What's New

Last Posted: Nov 27, 2020

• A machine learning-based approach for the prediction of periprocedural myocardial infarction by using routine data.
  Wang Yao et al. Cardiovascular diagnosis and therapy 2020 Oct 10(5) 1313-1324
• Genetics, coronary artery disease, and myocardial revascularization: will novel genetic risk scores bring new answers?
  Hui Sonya Kit et al. Indian journal of thoracic and cardiovascular surgery : official organ, Association of Thoracic and Cardiovascular Surgeons of India 2018 Dec 34(Suppl 3) 213-221
• Effects of the ABCB1 C3435T single nucleotide polymorphism on major adverse cardiovascular events in acute coronary syndrome or coronary artery disease patients undergoing percutaneous coronary intervention and treated with clopidogrel: A systematic review and meta-analysis.
  Biswas Mohitosh et al. Expert opinion on drug safety 2020 Oct
• Artificial neural network-based prediction of prolonged length of stay and need for post-acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention.
  Kulkarni Hemant et al. European journal of clinical investigation 2020 Oct e13406
• Impact of the CYP2C19*17 allele on outcomes in patients receiving genotype-guided antiplatelet therapy after percutaneous coronary intervention.
  Lee Craig R et al. Clinical pharmacology and therapeutics 2020 Sep
• A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.
  Zhong Junda et al. Journal of healthcare engineering 2020 20208847144
• Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches.
  Xue Ying et al. Journal of clinical pharmacy and therapeutics 2020 Jul
• Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.
  Shindo Kazuhiro et al. Cardiovascular drugs and therapy 2020 May
• Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.
  Rayfield Corbin et al. The Journal of invasive cardiology 2020 May 32(5) E122-E129
• Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.
  Sampedro-Gómez Jesús et al. The Canadian journal of cardiology 2020 Feb
• International survey of patients undergoing percutaneous coronary intervention and their attitudes toward pharmacogenetic testing.
  Pereira Naveen L et al. Pharmacogenetics and genomics 2019 29(4) 76-83
• Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data.
  Limdi Nita A et al. The pharmacogenomics journal 2020 Feb
• A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study.
  Hsieh Meng-Hsuen et al. Annals of translational medicine 2019 Dec 7(23) 732
• Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.
  Ma Hua et al. Medical image analysis 2020 Jan 61101634
• Cost-Effectiveness of Multigene Pharmacogenetic Testing in Patients With Acute Coronary Syndrome After Percutaneous Coronary Intervention.
  Dong Olivia M et al. Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 2020 Jan 23(1) 61-73