• AATS: Machine Learning-Derived Risk Model to Predict Post-Cardiotomy Cardiogenic Shock in High-Risk Cardiac Surgery Patients

  • May 16 2024
  • Duración: 3 m
  • Podcast

AATS: Machine Learning-Derived Risk Model to Predict Post-Cardiotomy Cardiogenic Shock in High-Risk Cardiac Surgery Patients Por arte de portada

AATS: Machine Learning-Derived Risk Model to Predict Post-Cardiotomy Cardiogenic Shock in High-Risk Cardiac Surgery Patients

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  • The American Association for Thoracic Surgery hosted its 104th Annual Meeting in Toronto at the end of April. There were many outstanding presentations. Edward Soltesz, MD, MPH, Surgical Director of the Kaufman Center for Heart Failure and Recovery and the Program and Director for the Department of Thoracic and Cardiovascular Surgery residency and fellowship training programs, highlights the area of high-risk cardiac surgery with temporary mechanical circulatory support assist. As an expert in the field, he presented Machine Learning-Derived Risk Model to Predict Post-Cardiotomy Cardiogenic Shock in High-Risk Cardiac Surgery Patients.

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