Advances in technology are poised to revolutionize value-based care throughout health care systems, and especially in the cardiology department. Non-invasive procedures, automated diagnostic tools, and more accurate imaging can all help improve risk assessment and treatment plan development.
Second-generation TAVR valves were a major topic of discussion at ACC 2016. These valves show promising results versus open-chest surgery for high and intermediate-risk patients. It is expected that further study will show the procedure is recommended for low-risk patients as well, making the cath lab an ever more important part of the cardiology department.
This article presents the results of a study on whether patients with left ventricular dysfunction and low aortic valve gradient should still be considered for TAVR. The study found that low aortic valve gradient is a driving force behind higher mortality rates for these patients. The study’s authors suggest these patients can still be considered for TAVR, but recommend more individualized risk assessment.
In a value-based world, health care providers rely on technology to improve efficiency without sacrificing quality of care. To test the limits of what technology can do in a clinical setting, the American College of Cardiology (ACC) recently began a competition for data scientists. Their goal is to spark a breakthrough in computer software’s ability to automatically process cardiac MRI data.
In this interview, two of ACC’s imaging experts—Prem Soman, MD, FACC and Victor Ferrari, MD, FACC—discuss how real-time automated MRI results might affect cardiac imaging. They also discuss whether artificial intelligence might play a role in the future of cardiac care, and how big data will affect the decisions cardiologists make.
For PCI patients, diagnostic imaging is vital for determining the appropriate level of care. When a previous PCI patient presents with new symptoms, clinicians rely on imaging to help weigh the risks of further intervention versus medication or therapy.
New imaging tools like fractional flow reserve (FFR) and intravascular ultrasound (IVUS) can provide more data to help guide decision-making, says author Randy Young. Young discusses the real-world implications of using FFR and IVUS, and how to overcome the challenges impeding widespread adoption of these imaging tools.
An interdisciplinary team at Johns Hopkins University is working on a diagnostic tool to help cardiologists weigh risk factors and determine appropriate levels of treatment. The team has developed a 3-D virtual heart model that is showing success in predicting patients’ risk of cardiac death due to arrhythmia.
This article explains the process the team used to develop and test their heart simulation, and how they plan to further improve their model.
As these articles demonstrate, data analysis is becoming a potent driver of better outcomes for cardiac patients. The new imaging tools and procedures just on the horizon will require new solutions for storing and analyzing massive amounts of data. Cardiology departments that pursue a single-database solution can be better equipped to take advantage of these breakthroughs.