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.

Clinical Predictors of Mortality and Heart Failure Hospitalization in Patients With Severe Tricuspid Regurgitation.

Background: There is an incomplete understanding of the predictors of morbidity and mortality in patients with severe tricuspid regurgitation (TR). This study sought to identify key risk factors for all-cause mortality and heart failure (HF) hospitalization among patients with severe TR. Methods: Patients with severe TR were identified from 2 centers, Oregon Health & Science University and Abrazo Health, from January 01, 2016 to December 31, 2018. Patients with any concomitant severe valvular diseases or prior valvular intervention were excluded. Multivariable regression was utilized to identify demographic, clinical, and echocardiographic variables independently associated with all-cause mortality or HF hospitalization. Results: 435 patients with severe TR were followed for a median of 2.8 years. The mean age of the population was 66.9 ± 18.5 years and 58% were female. All-cause mortality was identified in 20.5% of the population. Of the cohort, 35.4% of patients were hospitalized for HF. Isolated tricuspid valve intervention was performed in 2.5% of patients. Independent predictors of all-cause mortality included history of solid tumor (odds ratio [OR] 6.6, 95% confidence interval [CI] 2.1-19.1, p = 0.001), history of peripheral artery disease (OR 3.5, 95% CI 1.2-9.4, p = 0.013), and elevated international normalized ratio in the absence of anticoagulation (OR 1.9, 95% CI 1.2-3.2, p = 0.008). Predictors of HF hospitalization included history of diabetes mellitus (OR 2.2, 95% CI 1.1-4.0, p = 0.014) and history of reduced left ventricular ejection fraction (OR 5.7, 95% CI 2.9-11.7, p < 0.0001). Conclusions: Severe untreated TR is associated with high mortality and frequent HF hospitalizations. Understanding predictors of these outcomes is important to identify patients who may benefit from early tricuspid valve intervention to help improve outcomes in this patient population.

Clinical Predictors of Mortality in Patients with Moderate to Severe Mitral Regurgitation.

BACKGROUND: Mitral regurgitation is the most common form of valvular heart disease worldwide, however, there is an incomplete understanding of predictors of mortality in this population. This study sought to identify risk factors of mortality in a real-world population with mitral regurgitation. METHODS: All patients with moderate or severe mitral regurgitation were identified at a single center from January 1, 2016 to August 31, 2017. Multivariate regression was performed to evaluate variables independently associated with all-cause mortality. RESULTS: A total of 490 patients with moderate (76.3%) or severe (23.7%) mitral regurgitation due to primary (20.8%) or secondary (79.2%) etiology were identified. The mean age was 66.7 years; 50% were male. At a median follow-up of 3.1 years, the incidence of all-cause mortality was 30.1%, heart failure hospitalization 23.1%, and mitral valve intervention 11.6%. Of 117 variables, multivariate analysis demonstrated 5 that were independently predictive of mortality: baseline creatinine (hazard ratio [HR] 1.2; 95% CI, 1.0-1.3; P = .02), right atrial pressure by echocardiogram (HR 1.3; 95% CI, 1.07-1.55; P = .008), hemoglobin (HR 0.65; 95% CI, 0.52-0.83; P = .001), hospitalization for heart failure (HR 1.6; 95% CI, 1.1-2.4; P = .015), and mitral valve intervention (HR 0.40; 95% CI, 0.16-0.83; P = .049). CONCLUSION: In this retrospective, pragmatic analysis of patients with moderate or severe mitral regurgitation, admission for heart failure exacerbation, elevated right atrial pressure, renal dysfunction, anemia, and lack of mitral valve intervention were independently associated with increased risk of all-cause mortality. Whether these risk factors may better identify select patients who may benefit from more intensive monitoring or earlier intervention should be considered in future studies.

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 using a rules-based algorithm to search the electronic health record 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, the electronic health record, 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: In 2 health institutions in the United States 60,145 patients had 77,215 echocardiograms. Moderate or severe aortic stenosis (AS) was identified at a rate of 9.1% of patients (5474 and 6910 echocardiograms) in this population. The precision and accuracy of the algorithm for the detection of moderate or severe AS was 92.9% and 98.6%, respectively. Thirty-five percent of patients (441/1265) with moderate stenosis and a subsequent echocardiogram progressed to severe stenosis (mean interval, 358 days). In 1 sample 70.3% of moderate AS patients lacked a 6-month echocardiogram or appointment. The platform enabled 100% accountability for all patients with severe AS. CONCLUSIONS: A rules-based software program enhances detection of heart valve disease and can be used to measures disease progression and care pathway compliance.