Low Prevalence of Endoscopic Screening for Barrett's Esophagus in a Screening-Eligible Primary Care Population

Measuring and improving quality in esophageal care and swallowing disorders

Evaluating clinical care through quality-related metrics is increasingly common. There are now numerous quality statements and indicators related to the medical management of benign and pre-malignant esophageal diseases. Expert consensus leveraging evidence-based recommendations from published society guidelines has been the most frequently used basis for developing esophageal quality statements. While surgical care of patients with esophageal malignancies, including squamous cell carcinoma, has also been developed, those related to benign esophageal disease now include domains of diagnosis, treatment, and monitoring for gastroesophageal reflux disease, eosinophilic esophagitis (EoE), achalasia, and Barrett's esophagus (BE). Several recent studies evaluating adherence to quality metrics affirm substantial variation in practice patterns with opportunities for improvement in care across esophageal diseases. In particular, patient education regarding treatment options in achalasia, frequency of esophageal biopsies among patients with dysphagia to evaluate for EoE, and endoscopic evaluation within a BE segment are areas identified to have need for improvement. As the management of esophageal diseases becomes more complex and interdisciplinary, adherence to quality metrics may be a source of standardization and improvement in delivery and ultimately patient outcomes. Indeed, the development of national quality databases has resulted in a significant growth in the use of these metrics for quality improvement activities and may form the basis for future inclusion in quality reporting and payment programs.

Significant Reduction in the Diagnosis of Barrett's Esophagus and Related Dysplasia During the COVID-19 Pandemic

INTRODUCTION

The coronavirus disease 19 (COVID-19) pandemic disrupted endoscopy practices, creating unprecedented decreases in cancer screening and surveillance services. We aimed to assess the impact of the pandemic on the proportion of patients diagnosed with Barrett's esophagus (BE) and BE-related dysplasia and adherence to established quality indicators.

METHODS

Data from all esophagogastroduodenoscopies in the GI Quality Improvement Consortium, a national repository of matched endoscopy and pathology data, were analyzed from January 2018 to December 2022. Four cohorts were created based on procedure date and COVID-19 data: pre-pandemic (January 2018 to February 2020), pandemic-phase I (March 2020 to July 2020), pandemic-phase II (August 2020 to May 2021), and pandemic-phase III (June 2021 to December 2022). Observed and expected number of BE and BE-related dysplasia cases per month and adherence to the Seattle biopsy protocol and recommended surveillance intervals for nondysplastic BE (NDBE) were evaluated.

RESULTS

Among 2,446,857 esophagogastroduodenoscopies performed during the study period, 104,124 (4.3%) had pathology-confirmed BE. The histologic distribution was 87.4% NDBE, 1.8% low-grade dysplasia, 2.4% indefinite for dysplasia, and 1.4% high-grade dysplasia. The number of monthly BE (-47.9% pandemic-phase I, -21.5% pandemic-phase II, and -19.0% pandemic-phase III) and BE-related dysplasia (high-grade dysplasia: 41.2%, -27.7%, and -19.0%; low-grade dysplasia: 49.1%, -35.3%, and -26.5%; any dysplasia: 46.7%, -32.3%, and -27.9%) diagnoses were significantly reduced during the pandemic phases compared with pre-pandemic data. Adherence rates to the Seattle protocol and recommended surveillance intervals for NDBE did not decline during the pandemic.

DISCUSSION

There was a significant decline in the number of BE and BE-related dysplasia diagnoses during the COVID-19 pandemic, with an approximately 50% reduction in the number of cases of dysplasia diagnosed in the early pandemic. The absence of a compensatory increase in diagnoses in the pandemic-phase II and III periods may result in deleterious downstream effects on esophageal adenocarcinoma morbidity and mortality.

