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Updated: May 6, 2026
Primary Outcome Assessment in a Pig Model of Acute Myocardial Infarction
Published on: October 14, 2016
The Development of a Nomogram Predictive Model for Intracardiac Thrombosis Risk: A Study Based on Risk Factors in
Xiaowei Huo1, Zizhu Lian2, Peizhu Dang1
1Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an 710061, China.
Background/Objectives: 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. Methods: 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. Conclusions: 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, p < 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.
Background/Objectives: 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. Methods: 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. Conclusions: 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, p < 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.
