Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with New-Onset ST-Segment Elevation Myocardial Infarction

The logistic regression model effectively predicts MACE risk after PCI in STEMI patients. The nomogram serves as a practical tool for clinicians, supporting personalized risk assessment and improving clinical decision-making.

Authors

Ting Zhang (ORCID: https://orcid.org/0009-0005-8773-094X)
Chunli Chen (ORCID: https://orcid.org/0000-0002-1151-5582)
Min Chen (ORCID: https://orcid.org/0000-0002-0093-9056)
Cuiling Sun (ORCID: https://orcid.org/0009-0003-3130-4197)
Yang Li (ORCID: https://orcid.org/0000-0002-0112-1328)
Jing Zhang (ORCID: https://orcid.org/0000-0002-3958-2170)

Institutions

Bengbu Medical College (CN)
Anhui Medical University (CN)
Second Affiliated Hospital of Anhui Medical University (CN)

Publication Details

Journal: Reviews in Cardiovascular Medicine
Published: 2025-02-21
DOI: https://doi.org/10.31083/rcm25758
Citations: 2
Primary Topic: Acute Myocardial Infarction Research
Type: article
Field-Weighted Citation Impact: 2.78

Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with New-Onset ST-Segment Elevation Myocardial Infarction

Authors

Institutions

Publication Details

Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with New-Onset ST-Segment Elevation Myocardial Infarction

Application of Machine Learning Algorithms in Predicting Major Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with New-Onset ST-Segment Elevation Myocardial Infarction

Abstract

Ask Laika to Summarize, Analyze, and Connect papers live on the map.