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BACKGROUND Phlegmonous gastritis (PG) is a rare bacterial infectious disease characterized by neutrophil-based purulent inflammation of the gastric wall. The most representative causative bacterium is Streptococcus pyogenes, followed by Staphylococcus, Pneumococcus and Enterococcus. Hepatic portal venous gas (HPVG) is considered a potentially fatal condition and is rarely associated with PG. CASE SUMMARY The white blood cell count of a 70-year-old woman with acute lymphocytic leukemia in complete remission dropped to 100/mu L after consolidation chemotherapy. Her vital signs were consistent with septic shock. Venous blood culture revealed the presence of Bacillus cereus. Abdominal computed tomography (CT) and esophagogastroduodenoscopy (EGD) showed marked thickening of the gastric wall. As with the other findings, CT was suggestive of HPVG, and EGD showed pseudomembrane-like tissue covering the superficial mucosa. Histopathological examination of gastric biopsy specimens showed mostly necrotic tissue with lymphocytes rather than neutrophils. Culture of gastric specimens revealed the presence of Bacillus cereus. We finally diagnosed this case as PG with Bacillus cereus-induced sepsis and HPVG. This patient recovered successfully with conservative treatment, chiefly by using carbapenem antibiotics. CONCLUSION The histopathological finding of this gastric biopsy specimen should be called "neutropenic necrotizing gastritis".

BackgroundAlthough machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs model).MethodsAll adult patients hospitalized in a tertiary care hospital in Japan between October 2011 and October 2018 were included in this study. Random forest models with/without laboratory results (Vitals+Labs model and Vitals-Only model, respectively) were trained and tested using chronologically divided datasets. Both models use patient demographics and eight-hourly vital signs collected within the previous 48 hours. The primary and secondary outcomes were the occurrence of IHCA in the next 8 and 24 hours, respectively. The area under the receiver operating characteristic curve (AUC) was used as a comparative measure. Sensitivity analyses were performed under multiple statistical assumptions.ResultsOf 141,111 admitted patients (training data: 83,064, test data: 58,047), 338 had an IHCA (training data: 217, test data: 121) during the study period. The Vitals-Only model and Vitals+Labs model performed comparably when predicting IHCA within the next 8 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.862 [95% confidence interval (CI): 0.855-0.868] vs 0.872 [95% CI: 0.867-0.878]) and 24 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.830 [95% CI: 0.825-0.835] vs 0.837 [95% CI: 0.830-0.844]). Both models performed similarly well on medical, surgical, and ward patient data, but did not perform well for intensive care unit patients.ConclusionsIn this single-center study, the machine learning model predicted IHCAs with good discrimination. The addition of laboratory values to vital signs did not significantly improve its overall performance.