Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.

ABSTRACT

Patients with moderate aortic stenosis (AS) have a greater risk of adverse clinical outcomes than that of the general population. How this risk compares with those with severe AS, along with factors associated with outcomes and disease progression, is less clear. We analyzed serial echoes (from 2017 to 2019) from a single healthcare system using Tempus Next (Chicago, Illinois) software. AS severity was defined according to American Heart Association/American College of Cardiology guidelines. Outcomes of interest included death or heart failure hospitalization. We used Cox proportional hazards models and logistic regression to identify predictors of clinical outcome and disease progression, respectively. From 82,805 echoes for 61,546 patients, 1,770; 914; 565; and 1,463 patients had no, mild, moderate, or severe AS, respectively. Both patients with moderate and those with severe AS experienced a similar prevalence of adverse clinical outcomes (p = 0.45) that was significantly greater than that of patients without AS (p <0.01). In patients with moderate AS, atrial fibrillation (hazard ratio 3.29, 95% confidence interval 1.79 to 6.02, p <0.001) and end-stage renal disease (hazard ratio 3.34, 95% confidence interval 1.87 to 5.95, p <0.001) were associated with adverse clinical outcomes. One-third of patients with moderate AS with a subsequent echo (139/434) progressed to severe AS within 1 year. In conclusion, patients with moderate AS can progress rapidly to severe AS and experience a similar risk of adverse clinical outcomes; predictors include atrial fibrillation and low left ventricular ejection fraction. Machine learning algorithms may help identify these patients. Whether these patients may warrant earlier intervention merits further study.