Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

BACKGROUND

The 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.

OBJECTIVE

This study aimed to develop machine learning-based early prediction models for MACEs in patients with newly diagnosed AMI who underwent PCI.

METHODS

A 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.

RESULTS

In 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.

CONCLUSIONS

The 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.

A Biomarker Model to Distinguish Types of Myocardial Infarction and Injury

BACKGROUND

Discrimination among patients with type 1 myocardial infarction (T1MI), type 2 myocardial infarction (T2MI), and myocardial injury is difficult.

OBJECTIVES

The aim of this study was to investigate the discriminative value of a 29-biomarker panel in an emergency department setting.

METHODS

Patients presenting with suspected myocardial infarction (MI) were recruited. The final diagnosis in all patients was adjudicated on the basis of the fourth universal definition of MI. A panel of 29 biomarkers was measured, and multivariable logistic regression analysis was used to evaluate the associations of these biomarkers with the diagnosis of MI or myocardial injury. Biomarkers were chosen using backward selection. The model was internally validated using bootstrapping.

RESULTS

Overall, 748 patients were recruited (median age 64 years), of whom 138 had MI (107 T1MI and 31 T2MI) and 221 had myocardial injury. In the multivariable model, 4 biomarkers (apolipoprotein A-II, N-terminal prohormone of brain natriuretic peptide, copeptin, and high-sensitivity cardiac troponin I) remained significant discriminators between T1MI and T2MI. Internal validation of the model showed an area under the curve of 0.82. For discrimination between MI and myocardial injury, 6 biomarkers (adiponectin, N-terminal prohormone of brain natriuretic peptide, pulmonary and activation-regulated chemokine, transthyretin, copeptin, and high-sensitivity troponin I) were selected. Internal validation showed an area under the curve of 0.84.

CONCLUSIONS

Among 29 biomarkers, 7 were identified to be the most relevant discriminators between subtypes of MI or myocardial injury. Regression models based on these biomarkers allowed good discrimination. (Biomarkers in Acute Cardiac Care [BACC]; NCT02355457).