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Last Posted: Jun 20, 2024

• CYP2C19 Genotype Is Associated With Adverse Cardiovascular Outcomes in Black Patients Treated With Clopidogrel Undergoing Percutaneous Coronary Intervention.
  Kayla R Tunehag et al. J Am Heart Assoc 2024 e033791
• Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention.
  Yanxu Liu et al. Sci Rep 2024 14(1) 13393
• Machine learning predictions of the adverse events of different treatments in patients with ischemic left ventricular systolic dysfunction.
  Wenjie Chen et al. Intern Emerg Med 2024
• Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.
  Kaveh Hosseini et al. Int J Cardiol 2024 409132191
• Cardiovascular outcomes in patients with homozygous familial hypercholesterolaemia on lipoprotein apheresis initiated during childhood: long-term follow-up of an international cohort from two registries.
  M Doortje Reijman et al. Lancet Child Adolesc Health 2024
• The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.
  Blazej Michalski et al. Cardiol J 2024
• 1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.
  Seyed Reza Razavi et al. Medicina (Kaunas) 2024 60(4)
• Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.
  Xiang Zhu et al. BMC Med Res Methodol 2024 24(1) 59
• Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.
  Amir Hossein Behnoush et al. Eur J Med Res 2024 29(1) 76
• Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study.
  Pin Zhang et al. JMIR Form Res 2024 8e48487
• Implementing a Pharmacogenomic-driven Algorithm to Guide Antiplatelet Therapy among Caribbean Hispanics: A non-randomized prospective cohort study.
  Héctor Nuñez-Medina et al. medRxiv 2023
• Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus.
  Panke Chen et al. BMC Cardiovasc Disord 2023 23(1) 585
• Comparison of machine-learning models for the prediction of 1-year adverse outcomes of patients undergoing primary percutaneous coronary intervention for acute ST-elevation myocardial infarction.
  Saeed Tofighi et al. Clin Cardiol 2023
• Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction.
  Constantine Tarabanis et al. Cardiovasc Digit Health J 2023 4(4) 126-132
• Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.
  Chieh-Ju Chao et al. J Invasive Cardiol 2023 35(6) E297-E311
• Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for 'big data'.
  Kai Ninomiya et al. Eur Heart J Digit Health 2023 4(3) 275-278
• Diagnostic Performance of Machine Learning-Derived Radiomics Signature of Pericoronary Adipose Tissue in Coronary Computed Tomography Angiography for Coronary Artery In-Stent Restenosis.
  Keyi Cui et al. Acad Radiol 2023
• Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome.
  Sarawuth Limprasert et al. Healthc Inform Res 2023 29(2) 120-131
• Genetic Testing Enables the Diagnosis of Familial Hypercholesterolemia Underdiagnosed by Clinical Criteria: Analysis of Japanese Early-Onset Coronary Artery Disease Patients.
  Hiroshi Miyama et al. Cardiol Res Pract 2023 20232236422
• Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.
  Ulrich Güldener et al. J Clin Med 2023 12(8)
• Role of artificial intelligence and machine learning in interventional cardiology.
  Shoaib Subhan et al. Current problems in cardiology 2023 101698
• Post-Percutaneous Coronary Intervention CYP2C19 Genotyping in an Irish population: The Potential Role in Identifying Clopidogrel Therapy Related Bleeding Risks.
  Bing Wei Thaddeus Soh et al. British journal of clinical pharmacology 2023
• Machine learning for prediction of bleeding in acute myocardial infarction patients after percutaneous coronary intervention.
  Xueyan Zhao et al. Therapeutic advances in chronic disease 2023 1420406223231158561
• Performance of machine learning-based coronary computed tomography angiography for selecting revascularization candidates.
  Zengfa Huang et al. Acta radiologica (Stockholm, Sweden : 1987) 2023 2841851231158730
• Machine learning improves mortality prediction in three-vessel disease.
  Xinxing Feng et al. Atherosclerosis 2023 3671-7
• Prevalence of Diabetes and Its Association with Atherosclerotic Cardiovascular Disease Risk in Patients with Familial Hypercholesterolemia: An Analysis from the Hellenic Familial Hypercholesterolemia Registry (HELLAS-FH).
  Boutari Chrysoula et al. Pharmaceuticals (Basel, Switzerland) 2023 16(1)
• Identification of Coronary Culprit Lesion in ST Elevation Myocardial Infarction by Using Deep Learning.
  Tseng Li-Ming et al. IEEE journal of translational engineering in health and medicine 2023 1170-79
• Pharmacogenetics of P2Y Receptor Inhibitors.
  Thomas Cameron D et al. Pharmacotherapy 2023
• AGTR1rs5186 Polymorphism Is Associated with the Risk of Restenosis after Percutaneous Coronary Intervention: A Meta-Analysis.
  Lv Feng et al. Journal of cardiovascular development and disease 2022 9(11)
• Implementation of an All-Day Artificial Intelligence-Based Triage System to Accelerate Door-to-Balloon Times.
  Wang Yu-Chen et al. Mayo Clinic proceedings 2022
• Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention.
  Bhattacharya Amartya et al. Physiological measurement 2022
• Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing.
  González-Juanatey Carlos et al. Journal of clinical medicine 2022 11(20)
• Evaluation of race and ethnicity disparities in outcome studies of CYP2C19 genotype-guided antiplatelet therapy.
  Nguyen Anh B et al. Frontiers in cardiovascular medicine 2022 9991646
• Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction.
  Zhou Jian et al. BioMed research international 2022 20228552358
• A novel 6-metabolite signature for prediction of clinical outcomes in type 2 diabetic patients undergoing percutaneous coronary intervention.
  Wang Xue-Bin et al. Cardiovascular diabetology 2022 21(1) 126
• Using machine learning to aid treatment decision and risk assessment for severe three-vessel coronary artery disease.
  Jie Liu et al. Journal of geriatric cardiology : JGC 2022 19(5) 367-376
• Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods.
  Li Yong et al. Cardiology research and practice 2022 20228758617
• Cost-effectiveness of CYP2C19-guided P2Y12 inhibitors in Veterans undergoing percutaneous coronary intervention for acute coronary syndromes.
  Dong Olivia M et al. European heart journal. Quality of care & clinical outcomes 2022
• Prediction of 3-year all-cause and cardiovascular cause mortality in a prospective percutaneous coronary intervention registry: Machine learning model outperforms conventional clinical risk scores.
  Calburean Paul-Adrian et al. Atherosclerosis 2022 35033-40
• Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention.
  Deng Lianxiang et al. BMC medical informatics and decision making 2022 22(1) 109

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