ArticleFrontiers in cardiovascular medicine2022
Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment.
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 19 papers.
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Who cites it
19 citing papers in PubMed, 22 citations in OpenAlex.
- Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies.Scientific reports · 2026Article
- The diagnostic value of miR-338-3p in acute coronary syndrome and its correlation with MACE occurrence.Journal of cardiothoracic surgery · 2026Article
- Early prediction model for in-hospital all-cause death in patients with acute ST-elevation myocardial infarction after primary coronary artery stenting.Frontiers in cardiovascular medicine · 2026Article
- Construction of a predictive model for the efficacy of enhanced external counterpulsation therapy in patients with heart failure with preserved ejection fraction based on automated machine learning and echocardiography.Frontiers in cardiovascular medicine · 2026Article
- Machine learning-selected inflammation biomarkers for stable coronary artery disease with intermediate coronary lesions: potential for long-term prognosis in a multicenter cohort study.Frontiers in physiology · 2026Article
- A clinical nomogram for predicting major adverse cardiovascular and cerebrovascular events in elderly Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention: development and validation in a real-world cohort.Journal of geriatric cardiology : JGC · 2025Article
- Predicting cardiovascular outcomes in elderly patients with acute coronary syndrome: a nomogram approach.Research square · 2025Article
- Mitigating Bias in Opportunistic Screening for MACE with Causal Reasoning.IEEE transactions on artificial intelligence · 2025Article
- Multimodal data-based longitudinal prognostic model for predicting atrial fibrillation recurrence after catheter ablation in patients with patent foramen ovale and paroxysmal atrial fibrillation.European journal of medical research · 2025Article
- Developing a novel diagnostic model for identifying high-risk plaques in new onset unstable angina pectoris using coronary CT angiography.Frontiers in endocrinology · 2025Article
- An Easy-to-Use Risk Stratification System for NSTE-ACS Patients Combining Autonomic Nervous System and Coronary Physiology.International journal of medical sciences · 2025Article
- Multimodal Data-Driven Prognostic Model for Predicting Long-Term Prognosis in Patients With Ischemic Cardiomyopathy and Heart Failure With Preserved Ejection Fraction After Coronary Artery Bypass Grafting: A Multicenter Cohort Study.Journal of the American Heart Association · 2024Article
- Incorporating Inflammation Biomarker-Driven Multivariate Predictive Model for Coronary Microcirculatory Dysfunction in Acute Myocardial Infarction Following Emergency Percutaneous Coronary Intervention.Clinical cardiology · 2024Article
- Multimodal Data Integration Enhance Longitudinal Prediction of New-Onset Systemic Arterial Hypertension Patients with Suspected Obstructive Sleep Apnea.Reviews in cardiovascular medicine · 2024Article
- Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome.Journal of global health · 2024Article
- Article
- Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesions.Journal of inflammation research · 2024Article
- Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model.Cardiovascular diabetology · 2023Article
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Authors and funding
16 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Disruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS. Methods: ACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis. Results: There were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702-0.877) in the development cohort and 0.894 (95% CI: 0.820-0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739-0.866) in the development cohort and 0.798 (95% CI: 0.693-0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs. Conclusion: Our validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.
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