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

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.

Enhanced Detection of Heart Valve Disease Using Integrated Artificial Intelligence at Scale

BACKGROUND

Undertreatment of heart valve disease creates unnecessary patient risk. Poorly integrated healthcare data systems are unequipped to solve this problem. A software program utilizing a rules-based algorithm to search the electronic health record (EHR) for heart valve disease among patients treated by healthcare systems in the United States may provide a solution.

METHODS

A software interface allowed concurrent access to Picture Archiving Communication Systems (PACS), the electronic health record (EHR) and other sources. The software platform was created to programmatically run a rules engine to search structured and unstructured data for identification of moderate or severe heart valve disease using guideline reported values. Incidence and progression of disease as well as compliance with a care pathway were assessed.

RESULTS

60,145 patients had 77,215 echocardiograms in two health institutions in the United States. Moderate or severe aortic stenosis (AS) was identified at a rate of 9.1% (5,474) of patients (6,910 echoes) in this population. The precision and accuracy of the algorithm for the detection of moderate or severe aortic stenosis was 92.9% and 98.6%, respectively. Thirty five percent (441/1,265) of patients with moderate stenosis and a subsequent echocardiogram progressed to severe (mean interval 358 days). In one sample, 70.3% of moderate AS patients lacked a 6-month echo or appointment. The platform enabled 100% accountability for all patients with severe aortic stenosis.

CONCLUSIONS

A rules-based software program enhances detection of heart valve disease and can be used to measures disease progression and care pathway compliance.