Post-endoscopy Barrett's neoplasia after a negative index endoscopy: a systematic review and proposal for definitions and performance measures in endoscopy.

BACKGROUND: A high rate of neoplasia, both high grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) has been reported in Barrett's esophagus at index endoscopy, but precise rates of post-endoscopy Barrett's neoplasia (PEBN) are unknown. METHODS: A systematic review and meta-analysis was performed examining electronic databases (inception to October 2021) for studies reporting PEBN. Consistent with the definitions of post-colonoscopy colorectal cancer proposed by the World Endoscopy Organization, we defined neoplasia (HGD/EAC) detected at index endoscopy and/or within 6 months of a negative index endoscopy as "prevalent" neoplasia, that detected after 6 months of a negative index endoscopy and prior to next surveillance interval (i. e. 3 years) as PEBN or "interval" neoplasia, and that detected after 36 months from a negative index endoscopy as "incident" neoplasia. The pooled incidence rates and proportions relative to total neoplasia were analyzed. RESULTS: 11 studies (n = 59 795; 61 % men; mean [SD] age 62.3 [3.3] years) met the inclusion criteria. The pooled incidence rates were: prevalent neoplasia 4.5 % (95 %CI 2.2 %-8.9 %) at baseline and an additional 0.3 % (0.1 %-0.7 %) within the first 6 months, PEBN 0.52 % (0.46 %-0.58 %), and incident neoplasia 1.4 % (0.9 %-2.1 %). At 3 years from the index endoscopy, PEBN accounted for 3 % of total Barrett's neoplasia, while prevalent neoplasia accounted for 97 %. CONCLUSION: Neoplasia detected at or within 6 months of index endoscopy accounts for most cases of Barrett's neoplasia (> 90 %). PEBN accounts for ~3 % of cases and can be used for validation in future. This highlights the importance of a high quality index endoscopy in Barrett's esophagus and the need to establish quality benchmarks to measure endoscopists' performance.

Impact of age, sex, race, and regionality on major clinical outcomes of COVID-19 in hospitalized patients in the United States.

BACKGROUND: The COVID-19 pandemic has affected all people across the globe. Regional and community differences in timing and severity of surges throughout the pandemic can provide insight into risk factors for worse outcomes in those hospitalized with COVID-19. METHODS: The study cohort was derived from the Cerner Real World Data (CRWD) COVID-19 Database made up of hospitalized patients with proven infection from December 1, 2019 through November 30, 2020. Baseline demographic information, comorbidities, and hospital characteristics were obtained. We performed multivariate analysis to determine if age, race, comorbidity and regionality were predictors for mortality, ARDS, mechanical ventilation or sepsis hospitalized patients with COVID-19. RESULTS: Of 100,902 hospitalized COVID-19 patients included in the analysis (median age 52 years, IQR 36-67; 50.7% female), COVID-19 case fatality rate was 8.5% with majority of deaths in those ≥ 65 years (70.8%). In multivariate analysis, age ≥ 65 years, male gender and higher Charlson Comorbidity Index (CCI) were independent risk factors for mortality and ARDS. Those identifying as non-Black or non-White race have a marginally higher risk for mortality (OR 1.101, CI 1.032-1.174) and greater risk of ARDS (OR 1.44, CI 1.334-1.554) when compared to those who identify as White. The risk of mortality or ARDS was similar for Blacks as Whites. Multivariate analysis found higher mortality risk in the Northeast (OR 1.299, CI 1.22-1.29) and West (OR 1.26, CI 1.18-1.34). Larger hospitals also had an increased risk of mortality, greatest in hospitals with 500-999 beds (OR 1.67, CI 1.43-1.95). CONCLUSION: Advanced age, male sex and a higher CCI predicted worse outcomes in hospitalized COVID-19 patients. In multivariate analysis, worse outcomes were identified in small minority populations, however there was no difference in study outcomes between those who identify as Black or White.

Pandemic information support lifecycle: Evidence from the evolution of mobile apps during COVID-19.

Information sharing and consumption play an important role during a pandemic in managing constrained resources and devising effective plans to minimize a pandemic's impact. The type of support extended by information also changes as a pandemic evolves. In this paper, we present a novel framework to understand the different types of information support needed during a pandemic crisis. Adapting phases from the pandemic crisis management lifecycle, we propose five different overlapping phases of our proposed Pandemic Information Support Lifecycle (PISL): awareness information support, preventive care information support, active information support, confidence-building information support and evaluation information support. To validate the proposed PISL, we examine the evolution of new mobile apps during the current COVID-19 pandemic by developing a taxonomy for mobile app-based information support. The proposed lifecycle presents future phases of information support for the ongoing COVID-19 pandemic, while identifying specific areas that need additional research and mobile-based information support development.

Factors Associated With Poor Outcomes Among Patients With SARS-CoV-2 Coronavirus Infection and Gastrointestinal Symptoms.

