Analysis of an improved workflow of endoscope reprocessing for bedside endoscopic diagnosis and treatment on COVID-19 patients

Severe cases infected with the coronavirus disease 2019(COVID-19),named by the World Health Organization(WHO)on Feb.11,2020,tend to present a hypercatabolic state because of severe systemic consumption,and are susceptible to stress ulcers and even life-threatening gastrointestinal bleeding.Endoscopic diagnosis and treatment constitute an irreplaceable part in the handling of severe COVID-19 cases.Endoscopes,as reusable precision instruments with complicated structures,require more techniques than other medical devices in cleaning,disinfection,sterilization,and other reprocessing procedures.From 2016 to 2019,health care-acquired infection caused by improper endoscope reprocessing has always been among the top 5 on the list of top 10 health technology hazards issued by the Emergency Care Research Institute.Considering the highly infective nature of COVID-19 and the potential aerosol contamination therefrom,it is of pivotal significance to ensure that endoscopes are strictly reprocessed between uses.In accordance with the national standard"Regulation for Cleaning and Disinfection Technique of Flexible Endoscope(WS507-2016),"we improved the workflow of endoscope reprocessing including the selection of chemicals in an effort to ensure quality control throughout the clinical management towards COVID-19 patients.Based on the experience we attained from the 12 severe COVID-19 cases in our hospital who underwent endoscopy 23 times in total,the article provides an improved version of endoscopic reprocessing guidelines for bedside endoscopic diagnosis and treatment on COVID-19 patients for reference.

A novel model for predicting fatty liver disease by means of an artificial neural network

Background:The artificial neural network(ANN)emerged recently as a potent diagnostic tool,especially for complicated systemic diseases.This study aimed to establish a diagnostic model for the recognition of fatty liver disease(FLD)by virtue of the ANN.Methods:A total of 7,396 pairs of gender-and age-matched subjects who underwent health check-ups at the First Affiliated Hospital,College of Medicine,Zhejiang University(Hangzhou,China)were enrolled to establish the ANN model.Indices available in health check-up reports were utilized as potential input variables.The performance of our model was evaluated through a receiver-operating characteristic(ROC)curve analysis.Other outcome measures included diagnostic accuracy,sensitivity,specificity,Cohen’s k coefficient,Brier score,and Hosmer-Lemeshow test.The Fatty Liver Index(FLI)and the Hepatic Steatosis Index(HSI),retrained using our training-group data with its original designated input variables,were used as comparisons in the capability of FLD diagnosis.Results:Eight variables(age,gender,body mass index,alanine aminotransferase,aspartate aminotransferase,uric acid,total triglyceride,and fasting plasma glucose)were eventually adopted as input nodes of the ANN model.By applying a cut-off point of 0.51,the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908[95%confidence interval(CI),0.901-0.915]—significantly higher(P<0.05)than that of the FLI model(0.881,95%CI,0.872-0.891)and that of the HSI model(0.885;95%CI,0.877-0.893).Our ANN model exhibited higher diagnostic accuracy,better concordance with ultrasonography results,and superior capability of calibration than the FLI model and the HSI model.Conclusions:Our ANN system showed good capability in the diagnosis of FLD.It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.