The 2022 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board

Using a systematic literature search of original articles published during 2022 in Gastrointestinal Endoscopy and other high-impact medical and gastroenterology journals, the 10-member Editorial Board of the American Society for Gastrointestinal Endoscopy composed a list of the 10 most significant topic areas in GI endoscopy during the study year. Each Editorial Board member was directed to consider 3 criteria in generating candidate lists-significance, novelty, and global impact on clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting. The 10 identified areas collectively represent advances in the following endoscopic spheres: artificial intelligence, endoscopic submucosal dissection, Barrett's esophagus, interventional EUS, endoscopic resection techniques, pancreaticobiliary endoscopy, management of acute pancreatitis, endoscopic environmental sustainability, the NordICC trial, and spiral enteroscopy. Each board member was assigned a consensus topic area around which to summarize relevant important articles, thereby generating this précis of the "top 10" endoscopic advances of 2022.

Predicting Incident Adenocarcinoma of the Esophagus or Gastric Cardia Using Machine Learning of Electronic Health Records

BACKGROUND & AIMS

Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records to guide screening decisions are needed.

METHODS

The Veterans Health Administration (VHA) Corporate Data Warehouse was accessed to identify Veterans with 1 or more encounters between 2005 and 2018. Patients diagnosed with EAC (n = 8430) or GCA (n = 2965) were identified in the VHA Central Cancer Registry and compared with 10,256,887 controls. Predictors included demographic characteristics, prescriptions, laboratory results, and diagnoses between 1 and 5 years before the index date. The Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) tool was developed and internally validated using simple random sampling imputation and extreme gradient boosting, a machine learning method. Training was performed in 50% of the data, preliminary validation in 25% of the data, and final testing in 25% of the data.

RESULTS

K-ECAN was well-calibrated and had better discrimination (area under the receiver operating characteristic curve [AuROC], 0.77) than previously validated models, such as the Nord-Trøndelag Health Study (AuROC, 0.68) and Kunzmann model (AuROC, 0.64), or published guidelines. Using only data from between 3 and 5 years before index diminished its accuracy slightly (AuROC, 0.75). Undersampling men to simulate a non-VHA population, AUCs of the Nord-Trøndelag Health Study and Kunzmann model improved, but K-ECAN was still the most accurate (AuROC, 0.85). Although gastroesophageal reflux disease was strongly associated with EAC, it contributed only a small proportion of gain in information for prediction.

CONCLUSIONS

K-ECAN is a novel, internally validated tool predicting incident EAC and GCA using electronic health records data. Further work is needed to validate K-ECAN outside VHA and to assess how best to implement it within electronic health records.

Development of Electronic Health Record-Based Machine Learning Models to Predict Barrett's Esophagus and Esophageal Adenocarcinoma Risk

INTRODUCTION

Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database.

METHODS

The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models.

RESULTS

We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes.

DISCUSSION

ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

Missed opportunities to screen for Barrett's esophagus in the primary care setting of a large health system

BACKGROUND AND AIMS

The rate of esophageal adenocarcinoma (EAC) is rising. This is partly due to the lack of identification of Barrett's esophagus (BE), the main risk factor for EAC. Identifying neoplastic BE can allow for endoscopic therapy to prevent EAC. Our aim was to determine how many patients eligible for screening are actually being screened for BE in the primary care setting of a large health system.

METHODS

A digital search algorithm was constructed using the established gastroenterology guidelines and the Kunzmann model for screening for BE. The algorithm was then applied to the electronic medical record of all patients seen in the primary care setting of the health system. A manual review of charts of the identified patients was performed to confirm the high-risk status and determine if screening occurred.

RESULTS

Of 936,371 primary care charts analyzed by the algorithm, 3535 patients (.4%) were determined to be high-risk for BE. Of these 3535 patients, only 1077 (30%) were screened for BE in clinical practice with endoscopy. The algorithm identified 2458 (70%) additional high-risk patients. Of the patients screened in clinical practice, 105 (10%) were found to have BE (10% with neoplasia).

CONCLUSIONS

Numerous screening opportunities for BE are missed in the primary care setting of a large health system. Collaboration between gastroenterology and primary care services is needed to improve the screening rate.