Background and Aims: Gastrointestinal (GI) symptoms are present in 20% of patients with SARS-CoV-2 coronavirus infection (COVID-19). We studied the association of GI symptoms (in patients with COVID-19) with adverse outcomes and factors associated with poor outcomes in these patients. Methods: The study cohort included 100,902 patients from the Cerner Real-World Data COVID-19 Database of hospital encounters and emergency department visits with COVID-19 infection from December 1, 2019, to November 30, 2020. Multivariate analysis was used to study the effect of GI symptoms on adverse outcomes and the factors associated with mortality, acute respiratory distress syndrome (ARDS), sepsis, and ventilator requirement or oxygen dependence in patients with COVID-19 and GI symptoms. Results: Patients with COVID-19 and GI symptoms were significantly more likely to have ARDS (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.11, 1.29), sepsis (OR 1.19, 95% CI 1.14, 1.24), acute kidney injury (OR 1.30, 95% CI 1.24, 1.36), venous thromboembolism (OR 1.36, 95% CI 1.22, 1.52), or GI bleed (OR 1.62, 95% CI 1.47, 1.79) and less likely to experience cardiomyopathy (OR 0.87, 95% CI 0.77, 0.99) or death (OR 0.71, 95% CI 0.67, 0.75). Among those with GI symptoms, older age, higher Charlson comorbidity index scores, and use of proton pump inhibitors/H2 receptor antagonists were associated with higher mortality, ARDS, sepsis, and ventilator or oxygen requirement. Conclusion: Patients with COVID-19 who have GI symptoms have overall worse in-hospital complications but less cardiomyopathy and mortality. Older age, higher comorbidity scores, and the use of proton pump inhibitors and H2 receptor antagonists are associated with poor outcomes in these patients.

Growth in the development of health and fitness mobile apps amid COVID-19 pandemic.

Objectives: Technology in the form of mobile apps has played an essential role in facilitating, tracking, and maintaining health and fitness activities during the pandemic. When countries opted for partial or complete lockdowns to contain the spread of the coronavirus disease 2019 virus, it led to people working on their health and fitness-related activities from their homes, just as they continued working from home. This paper aims to quantify the impact of coronavirus disease 2019 on the development of health and fitness mobile apps. Specifically, we compute the effect of coronavirus disease 2019 on the growth of different sub-categories of health and fitness apps. Methods: We scraped data about a population of 78,890 health and fitness apps from the iOS App Store. First, categories of health and fitness apps are identified using text analysis on the descriptions of apps. Second, the rise in the development of new apps is analyzed. To quantify the impact of coronavirus disease 2019 on the growth of the health and fitness apps, multiple time-series forecasting models are created for different categories of health and fitness apps. Results: The text analysis identified twelve different types of health and fitness apps on the app market. Our models estimated that the number of health and fitness apps on the iOS app market exceeded the expected growth by 29.9% after the pandemic. The results of all categories of health and fitness are discussed in the paper. Conclusions: Our analysis found significant growth in the development of new health and fitness apps after the pandemic outbreak. The post hoc study of the population of health and fitness apps presented the current state of this particular area of the app market. In addition, it provided potential growth areas in app markets where there are fewer apps.

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records.

Health disparities across ethnic or racial groups are typically examined through single behavior at a time. The syndemics and multimorbidity health disparities have not been well examined by race. In this study, we study health disparities by identifying the networks of multimorbidities among individuals from seven population groups based on race, including White, African American, Asian, Hispanic, Native American, Bi- or Multi-racial and Pacific Islander. We examined a large electronic medical record (EMR) containing health records of more than 18.7 million patients and created multimorbidity networks considering their lifetime history from medical records in order to compare the network properties among seven population groups. In addition, the networks at organ system level depicting the relationship among disorders belonging to different organ systems are also compared. Our macro analysis at the organ-level indicates that African-Americans have a stronger multimorbidity network followed by Whites and Native Americans. The networks of Asians and Hispanics are sparse. Specifically, the relationship of infectious and parasitic disorders with respiratory, circulatory and genitourinary system disorders is stronger among African Americans than others. On the other hand, the relationship of mental disorders with respiratory, musculoskeletal system and connective tissue disorders is more prevalent in Whites. Similar other disparities are discussed. Recognition and explanation of such differences in multimorbidities inform the public health policies, and can inform clinical decisions as well. Our multimorbidity network analysis identifies specific differences in diagnoses among different population groups, and presents questions for biological, behavioral, clinical, social science, and policy research.

BIARAM: A process for analyzing correlated brain regions using association rule mining.

BACKGROUND AND OBJECTIVE: Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other disciplines to help determine items that might be purchased together. We apply this technique toward identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and suggest how to apply this process for generating insights about the brain's responses to specific stimuli (e.g. technology-associated interruptions). METHODS: We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution brain electromagnetic tomography); reorganize these into a "transactional" dataset; and generate association rules through ARM. RESULTS: We compare the results with more conventional methods for analyzing neuroimaging data. We show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interruptions. This result would not be obvious from independent analysis of each region. CONCLUSIONS: The main contribution of this paper is introducing ARM as a method for analyzing multiple images. We suggest that the biomedical community may apply this commonly available data mining technique to develop further insights about correlated regions affected by specific stimuli.

Examining health disparities by gender: A multimorbidity network analysis of electronic medical record.

PROBLEM: Multimorbidity health disparities have not been well examined by gender. Co-occurring diseases may be mutually deleterious, co-occurring independently, or co-occurring from a common antecedent. Diseases linked by a common antecedent may be caused by biological, behavioral, social, or environmental factors. This paper aims to address the co-occurrences of diseases using network analysis. METHODS: In this study, we identify these multi-morbidities from a large electronic medical record (EMR) containing diagnoses, symptoms and treatment data on more than 22.1 million patients. We create multimorbidity networks from males and females medical records and compare their structural properties. RESULTS: Our macro analysis at the organ-level indicates that females have a stronger multimorbidity network than males. For example, the female multimorbidity network includes six linkages to mental health, wherein the male multimorbidity network includes only two linkages to mental health. The strength of some disease associations between lipid metabolism and chronic heart disorders is stronger in males than females. CONCLUSION: Our multimorbidity network analysis by gender identifies specific differences in disease diagnosis by gender, and presents questions for biological, behavioral, clinical, and policy research.