Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

Journal: BMC cardiovascular disorders

PMID: 39849369

Abstract

OBJECTIVE: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI) using a machine learning (ML) algorithm.

Authors

Liu Yang

Department of Ultrasound, Hunan Children's Hospital, Changsha, China.
Li Du

Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China.
Yuanyuan Ge

Department of Cardiology, 920th Hospital of Joint Logistics Support Force, People's Liberation Army of China (PLA), Kunming, Yunnan, China.
Muhui Ou

Department of Cardiology, The Affiliated 920th Hospital of Joint Logistics Support Force, Kunming Medical University, Kunming, China.
Wanyan Huang

Department of Cardiology, The Affiliated 920th Hospital of Joint Logistics Support Force, Kunming Medical University, Kunming, China.
Xianmei Wang

Department of Cardiology, 920th Hospital of Joint Logistics Support Force, People's Liberation Army of China (PLA), Kunming, Yunnan, China. ryan9799@163.com.

Keywords

Aged Biomarkers Decision Support Techniques Decision Trees Female Humans Inflammation Inflammation Mediators Machine Learning Male Middle Aged Myocardial Infarction Neural Networks, Computer Nutrition Assessment Nutritional Status Percutaneous Coronary Intervention Predictive Value of Tests Risk Assessment Risk Factors Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (39849369)

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

Abstract

Authors

Keywords

External Resources

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