Utilizing artificial intelligence to predict the progression from moderate to severe aortic stenosis

Heart failure with a preserved ejection fraction (HFpEF) accounts for more than half of all heart failure cases and carries significant morbidity and mortality risk, especially after hospitalization1. Historically, management of the disease is largely focused on comorbidity management and lifestyle modifications, given that available therapies had not demonstrated sufficient impact on hospitalization burden or mortality. In recent years, however, the treatment paradigm for HFpEF has shifted, with a growing emphasis on SGLT2 inhibitors (SGLT2i). Originally developed to lower blood glucose levels in diabetic patients, several clinical trials have firmly established that SGLT2i is also beneficial for patients living with HFpEF2.

BACKGROUND Heart failure with a preserved ejection fraction (HFpEF) is characterized by frequent hospitalizations, and substantial mortality. While historically lacking effective therapies, newer treatments, including SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP-1 RA), have shown potential benefits. However, the clinical course of HFpEF under these therapies remains incompletely characterized,

On the heels of the exciting EARLY TAVR data and egnite’s Acute Valve Syndrome in Aortic Stenosis study, both led by Dr. Philippe Genereux, we’ve been asked by several cardiovascular (CV) administrators what a potential change in the management of asymptomatic severe aortic stenosis (SAS) patients could mean for their already busy heart teams.