Post-PCI Cardiac Patient Risk Algorithm Study

Share your details and we'll email you our validation study

Study with Mayo Clinic: JACC Cardiac Interventions

  • We share our peer-reviewed research studies via email. Enter yours, and we'll send it to you..
  • This field is for validation purposes and should be left unchanged.

Share:

Share on twitter
Share on linkedin
Share on facebook
Share on email

Post-PCI Cardiac Patient Risk Algorithm Study

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.

JACC Cardiovascular Interventions: Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention

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:

  • How machine learning models can help clinicians assess patient risk at different points on their clinical pathways;
  • Why AI the potential role for integrating machine learning into clinical practice
  • How random forest regression models (machine learning) were more predictive and discriminative than
    standard regression methods at identifying patients at risk for 180-day cardiovascular mortality and 30-day CHF rehospitalization,
  • Much, much more.


Get this peer-reviewed study today:

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.

Share:

Share on twitter
Share on linkedin
Share on facebook
Share on email