Blanchard Valley Health System

The objective of this article is to highlight the clinical features, screening, diagnosis, treatment, and prevention of gastric cancer (GC). Early GC is often asymptomatic leading to frequent delays in diagnosis. Weight loss and persistent abdominal pain are the most common symptoms at initial diagnosis. The diagnosis of GC typically involves a combination of endoscopy, biopsy, and imaging studies. Endoscopic resection techniques are emerging as successful treatment options for early GC. Treatment options for advanced GC include surgery and chemotherapy. The first line chemotherapy for advanced GC consists of doublet therapy with a combination of platinum and fluoropyrimidines. Trastuzumab, a monoclonal antibody, is used in the treatment of human epidermal growth factor 2 positive GCs. Antiangiogenic agents and immunotherapy are also useful in the treatment of GC. Currently there are no GC screening guidelines in the United States, but they exist in other regions where there is increased prevalence of GC. Prevention strategies for GC include Helicobacter pylori eradication and adoption of a healthy diet consisting of fruits and vegetables.

Artificial intelligence (AI) has made significant advancements in the medical domain in recent years. AI, an expansive field comprising Machine Learning (ML) and, within it, Deep Learning (DL), seeks to emulate the intricate operations of the human brain. It examines vast amounts of data and plays a crucial role in decision-making, overcoming limitations related to human evaluation. DL utilizes complex algorithms to analyze data. ML and DL are subsets of AI that utilizehard statistical techniques that help machines consistently improve at tasks with experience. Pancreatic cancer is more common in developed countries and is one of the leading causes of cancer-related mortalityworldwide. Managing pancreatic cancer remains a challenge despite significant advancements in diagnosis and treatment. AI has secured an almost ubiquitous presence in the field of oncological workup and management, especially in gastroenterology malignancies. AI is particularly useful for various investigations of pancreatic carcinoma because it has specific radiological features that enable diagnostic procedures without the requirement of a histological study. However, interpreting and evaluating resulting images is not always simple since images vary as the disease progresses. Secondly, a number of factors may impact prognosis and response to the treatment process. Currently, AI models have been created for diagnosing, grading, staging, and predicting prognosis and treatment response. This review presents the most up-to-date knowledge on the use of AI in the diagnosis and treatment of pancreatic carcinoma.

This review article aims to provide a comprehensive overview of recent epidemiology, pathogenesis, risk factors, and premalignant conditions of gastric cancer. Worldwide, gastric cancer is one of the most common and most fatal cancers. The incidence and mortality remain high in regions such as East Asia and Eastern Europe. Although there is a lower incidence in the United States, it remains a deadly disease. Age, gender, and race are non-modifiable demographic risk factors for developing gastric cancer. There have been several dietary and lifestyle risk factors such as salt preserved foods, N-nitroso compounds containing foods, tobacco smoke, alcohol use, and obesity that have been shown to contribute to the development of gastric cancer. Infections have additionally been shown to have a clear role in the pathogenesis of gastric cancer as Helicobacter pylori eradication has shown a significant reduction in the incidence of gastric cancer as well as other pathogens such as Epstein-Barr virus. There are certain premalignant lesions that increase the risk of developing gastric cancer. These include atrophic gastritis, and intestinal metaplasia amongst others.

Introduction: Clostridioides difficile infection (CDI) is a healthcare-associated infection that is associated with significant morbidity and mortality. A number of factors, including the use of antibiotics, advanced age, and comorbidities, increase the risk of CDI. However, research on the effects of body mass index (BMI) on CDI outcomes is not well studied. Conversely, lower BMI has been associated with worse infection outcomes, including higher mortality rates. The aim of this study is to investigate the effects of BMI on outcomes of CDI in hospitalized patients. Methods: A retrospective analysis was performed by utilizing the NIS database 2016 - 2020 and the International Classification of Diseases, Tenth Revision codes to identify the patients > 18 years old with CDI. We categorized BMI into four different categories, category I (19.9 or less), category II (20-29.9), category III (30-39.9), and category IV (> 40) using appropriate ICD-10-CM codes. Multivariable regression analyses were performed adjusting for demographics, hospital-level characteristics, and relevant comorbidities. Results: A total of 854,194 patients with CDI were identified in the year 2016 to 2020. Among these cases, different categories were identified: 56,055 patients (6.5%) were in category I, 59,289 patients (6.9%) into category II, 52,195 patients (6.1%) into category III, and 48,724 patients (17.7%) into category IV. Multivariate analysis demonstrated that patients in category IV had a significantly higher odds of mortality compared to other BMI subgroups (OR = 1.16, 95% CI: 1.07-1.29, P-value < 0.00). Additionally, patients in category I also exhibited increased odds of mortality (OR = 1.72, 95% (CI): 1.46-2.02; P-value < 0.00). Further, category IV patients displayed elevated odds of comorbidities such as hypertension (OR = 1.44, 95% CI: 1.40-1.48; p < 0.001), diabetes (OR = 1.19, 95% CI: 1.16-1.22; p < 0.001), and CKD (OR = 1.10, 95% CI: 1.06-1.15; p < 0.001) when compared to other BMI subgroups. Conclusion: The results revealed that both low BMI (< 20) and high BMI (>40) were associated with higher mortality rates from CDI. The relationship between extremely low or high BMI values and CDI mortality that has been observed emphasizes the significance of taking BMI into account as a risk factor and potential prognostic indicator in CDI management. Further research is warranted to unravel the underlying mechanisms and explore potential interventions to optimize outcomes in CDI patients with extreme BMI values (Table 1). Table 1. - Clostridioides difficile mortality based on different subclasses of BMI Total Number Frequency (%) BMI Category 1 (19.9 or less) 56,055 6.5 BMI Category 2 (20-29.9) 59,289 6.9 BMI Category 3 (30-39.9) 52,195 6.1 BMI Category 4 (> 40) 48,724 17.7 Mortality Odds ratio Adjusted OR P-value 95% CI BMI Category 1 (19.9 or less) 1.51 1.43 0.00 1.33-1.54 BMI Category 2 (20-29.9) 0.88 0.85 0.00 0.78-0.92 BMI Category 3 (30-39.9) 0.58 0.65 0.00 0.59-0.73 BMI Category 4 (> 40) 1.06 1.17 0.00 1.07-1.29