New Techniques to Screen for Barrett Esophagus

Barrett esophagus (BE) is the only known precursor to esophageal adenocarcinoma (EAC), a cancer that continues to have a poor 5-year survival rate of 20%. Current BE screening strategies aim to detect BE and EAC at early, curable stages, but the majority of patients with EAC are diagnosed outside of BE screening and surveillance programs. Guidelines around the world suggest screening for BE in patients with gastroesophageal reflux disease (GERD) and additional demographic and clinical risk factors using high-definition white-light endoscopy (HDWLE). However, current strategies relying on HDWLE are problematic with high direct and indirect costs, procedural risks, and limitations in patient selection owing to the low sensitivity of GERD as a risk factor for detection of BE. In an effort to address these shortcomings, a variety of other screening strategies are under investigation, including risk prediction algorithms, noninvasive cell collection devices, and other new technologies to make screening more efficient and cost-effective. At this time, only cell collection devices have been integrated into professional guidelines, and clinical implementation of alternatives to endoscopy has lagged. In the future, screening may be personalized using a combination of different screening modalities. This article discusses the current state of BE screening and new approaches that may alter the future of screening.

Construction of the Interaction Network of Hub Genes in the Progression of Barrett's Esophagus to Esophageal Adenocarcinoma

Introduction

Esophageal adenocarcinoma (EAC) is one of the histologic types of esophageal cancer with a poor prognosis. The majority of EAC originate from Barrett's esophagus (BE). There are few studies focusing on the dynamic progression of BE to EAC.

Methods

R software was used to analyze differentially expressed genes (DEGs) based on RNA-seq data of 94 normal esophageal squamous epithelial (NE) tissues, 113 BE tissues and 147 EAC tissues. The overlapping genes of DEGs between BE and EAC were analyzed by Venn diagram tool. The hub genes were selected by Cytoscape software based on the protein-protein interaction network of the overlapping genes using STRING database. The functional analysis of hub genes was performed by R software and the protein expression was identified by immunohistochemistry.

Results

In the present study, we found a large degree of genetic similarity between BE and EAC, and further identified seven hub genes (including COL1A1, TGFBI, MMP1, COL4A1, NID2, MMP12, CXCL1) which were all progressively upregulated in the progression of NE-BE-EAC. We have preliminarily uncovered the probable molecular mechanisms of these hub genes in disease development and constructed the ceRNA regulatory network of hub genes. More importantly, we explored the possibility of hub genes as biomarkers in the disease progression of NE-BE-EAC. For example, TGFBI can be used as biomarkers to predict the prognosis of EAC patients. COL1A1, NID2 and COL4A1 can be used as biomarkers to predict the response to immune checkpoint blockade (ICB) therapy. We also constructed a disease progression risk model for NE-BE-EAC based on CXCL1, MMP1 and TGFBI. Finally, the results of drug sensitivity analysis based on hub genes showed that drugs such as PI3K inhibitor TGX221, bleomycin, PKC inhibitor Midostaurin, Bcr-Abl inhibitor Dasatinib, HSP90 inhibitor 17-AAG, and Docetaxel may be potential candidates to inhibit the progression of BE to EAC.

Conclusion

This study is based on a large number of clinical samples with high credibility, which is useful for revealing the probable carcinogenic mechanism of BE to EAC and developing new clinical treatment strategies.

Gastroesophageal Reflux Disease Is Not a Great Screening Criterion: Time to Move on to Other Strategies for Controlling the Burden of Esophageal Adenocarcinoma

ABSTRACT

Gastroesophageal reflux disease (GERD) is key in the pathogenesis of Barrett's esophagus and esophageal adenocarcinoma (EAC). Endoscopic screening of select individuals with GERD symptoms for Barrett's esophagus and EAC has been recommended, but the great majority of patients with EAC had never undergone prior screening, despite over a million esophagogastroduodenoscopies (EGDs) performed annually in the United States among individuals with GERD symptoms. This is likely due to a conflation among providers regarding diagnostic EGD in those with refractory symptoms and screening EGD. An alternative approach is needed that de-emphasizes GERD to avoid confusion and increase uptake of appropriate screening.