Hydrogen and methane gases are frequently detected in the stomach

AIM： To investigate the incidence of bacterial overgrowth in the stomach by using a new endoscopic method in which intragastric hydrogen and methane gases are collected and analyzed. METHODS： Studies were performed in 490 consecutive patients undergoing esophagogastroscopy, At endoscopy, we intubated the stomach without inflation by air, and 20 mL of intragastric gas was collected through the biopsy channel using a 30 mL syringe, Intragastric hydrogen and methane concentrations were immediately measured by gaschromatography, H pylori infection was also determined by serology. RESULTS： Most of intragastric hydrogen and methane levels were less than 15 ppm （parts per million）. The median hydrogen and methane values （interquartile range） were 3 （1-8） ppm and 2 （1-5） ppm, respectively. The high hydrogen and methane levels for indication of fermentation were decided if the patient had the values more than 90 percentile range in each sample. When a patient had a high level of hydrogen or methane in one or more samples, the patient was considered to have fermentation. The overall incidence of intragastric fermentation was 15.4% （73/473）, Intragastric methane levels were higher in the postoperative group than in other groups. None of the mean hydrogen or methane values was related to Hpylori infection. CONCLUSION： Hydrogen and methane gases are more frequently detected in the stomach than expected, regardless of the presence of abdominal symptoms. Previous gastric surgery influences on the growth of methaneproducing bacteria in the fasting stomach.

Clinical prediction of complicated appendicitis:A case-control study utilizing logistic regression

BACKGROUND Since high-quality evidence on conservative treatment of acute appendicitis using antibiotics has increased,differentiation of patients with complicated appendicitis(CA)from those with simple appendicitis(SA)has become increasingly important.Previous studies have revealed that male gender,advanced age,comorbid conditions,prehospital delay,fever,and anorexia are risk factors of perforated appendicitis.Elevated serum C-reactive protein(CRP)level and hyponatremia have also been reported as predictive biomarkers of CA.However,confounding between various factors is problematic because most previous studies were limited to univariate analysis.AIM To evaluate non-laboratory and laboratory predictive factors of CA using logistic regression analyses.METHODS We performed an exploratory,single-center,retrospective case-control study that evaluated 198 patients(83.9%)with SA and 38 patients(16.1%)with CA.Diagnoses were confirmed by computed tomography images for all cases.We compared age,sex,onset-to-visit interval,epigastric/periumbilical pain,right lower quadrant pain,nausea/vomiting,diarrhea,anorexia,medical history(of previous non-surgically treated appendicitis,diabetes,hypertension,dyslipidemia,liver cirrhosis,hemodialysis,chronic lung diseases,malignant tumors,immunosuppressant use,and antiplatelet use),vital signs,physical findings,and laboratory data to select the explanatory variates for logistic regression.Based on the univariate comparisons,we performed logistic regression for clinical differentiation between CA and SA using only nonlaboratory factors and also including both non-laboratory and laboratory factors.RESULTS The 236 eligible patients consisted of 198 patients(83.9%)with SA and 38 patients(16.1%)with CA.The median ages were 34 years old[interquartile ranges(IR),24-45 years]in the SA group and 49 years old(IR,35-63 years)in the CA group(P<0.001).The median onset-to-visit interval was 1 d(IR,0-1)and 1 d(IR,1-2)in the SA and CA groups,respectively(P<0.001).Heart rate,body temperature,and serum CRP level in the CA group were significantly higher than in the SA group;glomerular filtration rate and serum sodium were significantly lower in the CA group.Anorexia was significantly more prevalent in the CA group.The regression model including age,onset-to-visit interval,anorexia,tachycardia,and fever as non-laboratory predictive factors of CA(Model 1)showed that age≥65 years old,longer onset-to-visit interval,and anorexia had significantly high odds ratios.The logistic regression for prediction of CA including age,onset-to-visit interval,anorexia,serum CRP level,hyponatremia(serum sodium<135 mEq/L),and glomerular filtration rate<60 mL/min/1.73 m2(Model 2)showed that only elevated CRP levels had significantly high odds ratios.Under the curve values of receiver operating characteristics curves of each regression model were 0.74 for Model 1 and 0.87 for Model 2.CONCLUSION Our logistic regression analysis on differentiating factors of CA from SA showed that high CRP level was a strong dose-dependent predictor of CA.

