Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Zhixun Bai; Shan Hu; Yan Wang; Wenwen Deng; Ning Gu; Ranzun Zhao; Wei Zhang; Yi Ma; Zhenglong Wang; Zhijiang Liu; Changyin Shen; Bei Shi

doi:10.21037/atm-21-2905

Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

Ann Transl Med. 2021 Jul;9(14):1162. doi: 10.21037/atm-21-2905.

Authors

Zhixun Bai^{1

2

3}, Shan Hu^{1

2}, Yan Wang^{1

2}, Wenwen Deng^{1

2}, Ning Gu^{1

2}, Ranzun Zhao^{1

2}, Wei Zhang², Yi Ma², Zhenglong Wang², Zhijiang Liu², Changyin Shen³, Bei Shi^{1

2}

Affiliations

¹ College of Medicine, Soochow University, Suzhou, China.
² Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
³ Department of Internal Medicine, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.

Abstract

Background: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This report compared the performances of multiple machine learning models and established a late-CS risk nomogram for STEMI patients.

Methods: This study used logistic regression (LR) models, least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and tree-based ensemble machine learning models [light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost)] to predict CS risk in STEMI patients. The models were developed based on 1,598 and 684 STEMI patients in the training and test datasets, respectively. The models were compared based on accuracy, the area under the curve (AUC), recall, precision, and Gini score, and the optimal model was used to develop a late CS risk nomogram. Discrimination, calibration, and the clinical usefulness of the predictive model were assessed using C-index, calibration plotd, and decision curve analyses.

Results: A total of 2282 STEMI patients recruited between January 1, 2016 and May 31, 2020, were included in the complete dataset. The linear models built using LASSO and LR showed the highest overall predictive power, with an average accuracy over 0.93 and an AUC above 0.82. With a C-index of 0.811 [95% confidence interval (CI): 0.769-0.853], the LASSO nomogram showed good differentiation and proper calibration. In internal validation tests, a high C-index value of 0.821 was achieved. Decision curve analysis (DCA) and clinical impact curve (CIC) examination showed that compared with the previous score-based models, the LASSO model showed superior clinical relevance.

Conclusions: In this study, five machine learning methods were developed for in-hospital CS prediction. The LASSO model showed the best predictive performance. This nomogram could provide an accurate prognostic prediction for CS risk in patients with STEMI.

Keywords: Machine learning; ST-segment elevation myocardial infarction (STEMI); cardiogenic shock (CS); least absolute shrinkage and selection operator (LASSO).