Immune checkpoint inhibitor-associated gastritis:Patterns and management

Immune checkpoint inhibitors(ICIs)are widely used due to their effectiveness in treating various tumors.Immune-related adverse events(irAEs)are defined as adverse effects resulting from ICI treatment.Gastrointestinal irAEs are a common type of irAEs characterized by intestinal side effects,such as diarrhea and colitis,which may lead to the cessation of ICIs.Although irAE gastritis is rarely reported,it may lead to serious complications such as gastrorrhagia.Furthermore,irAE gastritis is often difficult to identify early due to its diverse symptoms.Although steroid hormones and immunosuppressants are commonly used to reverse irAEs,the best regimen and dosage for irAE gastritis remains uncertain.In addition,the risk of recurrence of irAE gastritis after the reuse of ICIs should be considered.In this editorial,strategies such as early identification,pathological diagnosis,mana-gement interventions,and immunotherapy rechallenge are discussed to enable clinicians to better manage irAE gastritis and improve the prognosis of these patients.

Contrast-enhanced ultrasound using SonoVue mixed with oral gastrointestinal contrast agent to evaluate esophageal hiatal hernia: Report of three cases and a literature review

BACKGROUND Due to a thicker abdominal wall in some patients,ultrasound artifacts from gastrointestinal gas and surrounding tissues can interfere with routine ultrasound examination,precluding its ability to display or clearly show the structure of a hernial sac(HS)and thereby diminishing diagnostic performance for esophageal hiatal hernia(EHH).Contrast-enhanced ultrasound(CEUS)imaging using an oral agent mixture allows for clear and intuitive identification of an EHH sac and dynamic observation of esophageal reflux.CASE SUMMARY In this case series,we report three patients with clinically-suspected EHH,including two females and one male with an average age of 67.3±16.4 years.CEUS was administered with an oral agent mixture(microbubble-based SonoVue and gastrointestinal contrast agent)and identified a direct sign of supradiaphragmatic HS(containing the hyperechoic agent)and indirect signs[e.g.,widening of esophageal hiatus,hyperechoic mixture agent continuously or intermittently reflux flowing back and forth from the stomach into the supradiaphragmatic HS,and esophagus-gastric echo ring(i.e.,the“EG”ring)seen above the diaphragm].All three cases received a definitive diagnosis of EHH by esophageal manometry and gastroscopy.Two lesions resolved upon drug treatment and one required surgery.The recurrence rate in follow-up was 0%.The data from these cases suggest that the new non-invasive examination method may greatly improve the diagnosis of EHH.CONCLUSION CEUS with the oral agent mixture can facilitate clear and intuitive identification of HS and dynamic observation of esophageal reflux.

Development and external validation of models to predict acute respiratory distress syndrome related to severe acute pancreatitis

BACKGROUND Acute respiratory distress syndrome(ARDS)is a major cause of death in patients with severe acute pancreatitis(SAP).Although a series of prediction models have been developed for early identification of such patients,the majority are complicated or lack validation.A simpler and more credible model is required for clinical practice.AIM To develop and validate a predictive model for SAP related ARDS.METHODS Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts.Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method.Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors.The discriminatory power of new models was compared with some common models.The performance of calibration ability and clinical utility of the predictive models were evaluated.RESULTS Out of 597 patients with AP,139 were diagnosed with SAP(80 in derivation cohort and 59 in validation cohort)and 99 with ARDS(62 in derivation cohort and 37 in validation cohort).Four identical variables were identified as independent risk factors for both SAP and ARDS:heart rate[odds ratio(OR)=1.05;95%CI:1.04-1.07;P<0.001;OR=1.05,95%CI:1.03-1.07,P<0.001],respiratory rate(OR=1.08,95%CI:1.0-1.17,P=0.047;OR=1.10,95%CI:1.02-1.19,P=0.014),serum calcium concentration(OR=0.26,95%CI:0.09-0.73,P=0.011;OR=0.17,95%CI:0.06-0.48,P=0.001)and blood urea nitrogen(OR=1.15,95%CI:1.09-1.23,P<0.001;OR=1.12,95%CI:1.05-1.19,P<0.001).The area under receiver operating characteristic curve was 0.879(95%CI:0.830-0.928)and 0.898(95%CI:0.848-0.949)for SAP prediction in derivation and validation cohorts,respectively.This value was 0.892(95%CI:0.843-0.941)and 0.833(95%CI:0.754-0.912)for ARDS prediction,respectively.The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.CONCLUSION The present study constructed and validated a simple and accurate predictive model for SAPrelated ARDS in patients with AP.

Machine learning improves prediction of severity and outcomes of acute pancreatitis:a prospective multi-center cohort study

Dear Editor,Acute pancreatitis(AP)is a common acute pancreatic disease of variable severity and outcomes(Mederos et al.,2021).According to systemic and local complications,patients can be classified into severe,moderately severe,and mild AP(Banks et al.,2013).About 20%of AP patients develop severe acute pancreatitis(SAP,with persistent organ failures)of whom 20%–50%die.