Sous l’effet de la mobilité croissante des individus à l’échelle mondiale et des changements qui ont affecté les États-nations modernes, le nombre de personnes détenant au moins deux nationalités est en constante augmentation, au point que la pluri-nationalité serait en train de devenir une « nouvelle norme globale » (Weil, 2011). Ce phénomène plonge ses racines dans l’héritage migratoire des sociétés et s’explique par plusieurs facteurs, parmi lesquels l’importance croissante du jus soli (d...

Introduction: Portal vein thrombosis (PVT) is a frequent complication in patients with cirrhosis. There is limited data on the outcomes of PVT in patients who smoke in relation to cirrhosis. Still, it may also exist as an independent vascular condition without liver damage, such as in prothrombotic states [1]. We aim to determine outcomes in patients diagnosed with PVT who smokes with and without cirrhosis. Methods: A retrospective analysis was performed by utilizing the National Inpatient Sample database (2016, 2017, and 2018) and the International Classification of Diseases, Tenth Revision codes to identify the patients with the principal diagnosis of PVT and smoking. We assessed the all-cause in-hospital mortality, morbidity, length of hospital stay (LOS), and total costs between groups with and without cirrhosis. Categorical variables were compared using the chi-square test, and continuous variables were compared using the t-test. Results: We identified 33,314 patients with PVT who are also smokers, of whom 14,991 had cirrhosis, and 18,323 were without cirrhosis. The in-hospital mortality was significantly higher in patients with cirrhosis (OR 2.95, 95% CI 2.40–3.63; P< 0.01). Diabetes (P< 0.01), obesity (P=0.001), cardiovascular comorbidity (P< 0.01), and older age (P=0.02) are identified as predictors of mortality. Patients with PVT and smoking with cirrhosis have high odds of upper GI bleeding (OR 1.87, 95% CI 1.48–2.37; P< 0.01), peritonitis (OR 2.0, 95% CI 3.82–6.82; P< 0.01), and acute kidney injury (OR 2.45, 95% CI 2.18–2.76; P< 0.01). We found that PVT patients with cirrhosis had a longer LOS (6.7 days vs. 6.1 days; P< 0.01) and higher total hospital costs ($12,324 vs. $10,238; P< 0.00). Conclusion: In patients with PVT who are current smokers, cirrhosis is an independent significant risk factor for in-hospital mortality. Cirrhosis has been associated with increased complications like upper GI bleeding, peritonitis, and acute kidney injury in PVT. Mean LOS and resource utilization were also higher in patients with cirrhosis compared to patients without cirrhosis. (Tables) 1. Intagliata NM, Caldwell SH, Tripodi A. Diagnosis, Development, and Treatment of Portal Vein Thrombosis in Patients With and Without Cirrhosis. Gastroenterology. 2019;156(6):1582- 1599.e1. doi:10.1053/j.gastro.2019.01.265 Table 1. - Primary and secondary outcomes of Portal Vein thrombosis and smoking with cirrhosis vs without cirrhosis Table A Frequency in % With Cirrhosis (N= 14,991) Without Cirrhosis (N= 18,323) OR and CI p-value In-hospital mortality 10.0 3.6 2.8 [2.40-3.630 0.00 Upper GI bleeding 6.2 3.4 1.87 [1.48-2.37] 0.00 AKI 31.6 15.8 2.45 [2.18-2.76] 0.00 Peritonitis 7.1 1.4 5.00 [3.82-6.82] 0.00 Sepsis 6.2 5.7 1.09 [0.89-1.33] 0.395 Ileus 2.53 2.95 0.85 [0.62-1.15] 0.31 Table 2. - Patient's characteristics and comorbidities Table B Age (mean) 61 60 Female 29.6 38.3 0.000 Race Caucasian 66.6 72.3 0.000 African American 10.2 12.2 Hispanic 15.4 9.0 Asian 3.5 2.8 Native American 1.0 0.3 Others 3.1 3.0 Insurance Medicare 50.2 46.2 0.000 Medicaid 21.1 15.81 Private 25.1 34.1 Others/Uninsured 3.4 3.8 Bed size Small 12.8 14.3 0.25 Medium 21.2 21.6 Large 65.5 64.5 Hospital Region Northeast 19.2 22.1 0.32 Midwest 24.0 26.5 South 31.4 30.8 West 25.2 20.4 Teaching hospital 83.8 81.8 0.03 Chronic comorbidity Hypertension 36.6 41.9 0.00 Diabetes mellitus 38.8 29.6 0.000 Chronic kidney disease 14.8 8.8 0.000 Chronic heart failure 8.2 6.8 0.03 Obesity 13.5 13.4 0.91 Dyslipidemia 20.5 30.3 0.00 Coronary artery disease 14.1 15.0 0.31

We are presenting a rare case of pyloric stenosis due to Crohn’s disease. A 53-year-old woman with prior history of colonic Crohn’s disease was admitted to the hospital with gastric outlet obstruction. Esophagogastroduodenoscopy demonstrated pyloric stenosis and biopsy was consistent with Crohn’s disease. She was treated with corticosteroids and her condition improved.