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一种用于预测心内血栓形成风险的列线图预测模型的开发:一项基于急性心肌梗死患者危险因素的研究

The Development of a Nomogram Predictive Model for Intracardiac Thrombosis Risk: A Study Based on Risk Factors in Patients with Acute Myocardial Infarction.

作者信息

Huo Xiaowei, Lian Zizhu, Dang Peizhu, Zhang Yongjian

机构信息

Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710061, China.

Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710061, China.

出版信息

Biomedicines. 2025 Mar 10;13(3):679. doi: 10.3390/biomedicines13030679.

Abstract

: Intracardiac thrombosis (ICT) is a serious complication in acute myocardial infarction (AMI) patients. This study aimed to identify potential risk factors of ICT in AMI patients, providing valuable insights for clinical management. : A case-control study was conducted involving consecutive AMI patients admitted to the First Affiliated Hospital of Xi'an Jiaotong University between January 2019 and December 2022. Binary logistic regression identified independent risk factors of ICT and a nomogram prediction model was constructed and validated for accuracy. : A total of 7341 patients with ICT and 74 without ICT were included. Multivariate logistic regression identified male gender, acute anterior wall myocardial infarction (AWMI), ventricular aneurysm, and lower prothrombin activity as independent risk factors of ICT in AMI patients. A nomogram based on these factors demonstrated excellent performance (AUC: 0.910, 95% CI: 0.877-0.943, < 0.001), with calibration and sensitivity analyses confirming its robustness. This nomogram provides an accurate tool for predicting ICT risk, facilitating personalized management and early intervention in AMI patients.

摘要

心内血栓形成(ICT)是急性心肌梗死(AMI)患者的一种严重并发症。本研究旨在确定AMI患者ICT的潜在危险因素,为临床管理提供有价值的见解。:进行了一项病例对照研究,纳入了2019年1月至2022年12月期间在西安交通大学第一附属医院连续收治的AMI患者。二元逻辑回归确定了ICT的独立危险因素,并构建了列线图预测模型并验证其准确性。:共纳入7341例有ICT的患者和74例无ICT的患者。多因素逻辑回归确定男性、急性前壁心肌梗死(AWMI)、室壁瘤和较低的凝血酶原活性是AMI患者ICT的独立危险因素。基于这些因素的列线图表现出色(AUC:0.910,95%CI:0.877 - 0.943,<0.001),校准和敏感性分析证实了其稳健性。该列线图为预测ICT风险提供了一种准确的工具,有助于对AMI患者进行个性化管理和早期干预。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6326/11940212/664647137f24/biomedicines-13-00679-g001.jpg

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