Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.