Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Objective To systematically review the safety of disialoganglioside(GD2)targeted therapies in the treatment of pediatric neuroblastoma(NB).Methods PubMed,Embase,Cochrane Library,CNKI,WanFang Data,SinoMed databases and ClinicalTrials.gov clinical trial registration system were electronically searched to collect clinical studies of GD2-targeted therapies for pediatric NB from inception to November 30,2024.Two reviewers independently screened the literature,extracted data and assessed the risk of bias of the included studies.Meta-analysis was performed by using R 4.3.1 software.Results A total of 21 studies involving 2,438 patients were included.Meta-analysis results showed that the overall incidence(95％CI)of serious adverse drug events(SADEs)in GD2-targeted therapies for pediatric NB was 51.67％(35.43％to 67.92％).The serious fever and alanine aminotransferase(ALT)increase were common with the incidence(95％CI)of 7.80％(0.80％to 18.85％)and 5.10％(2.02％to 10.55％)respectively.The incidences(95％CI)of grade 3 to 5 adverse drug reactions(ADRs):fever 18.81％(8.00％to 29.61％),pain 30.32％(16.21％to 44.44％),hypokalemia 18.96％(6.44％to 31.49％),ALT increased 10.41％(2.96％to 17.86％),hypotension 8.48％(3.78％to 13.18％),anemia 16.62％(5.97％to 27.26％),platelet count decreased 16.46％(5.55％to 27.37％),lymphocyte count decreased 10.42％(0.97％to 26.09％),neutrophil count decreased 13.37％(3.12％to 23.62％).Among the intervention subgroups,the incidences of grade 3 to 5 ADRs were relatively low in dinutuximab β group and GD2-chimeric antigen receptor T cells(GD2 CART)group.Conclusions The incidences of SADEs and grade 3 to 5 ADRs are high in pediatric NB patients receiving GD2-targeted therapy,which should be paid attention to.The safety profile of GD2 targeted therapies under development for pediatric NB are even better than those of GD2 targeted drugs already on the market,especially GD2 CART,which shows good safety potential.

Appropriate data sources are the cornerstone of pharmacoepidemiological research.Based on the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition),hereinafter referred to as"the Guideline(2nd edition)",this article provides an in-depth interpretation of the classification and selection of data sources in pharmacoepidemiological research.It begins with a brief overview of the evolution of data sources in pharmacoepidemiological studies,followed by a detailed analysis of the definitions,applicable scopes,conditions of use,and strengthens and limitations of primary and secondary data sources.It further introduces the characteristics of common data sources and relevant classic domestic and international databases.Subsequently,integrating aspects of the Guideline(2nd edition),including research questions,study design,study population,sample size,exposure or intervention,outcomes,and covariates,the article discusses the criteria and strategies for choosing data sources in pharmacoepidemiological research.Ultimately,a data source selection methodology is established based on clear classification,guided by research questions,tailored to research methods,and supported by data quality,aiming to promote the development of high-quality pharmacoepidemiological research in China.

Epigastric pain, the most common symptom of chronic pancreatitis (CP), seriously affects the quality of life and causes huge social and economic burden. The pathogenesis of pain involves pancreatic duct hypertension, neurogenic mechanisms, and the effects of inflammatory mediators. As a minimally invasive treatment, endoscopic therapy has emerged as a pivotal option for pain treatment in CP, primarily encompassing pancreatic duct decompression techniques and nerve interventions under endoscopy. Endoscopic pancreatic duct decompression, based on endoscopic retrograde cholangiopancreatography (ERCP) and combined with extracorporeal shock wave lithotripsy (ESWL), can effectively reduce pancreatic duct pressure and relieve pain through pancreatic duct stone removal and main pancreatic duct stent implantation. Endoscopic nerve intervention techniques mainly include celiac plexus block/neurolysis and radiofrequency ablation under the guidance of endoscopic ultrasonography (EUS), which can relieve pain by inhibiting nociceptive transmission or destroying nerve fibers. This article reviewed the mechanism of CP abdominal pain and the progress of endoscopic treatment.

Objective To systematically review the safety of disialoganglioside(GD2)targeted therapies in the treatment of pediatric neuroblastoma(NB).Methods PubMed,Embase,Cochrane Library,CNKI,WanFang Data,SinoMed databases and ClinicalTrials.gov clinical trial registration system were electronically searched to collect clinical studies of GD2-targeted therapies for pediatric NB from inception to November 30,2024.Two reviewers independently screened the literature,extracted data and assessed the risk of bias of the included studies.Meta-analysis was performed by using R 4.3.1 software.Results A total of 21 studies involving 2,438 patients were included.Meta-analysis results showed that the overall incidence(95％CI)of serious adverse drug events(SADEs)in GD2-targeted therapies for pediatric NB was 51.67％(35.43％to 67.92％).The serious fever and alanine aminotransferase(ALT)increase were common with the incidence(95％CI)of 7.80％(0.80％to 18.85％)and 5.10％(2.02％to 10.55％)respectively.The incidences(95％CI)of grade 3 to 5 adverse drug reactions(ADRs):fever 18.81％(8.00％to 29.61％),pain 30.32％(16.21％to 44.44％),hypokalemia 18.96％(6.44％to 31.49％),ALT increased 10.41％(2.96％to 17.86％),hypotension 8.48％(3.78％to 13.18％),anemia 16.62％(5.97％to 27.26％),platelet count decreased 16.46％(5.55％to 27.37％),lymphocyte count decreased 10.42％(0.97％to 26.09％),neutrophil count decreased 13.37％(3.12％to 23.62％).Among the intervention subgroups,the incidences of grade 3 to 5 ADRs were relatively low in dinutuximab β group and GD2-chimeric antigen receptor T cells(GD2 CART)group.Conclusions The incidences of SADEs and grade 3 to 5 ADRs are high in pediatric NB patients receiving GD2-targeted therapy,which should be paid attention to.The safety profile of GD2 targeted therapies under development for pediatric NB are even better than those of GD2 targeted drugs already on the market,especially GD2 CART,which shows good safety potential.

Appropriate data sources are the cornerstone of pharmacoepidemiological research.Based on the Guide on Methodological Standards in Pharmacoepidemiology in China(2nd edition),hereinafter referred to as"the Guideline(2nd edition)",this article provides an in-depth interpretation of the classification and selection of data sources in pharmacoepidemiological research.It begins with a brief overview of the evolution of data sources in pharmacoepidemiological studies,followed by a detailed analysis of the definitions,applicable scopes,conditions of use,and strengthens and limitations of primary and secondary data sources.It further introduces the characteristics of common data sources and relevant classic domestic and international databases.Subsequently,integrating aspects of the Guideline(2nd edition),including research questions,study design,study population,sample size,exposure or intervention,outcomes,and covariates,the article discusses the criteria and strategies for choosing data sources in pharmacoepidemiological research.Ultimately,a data source selection methodology is established based on clear classification,guided by research questions,tailored to research methods,and supported by data quality,aiming to promote the development of high-quality pharmacoepidemiological research in China.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.