Trial reportLipids in health and disease2021
Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.
Trial report in Lipids in health and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 29 citations in OpenAlex.
- Main differences between two highly effective lipid-lowering therapies in subclasses of lipoproteins in patients with acute myocardial infarction.Lipids in health and disease · 2021Trial
- Artificial Intelligence in Cardiovascular Risk Prediction: An Up-to-Date Narrative Review on the Emerging Role of Lipid Profile-Based Models.Journal of clinical medicine · 2026Review
- Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.European journal of clinical investigation · 2026Observational
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Cardiovascular risk assessment enhanced by automated machine learning in a multi-phase study.Scientific reports · 2025Article
- Reperfusion injury in STEMI: a double-edged sword.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Review
- Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Article
- Review
- Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning.BMC pediatrics · 2024Article
- Exosomal circ-0020887 and circ-0009590 as novel biomarkers for the diagnosis and prediction of short-term adverse cardiovascular outcomes in STEMI patients.Open medicine (Warsaw, Poland) · 2023Article
- Diagnostic Accuracy of the Deep Learning Model for the Detection of ST Elevation Myocardial Infarction on Electrocardiogram.Journal of personalized medicine · 2022Article
- Machine learning links different gene patterns of viral infection to immunosuppression and immune-related biomarkers in severe burns.Frontiers in immunology · 2022Article
- Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.Journal of personalized medicine · 2021Article
- Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.Medical sciences (Basel, Switzerland) · 2021Article
- Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia.Diseases (Basel, Switzerland) · 2021Article
- Targeting Epigenetics and Non-coding RNAs in Myocardial Infarction: From Mechanisms to Therapeutics.Frontiers in genetics · 2021Review
- A new clustering model based on the seminal plasma/serum ratios of multiple trace element concentrations in male patients with subfertility.Reproductive medicine and biologyArticle
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundNumerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratification of STEMI patients using machine learning algorithms.
methodsA total of 1355 STEMI patients who underwent percutaneous coronary intervention were enrolled in this study during 2015-2018. Unsupervised machine learning (consensus clustering) was applied to the present cohort to classify patients into different lipid expression phenogroups, without the guidance of clinical outcomes. Kaplan-Meier curves were implemented to show prognosis during a 904-day median follow-up (interquartile range: 587-1316). In the adjusted Cox model, the association of cluster membership with all adverse events including all-cause mortality, all-cause rehospitalization, and cardiac rehospitalization was evaluated.
resultsAll patients were classified into three phenogroups, 1, 2, and 3. Patients in phenogroup 1 with the highest Lp(a) and the lowest HDL-C and apoA1 were recognized as the statin-modified cardiovascular risk group. Patients in phenogroup 2 had the highest HDL-C and apoA1 and the lowest TG, TC, LDL-C and apoB. Conversely, patients in phenogroup 3 had the highest TG, TC, LDL-C and apoB and the lowest Lp(a). Additionally, phenogroup 1 had the worst prognosis. Furthermore, a multivariate Cox analysis revealed that patients in phenogroup 1 were at significantly higher risk for all adverse outcomes.
conclusionMachine learning-based cluster analysis indicated that STEMI patients with increased concentrations of Lp(a) and decreased concentrations of HDL-C and apoA1 are likely to have adverse clinical outcomes due to statin-modified cardiovascular risks.
trial registrationChiCTR1900028516 ( http://www.chictr.org.cn/index.aspx ).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.