-0.99) with sensitivity, specificity, and accuracy of 96.2% (95% CI 94.2%-97.6%), 96.4% (95% CI 94.8%-97.9%), and 96.4% (95% CI 94.4%-97.8%), respectively. The AUC for recognizing GIM was 0.99 (95% CI 0.98-1.00) with sensitivity, specificity, and accuracy of 97.9% (95% CI 96.2%-98.9%), 97.5% (95% CI 95.8%-98.6%), and 97.6% (95% CI 95.8%-98.6%), respectively. DISCUSSION: CNN using endoscopic white light images achieved high diagnostic accuracy in recognizing AG and GIM.