ArticleFrontiers in cardiovascular medicine2022
A prediction model for major adverse cardiovascular events in patients with heart failure based on high-throughput echocardiographic data.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- Evaluation of neutrophil oxidative burst capacity in relation to major adverse cardiac events and infection in patients with cardiovascular disease.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Prognostic Value of Serum MicroRNA-423-5p Levels for Cardiovascular Events in Patients with Heart Failure.Biomedicines · 2026Article
- Mortality prediction in the elderly patients with coronary artery disease and atrial fibrillation: a retrospective machine learning approach.Frontiers in medicine · 2026Article
- Machine learning in heart failure diagnosis, prediction, and prognosis: review.Annals of medicine and surgery (2012) · 2024Review
- Serum apolipoprotein B to apolipoprotein A-I ratio predicts mortality in patients with heart failure.ESC heart failure · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
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Abstract
Background: Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options. Materials and methods: We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters. Results: Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 ( Conclusion: In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis.
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