Work

Development and validation of a nomogram for in-hospital mortality prediction in acute myocardialinfarction patients with cardiac arrest undergoing percutaneous coronary intervention supported by veno-arterial extracorporeal membrane oxygenation

Year: 2025

Type: article

Abstract: BACKGROUND: The combination of percutaneous coronary intervention (PCI) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO) has become a widely used approach for resuscitating patients with acute myocardial infarction (AMI) complicated by cardiac arrest (CA). Nonetheless, limited research has focused on predicting in-hospital mortality in affected patients. This study aims to identify factors associated with in-hospital mortality and develop a clinical prediction model for these patients. METHODS: Clinical presentations of AMI patients with CA undergoing PCI supported by VA-ECMO at two hospitals in Zhengzhou were evaluated. Patients were stratified based on their survival status at discharge. A comprehensive analysis, which included univariate logistic regression, LASSO regression, and multivariate logistic regression, was conducted to identify predictors and develop a nomogram for in-hospital mortality. The nomogram's predictive performance was subsequently compared to that of existing models. RESULTS: The study included 139 patients, of whom 84 died during hospitalization. Using factors such as age, current smoking, left main culprit vessel, lactic acid levels, and serum creatinine, a nomogram model was developed. The model demonstrated good predictive performance, with an area under the curve of 0.826 (95% CI 0.757-0.894) in the training dataset and 0.783 (95% CI 0.706-0.859) in the internal validation dataset, indicating high accuracy and stability. Clinical decision curve analysis confirmed the model's utility, particularly for risk thresholds above 20%, outperforming existing models. CONCLUSIONS: This study identified independent predictors of in-hospital mortality in AMI patients with CA undergoing PCI supported by VA-ECMO and developed a clinically applicable prediction model.

Source: European journal of medical research

DOI: 10.1186/s40001-025-03004-5

Authors: Haina Li, Mingxuan Duan, Guoyu Duan, Yan Zhou, Xiaoyan Zhao

Institutions: Zhengzhou Central Hospital, First Affiliated Hospital of Zhengzhou University

Language: English

Topic: Mechanical Circulatory Support Devices

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,190