In this study collaboration with Mayo Clinic physicians, an algorithm developed by Medial EarlySign data scientists was used to identify cardiac patients at highest risk of complications and hospital readmission after undergoing percutaneous coronary intervention (PCI). This procedure, formerly known as angioplasty with stent, is frequently performed in U.S. hospitals.
Objectives: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after PCI.
Background: Contemporary risk models for event prediction after PCI have limited predictive ability. Machine learning has the potential to identify complex nonlinear patterns within datasets, improving the predictive power of models.
This peer-reviewed study in the Journal of American College of Cardiology’s (JACC) Cardiovascular Interventions gives healthcare systems, cardiologists, pharmaceutical companies and emergency room physicians, and intensive care teams insights on how machine learning and artificial intelligence (AI) are ideal tools to identify post-discharge cardiac patients who may be on high-risk trajectories.
Highlights of the study include:
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Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, Chad J. Zack, MD, MS, et al., JACC Cardiovasc Interv. 2019 Jul 22;12(14):1304-1311. doi: 10.1016/j.jcin.2019.02.035. Epub 2019 Jun 26.