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Tumor necrosis factor-α (TNFα) antagonists have advanced the management of inflammatory bowel diseases patients leading to an improvement of patient's quality of life with the reduction of number of surgeries and hospitalizations. Despite these advances, many patients do not respond to the induction therapy (primary non-response-PNR) or lose response during the treatment (secondary loss of response-LOR). In this paper we will provide an overview of the definition, epidemiology and risk factors for PNR and LOR, as well as discuss the therapeutic options for managing LOR.

Although efficacy and effectiveness studies are both important when evaluating interventions, they serve distinct purposes and have different study designs. Unfortunately, the distinction between these two types of trials is often poorly understood. In this primer, we highlight several differences between these two types of trials including study design, patient populations, intervention design, data analysis, and result reporting.

There is a fine balance in the mutual relationship between the intestinal microbiota and its mammalian host. It is thought that disruptions in this fine balance contribute/account for the pathogenesis of many diseases. Recently, the significance of the relationship between gut microbiota and its mammalian host in the pathogenesis of obesity and the metabolic syndrome has been demonstrated. Emerging data has linked intestinal dysbiosis to several gastrointestinal diseases including inflammatory bowel disease, irritable bowel syndrome, nonalcoholic fatty liver disease, and gastrointestinal malignancy. This article is intended to review the role of gut microbiota maintenance/alterations of gut microbiota as a significant factor as a significant factor discriminating between health and common diseases. Based on current available data, the role of microbial manipulation in disease management remains to be further defined and a focus for further clinical investigation.

The incidence of Clostridium difficile infection (CDI) has been rising in hospitals, long-term care facilities, and within the community. Cases have been more severe with more complications, deaths, and higher healthcare-associated costs. With the emergence of a hypervirulent strain of C. difficile and the increasing prevalence of community-acquired CDI among healthy patients without traditional risk factors, the epidemiology of C. difficile has been evolving. This changing epidemiology requires a change in management. Taking into account new risk factors for CDI and growing subpopulations of affected individuals, diagnostic, treatment, and prevention approaches need to be adjusted.

Prediction research is becoming increasing popular; however, the differences between traditional explanatory research and prediction research are often poorly understood, resulting in a wide variation in the methodologic quality of prediction research. This primer describes the basic methods for conducting prediction research in gastroenterology and highlights differences between traditional explanatory research and predictive research.

Grounded in intimate moments of family life in and out of hospitals, this book explores the hope that inspires us to try to create lives worth living, even when no cure is in sight. The Paradox of Hope focuses on a group of African American families in a multicultural urban environment, many of them poor and all of them with children who have been diagnosed with serious chronic medical conditions. Cheryl Mattingly proposes a narrative phenomenology of practice as she explores case stories in this highly readable study. Depicting the multicultural urban hospital as a border zone where race, class, and chronic disease intersect, this theoretically innovative study illuminates communities of care that span both clinic and family and shows how hope is created as an everyday reality amid trying circumstances.

Current guidelines recommend topical steroids as first-line treatment for patients with eosinophilic esophagitis (EoE). However, the evidence for this approach has been inconsistent in earlier reports. This meta-analysis aimed to clarify the efficacy of topical steroid treatment in active EoE using updated evidence. CENTRAL, MEDLINE and EMBASE databases were searched for randomized controlled trials (RCTs) published up to May 2014 that compared topical steroids with control treatments for active EoE. Study bias was assessed using the Cochrane Collaboration Tool, and outcomes were pooled using random effects models. The primary outcome was the mean change in eosinophil counts. Secondary outcomes were symptom responses and adverse events. In total, seven RCTs (226 patients) were included. Topical steroids were associated with a significant reduction in esophageal mucosal eosinophil counts compared with control therapy although substantial heterogeneity between studies was observed (weighted mean difference (WMD) -27.2, 95% confidence interval (CI) -45.3 to -9.1, I(2)=56.2%). Subgroup analysis indicated the reduction in eosinophil counts was only present in studies where a proton pump inhibitor (PPI) trial was used to exclude other diagnoses (WMD -46.3, 95% CI -61.3 to -31.4, I(2)=0.0%). Subdivision of studies on the use of a PPI trial also accounted for the majority of heterogeneity among RCTs. No clear trends in symptom resolution were observed. Eleven out of 127 patients who received topical steroids developed asymptomatic esophageal candidiasis. These data provide updated high-quality evidence that support current guidelines for first-line EoE treatment with topical steroids after an initial PPI trial to exclude non-EoE pathologies (PROSPERO ID: CRD42014008828).

Background The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods. Methods This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment. Results Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus. Conclusions Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.

Effective endoscopic screening for colorectal cancer (CRC), one of the few preventable cancers, is dependent on the adequate detection and removal of potentially precancerous lesions. However, observed variation in colonoscopy performance in practice and outcomes has highlighted the need for consistent quality measures. Quality indicators or measures are tools that help to quantify health-care processes and can aid in providing high-quality health care. The primary colonoscopy quality indicator is the adenoma detection rate (ADR), which is defined as the proportion of an endoscopist's screening colonoscopies in which one or more adenomas have been detected. The risk of post-colonoscopy CRC is inversely correlated with an endoscopist's ADR. However, ADR is dependent on other quality measures, including cecal intubation rates, withdrawal times, and quality of bowel preparation. Achieving suggested benchmarks for these other quality measures will aid the endoscopist in achieving the recently updated ADR benchmark of 25% in their practice. In addition, beyond ensuring adequate ADRs, endoscopists should have high compliance rates with guideline-recommended and evidence-based screening and surveillance intervals. Compliance with quality measures will ensure effective and safe CRC prevention and better patient outcomes.