Connecting Cardio & Cancer: Mitigating Cardiotoxicity with Optimized Treatment Strategies

05/02/24 • 18 min

As methods for early cancer detection improve and the number of cancer survivors rises, the innovative field of cardio-oncology has emerged to ensure that patients with chemotherapy or cancer-related cardiac dysfunction can be safely, and swiftly, treated for their cancer. Dr. Stephanie Feldman, a clinical cardiologist with focus on cardio-oncology at NewYork-Presbyterian/Weill Cornell Medicine, is one of a growing number of physicians advancing research and pushing care in this field forward with a multi-disciplinary, comprehensive approach to care. Dr. Feldman joins us to discuss the rare risks of immune checkpoint inhibitors, how genetic mutations could put patients at risk for arterial thromboembolism, and how the cardio-oncology field can optimize the course of cancer care for patients at risk for cardiovascular complications.

For more information visit nyp.org/Advances

For more information visit nyp.org/Advances

Previous Episode

The Surgical Robot: Advancing Medicine with Robot-Assisted Technology

With perspectives spanning hepatobiliary, gynecologic, and cardiac surgeries, NewYork-Presbyterian’s Dr. Jason Hawksworth (Columbia), Dr. Tamatha Fenster (Weill Cornell Medicine), and Dr. Arnar Geirsson (Columbia) describe how they each came to incorporate robotics into their practices. One of the biggest takeaways: robotic surgeries allow for more accuracy in minimally-invasive approaches; so patients experience shorter hospital stays and quicker recoveries, even after major procedures. But there are some limitations to robotic surgeries that the doctors are still navigating. Dr. Fenster discusses how there are haptics limitations in robotic surgery. As a result, she shares more about her innovative smartHER 3D MRI program that is addressing this issue and details how her and her team are developing a way of holographically projecting 3D MRIs over patients to help guide surgeons while they operate.

For more information visit nyp.org/Advances

Next Episode

Data Mining: Using Machine Learning for Predictive Neurocritical Care

Monitoring patients with aneurysmal rupture for delayed cerebral ischemia was historically a numbers game. It was difficult for doctors to predict outcomes in the weeks that followed their rupture, so at-risk patients could find themselves under observation in the ICU anywhere from 7 to 21 days. Dr. Soojin Park, Medical Director of Critical Care Data Science and AI at NewYork-Presbyterian/Columbia, knew there had to be a better way to monitor patients and predict outcomes. So, relying on her background in machine learning and leveraging vast amounts of data, Dr. Park developed the potentially game-changing Continuous Monitoring Tool for Delayed Cerebral Ischemia (or COSMIC) score. The score uses machine learning, and basic patient data that can be collected with equipment available at any hospital, to detect signals that more accurately assess risk, allowing doctors to treat each neurocritical patient with targeted care - ultimately improving outcomes and patient experience.