Clinical Prediction Models for Hepatitis B Virus-related Acute-on-chronic Liver Failure:A Technical Report

Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and performance of available clinical prediction models(CPMs).Methods:A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed.Both the quality and performance of the CPMs were assessed.Results:Fifty-two CPMs were identified,of which 31 were HBV-ACLF specific.The modeling data were mostly derived from retrospective(83.87%)and single-center(96.77%)cohorts,with sample sizes ranging from 46 to 1,202.Three-month mortality was the most common endpoint.The Asian Pacific Association for the Study of the Liver consensus(51.92%)and Chinese Medical Association liver failure guidelines(40.38%)were commonly used for HBV-ACLF diagnosis.Serum bilirubin(67.74%),the international normalized ratio(54.84%),and hepatic encephalopathy(51.61%)were the most frequent variables used in models.Model discrimination was commonly evaluated(88.46%),but model calibration was seldom performed.The model for end-stage liver disease score was the most widely used(84.62%);however,varying performance was reported among the studies.Conclusions:Substantial limitations lie in the quality of HBV-ACLF-specific CPMs.Disease severity of study populations may impact model performance.The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

Development of a prediction model for enteral feeding intolerance in intensive care unit patients:A prospective cohort study

BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.