Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study
BackgroundThe incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking....
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-01-01
|
| Series: | JMIR Formative Research |
| Online Access: | https://formative.jmir.org/2024/1/e48487 |
| _version_ | 1797367176986886144 |
|---|---|
| author | Pin Zhang Lei Wu Ting-Ting Zou ZiXuan Zou JiaXin Tu Ren Gong Jie Kuang |
| author_facet | Pin Zhang Lei Wu Ting-Ting Zou ZiXuan Zou JiaXin Tu Ren Gong Jie Kuang |
| author_sort | Pin Zhang |
| collection | DOAJ |
| description |
BackgroundThe incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking.
ObjectiveThis study aimed to develop machine learning–based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI.
MethodsA total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models—artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest—were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score.
ResultsIn total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease.
ConclusionsThe ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning–based prediction models may improve patient management and outcomes in clinical practice. |
| first_indexed | 2024-03-08T17:14:34Z |
| format | Article |
| id | doaj.art-1cfee21748cc492b87b49ca6a60b84e0 |
| institution | Directory Open Access Journal |
| issn | 2561-326X |
| language | English |
| last_indexed | 2024-03-08T17:14:34Z |
| publishDate | 2024-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Formative Research |
| spelling | doaj.art-1cfee21748cc492b87b49ca6a60b84e02024-01-03T14:00:27ZengJMIR PublicationsJMIR Formative Research2561-326X2024-01-018e4848710.2196/48487Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort StudyPin Zhanghttps://orcid.org/0000-0002-7141-9541Lei Wuhttps://orcid.org/0000-0002-4650-5528Ting-Ting Zouhttps://orcid.org/0000-0002-0388-0037ZiXuan Zouhttps://orcid.org/0009-0000-7409-0123JiaXin Tuhttps://orcid.org/0000-0002-5221-2182Ren Gonghttps://orcid.org/0000-0002-1095-3177Jie Kuanghttps://orcid.org/0000-0002-0674-7266 BackgroundThe incidence of major adverse cardiovascular events (MACEs) remains high in patients with acute myocardial infarction (AMI) who undergo percutaneous coronary intervention (PCI), and early prediction models to guide their clinical management are lacking. ObjectiveThis study aimed to develop machine learning–based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI. MethodsA total of 1531 patients with AMI who underwent PCI from January 2018 to December 2019 were enrolled in this consecutive cohort. The data comprised demographic characteristics, clinical investigations, laboratory tests, and disease-related events. Four machine learning models—artificial neural network (ANN), k-nearest neighbors, support vector machine, and random forest—were developed and compared with the logistic regression model. Our primary outcome was the model performance that predicted the MACEs, which was determined by accuracy, area under the receiver operating characteristic curve, and F1-score. ResultsIn total, 1362 patients were successfully followed up. With a median follow-up of 25.9 months, the incidence of MACEs was 18.5% (252/1362). The area under the receiver operating characteristic curve of the ANN, random forest, k-nearest neighbors, support vector machine, and logistic regression models were 80.49%, 72.67%, 79.80%, 77.20%, and 71.77%, respectively. The top 5 predictors in the ANN model were left ventricular ejection fraction, the number of implanted stents, age, diabetes, and the number of vessels with coronary artery disease. ConclusionsThe ANN model showed good MACE prediction after PCI for patients with AMI. The use of machine learning–based prediction models may improve patient management and outcomes in clinical practice.https://formative.jmir.org/2024/1/e48487 |
| spellingShingle | Pin Zhang Lei Wu Ting-Ting Zou ZiXuan Zou JiaXin Tu Ren Gong Jie Kuang Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study JMIR Formative Research |
| title | Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study |
| title_full | Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study |
| title_fullStr | Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study |
| title_full_unstemmed | Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study |
| title_short | Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study |
| title_sort | machine learning for early prediction of major adverse cardiovascular events after first percutaneous coronary intervention in patients with acute myocardial infarction retrospective cohort study |
| url | https://formative.jmir.org/2024/1/e48487 |
| work_keys_str_mv | AT pinzhang machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT leiwu machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT tingtingzou machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT zixuanzou machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT jiaxintu machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT rengong machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy AT jiekuang machinelearningforearlypredictionofmajoradversecardiovasculareventsafterfirstpercutaneouscoronaryinterventioninpatientswithacutemyocardialinfarctionretrospectivecohortstudy |