Clinical differentiation of acute appendicitis and right colonic diverticulitis: A case-control study

BACKGROUND Acute right colonic diverticulitis (ARCD) is an important differential diagnosis of acute appendicitis (AA) in Asian countries because of the unusually high prevalence of right colonic diverticula. Due to qualitative improvement and the high penetration rate of computed tomography (CT) scanning in Japan, differentiation of ARCD and AA mainly depends on this modality. But cost, limited availability, and concern for radiation exposure make CT scanning problematic. Differential findings of ARCD from AA are based on several small studies that used univariate comparisons from Korea and Taiwan. Previous studies on clinical and laboratory differences between AA and ARCD are limited. AIM To determine clinical differences between AA and ARCD for differentiation of these two diagnoses by creating a logistic regression model. METHODS We performed an exploratory single-center retrospective case-control study evaluating 369 Japanese patients (age ≥ 16 years), 236 (64.0%) with AA and 133 (36.0%) with ARCD, who were hospitalized between 2012 and 2016. Diagnoses were confirmed by CT images. We compared age, sex, onset-to-visit interval, epigastric/periumbilical pain, right lower quadrant (RLQ) pain, nausea/vomiting, diarrhea, anorexia, medical history, body temperature, blood pressure, heart rate, RLQ tenderness, peritoneal signs, leukocyte count, and levels of serum creatinine, serum C-reactive protein (CRP), and serum alanine aminotrans-ferase. We subsequently performed logistic regression analysis for differentiating AA from ARCD based on the results of the univariate analyses.RESULTS In the AA and ARCD groups, median ages were 35.5 and 41.0 years, respectively (p=0.011);median onset-to-visit intervals were 1 [interquartile range (IQR): 0-1] and 2 (IQR: 1-3) days, respectively (P < 0.001);median leukocyte counts were 12600 and 11500/mm3, respectively (P = 0.002);and median CRP levels were 1.1 (IQR: 0.2-4.1) and 4.9 (IQR: 2.9-8.5) mg/dL, respectively (P < 0.001). In the logistic regression model, odds ratios (ORs) were significantly high in nausea/vomiting (OR: 3.89, 95%CI: 2.04-7.42) and anorexia (OR: 2.13, 95%CI: 1.06-4.28). ORs were significantly lower with a longer onset-to-visit interval (OR: 0.84, 95%CI: 0.72- 0.97), RLQ pain (OR: 0.28, 95%CI: 0.11-0.71), history of diverticulitis (OR: 0.034, 95%CI: 0.005-0.20), and CRP level > 3.0 mg/dL (OR: 0.25, 95%CI: 0.14-0.43). The regression model showed good calibration, discrimination, and optimism. CONCLUSION Clinical findings can differentiate AA and ARCD before imaging studies;nausea/vomiting and anorexia suggest AA, and longer onset-to-visit interval, RLQ pain, previous diverticulitis, and CRP level > 3.0 mg/dL suggest ARCD.

Seventy-five gram glucose tolerance test to assess carbohydrate malabsorption and small bowel bacterial overgrowth

AIM： To investigate non-invasively the incidence of absorption of carbohydrates in diabetic patients during an oral glucose tolerance test （OGTT） and to determine whether malabsorption may be associated with insulin secretion and insulin resistance. METHODS： A standard 75-g OGTT was performed in 82 diabetic patients. The patients received 75 g of anhydrous glucose in 225 mL of water after an overnight fasting and breath samples were collected at baseline and up to 120 rain after ingestion. Breath hydrogen and methane concentrations were measured. Blood glucose and serum insulin concentrations were measured before ingestion and at 30, 60, 90, 120 rain post-ingestion. RESULTS： When carbohydrate malabsorption was defined as subjects with an increase of at least 10 ppm （parts per million） in hydrogen or methane excretion within a 2-h period, 28 （34%） had carbohydrate malabsorption. According to the result of increased breath test, 21 （75%） patients were classified as small bowel bacterial overgrowth and 7 （25%） as glucose malabsorption. Patients with carbohydrate malabsorption were older and had poor glycemic control as compared with those without carbohydrate malabsorption. The HOMA value, the sum of serum insulin during the test and the AinsulinlAglucose ratio were greater in patients with carbohydrate malabsorption. CONCLUSION： Insulin resistance may be overestimated by using these markers if the patient has carbohydrate malabsorption, or that carbohydrate malabsorption may be present prior to the development of insulin resistance. Hence carbohydrate malabsorption should be taken into account for estimating insulin resistance and β-cell function.