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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Welcome to Cleveland's Clinic Cardiac Consult, brought to you by the Sydell and Arnold Miller Family Heart, Vascular, and Thoracic Institute at Cleveland Clinic.
Edward Soltesz, MD, MPH:
Hi, I'm Dr. Ed Soltesz. I'm a heart surgeon here at the Cleveland Clinic and I'm the Lewis Endowed Chair in Cardiothoracic Surgery and the Surgical Director of the Kaufman Center for Heart Failure and Recovery.
The AATS this year was very exciting. We had a tremendous number of presentations, and abstracts, and papers on all aspects of heart transplant, ventricular assist device therapy and high-risk heart surgery. Particularly, I think it was exciting to see some of the presentations that focused on performing high-risk heart surgery with temporary mechanical circulatory support assist. This allows us to perform surgery in patients who otherwise would not necessarily be candidates for conventional cardiac surgery and otherwise would need to undergo transplant or LVAD therapy. Additionally, I presented our data on developing a model for predicting post-cardiotomy cardiogenic shock, particularly in patients with poor left ventricular function.
I think that one of the major issues surrounding operating on high-risk patients is the development of this syndrome called post-cardiotomy cardiogenic shock, which has about a 30% mortality rate. If we can get patients through the operation safely, in particularly the first 24 to 48 hours, their survival is excellent and they do very well.
My colleagues here, together with Dr. Gene Blackstone and some others, developed a machine learning risk model to predict post-cardiotomy cardiogenic shock pre-operatively so we have an objective way of identifying which patients are at high risk for this terrible complication postoperatively. Those patients, of course, would then have a little bit more careful focus on supporting their circulation, potentially with temporary circulatory support, during and after the operation.
This risk prediction model takes advantage of some of the machine learning techniques that Dr. Blackstone has developed over the years, and it is able to be embedded within the EMR of a hospital so that we have objective data at the time when we see patients before surgery. So again, this is one of many great presentations at the AATS that I'm highlighting, but I think overall we're very excited with some of the innovations that we've been able to see at this conference, so thank you very much, and good day.
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