Constructing risk prediction models for major adverse cardiovascular and cerebrovascular events in patients with acute myocardial infarction and renal insufficiency
Han Fusheng
Tian Xue
Mi Yuhong
He Hua
Abstract：Objective To construct risk prediction models for major adverse cardiovascular and cerebrovascular events(MACCE)in patients with acute myocardial infarction(AMI)and renal insufficiency using different machine learning algorithms.Methods A total of 740 patients with AMI and renal insufficiency who were hospitalized in Beijing Anzhen Hospital,Capital Medical University from January 2014 to August 2019 were selected as the research objects.Clinical data such as general characteristics,vital signs,comorbidities and laboratory examination results of the patients were collected.The subjects were divided into training set(592 cases)and test set(148 cases)by the simple random sampling method at a ratio of 80％:20％.Five machine learning algorithms,including Logistic regression,random forest,extreme gradient boosting(XGBoost),support vector machine and deep neural network,were used to construct the prediction model of MACCE.The area under the receiver operating characteristic curve(AUC)was used to evaluate the reliability of the model and select the optimal model.Shapley's additive explanation algorithm was used to evaluate the feature influence and perform feature selection to construct the final model.Results MACCE occurred in 473(63.9％)of 740 patients with AMI and renal insufficiency.The XGBoost model had the largest AUC(AUC=0.862).After reducing the features according to their importance rank,an interpretable final XGBoost model with five features was built.The final model could accurately predict the occurrence of MACCE in the internal validation(AUC=0.955).The important clinical characteristics affecting the XGBoost model were serum uric acid,albumin,glycated albumin,body weight and platelet count.Conclusion Among the five models based on machine learning algorithms,XGBoost model has the best effect in predicting MACCE in patients with AMI and renal insufficiency.
Keywords：Acute myocardial infarctionRenal insufficiencyMachine learningMajor adverse cardiovascular and cerebrovascular eventsShapley's additive explanation algorithm
Publication Date：2024-07-08
Online Publishing Date：2026-09-14(First online date of this platform, not the publication date of the document)
Pages：5( 975-979 )