西安交通大学学报（医学版）2025，Vol.46Issue(3)：393-401,9.DOI:10.7652/jdyxb202503003
心肌梗死并心衰患者PCI术后院内死亡的机器学习预测模型的构建
Machine learning model for in-hospital mortality prediction in myocardial infarction and heart failure patients post-PCI
摘要
Abstract
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.关键词
机器学习/心肌梗死(MI)/心力衰竭(HF)/经皮冠状动脉介入术(PCI)/院内死亡Key words
machine learning/myocardial infarction(MI)/heart failure(HF)/percutaneous coronary intervention(PCI)/in-hospital mortality分类
临床医学引用本文复制引用
吕华胜,孙丰宇,袁腾,沈好亮,拉再依·巴合提,冀伟,陈铀..心肌梗死并心衰患者PCI术后院内死亡的机器学习预测模型的构建[J].西安交通大学学报（医学版）,2025,46(3):393-401,9.基金项目
新疆维吾尔自治区自然科学基金资助项目(No.2022D01E71)Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01E71) （No.2022D01E71）
