Machine learning prediction of in-hospital recurrent infarction and cardiac death in patients with myocardial infarction

Abstract

Background and aim: The aim of the study is to identify statistical patterns in patients with myocardial infarction (MI) during hospitalization that allow predicting the development of acute conditions (recurrent myocardial infarction, cardiac death). Methods: We identified 3471 episodes of patients treated with a diagnosis acute MI in Almazov National Medical Research Centre. For modelling we selected episodes with acute MI with cardiac surgery operations. Classical machine learning models were chosen as forecasting models: decision trees and ensembles based on them, logistic regression and support vector machine. Results: The important signs for predicting recurrent MI were the minimum values of hemoglobin, the echocardiography parameters end systolic volume and pulmonary regurgitation, and the minimum value of leukocyte level. Predictors of lethal outcome during hospitalization were advanced age, high values of leukocytes, low values of hemoglobin, high values of alanine aminotransferase. Conclusion: The obtained results make it possible to predict the development of a lethal outcome and re-infarction based on simple parameters that are easily available in clinical practice.

Keywords

• Myocardial infarction
• Predictors of death
• Predictors of re-infarction
• Machine learning