Risk for Mortality with Increasingly Severe Aortic Stenosis: An International Cohort Study

An Artificial Intelligence Algorithm for Detection of Severe Aortic Stenosis: A Clinical Cohort Study

Background

Identifying individuals with severe aortic stenosis (AS) at high risk of mortality remains challenging using current clinical imaging methods.

Objectives

The purpose of this study was to evaluate an artificial intelligence decision support algorithm (AI-DSA) to augment the detection of severe AS within a well-resourced health care setting.

Methods

Agnostic to clinical information, an AI-DSA trained to identify echocardiographic phenotype associated with an aortic valve area (AVA)<1 cm2 using minimal input data (excluding left ventricular outflow tract measures) was applied to routine transthoracic echocardiograms (TTE) reports from 31,141 U.S. Medicare beneficiaries at an academic medical center (2003-2017).

Results

Performance of AI-DSA to detect the phenotype associated with an AVA<1 cm2 was excellent (sensitivity 82.2%, specificity 98.1%, negative predictive value 9.2%, c-statistic = 0.986). In addition to identifying clinical severe AS cases, AI-DSA identified an additional 1,034 (3.3%) individuals with guideline-defined moderate AS but with a similar clinical and TTE phenotype to those with severe AS with low rates of aortic valve replacement (6.6%). Five-year mortality was 75.9% in those with known severe AS, 73.5% in those with a similar phenotype to severe AS, and 44.6% in those without severe AS. The AI-DSA continued to perform well to identify severe AS among those with a depressed left ventricular ejection fraction. Overall rates of aortic valve replacement remained low, even in those with an AVA<1 cm2 (21.9%).

Conclusions

Without relying on left ventricular outflow tract measurements, an AI-DSA used echocardiographic reports to reliably identify the phenotype of severe AS. These results suggest possible utility for this AI-DSA to enhance detection of severe AS individuals at risk for adverse outcomes.

Outcomes of patients with early calcific aortic valve disease detected by clinically indicated echocardiography

AIMS

Previous studies have demonstrated relatively slow rates of progression of early calcific aortic valve disease (CAVD), which encompasses aortic sclerosis (ASc) and mild aortic stenosis (AS). The potential evolution to clinically significant AS is unclear, and we therefore examined the long-term outcomes of patients with ASc and mild AS detected at the time of clinically indicated echocardiography.

METHODS AND RESULTS

Data from initial clinically indicated echocardiograms performed between 2010 and 2018 in patients aged ≥18 years were extracted and linked to nationally collected outcome data. Those with impaired right or left systolic ventricular function or other significant left-sided valve disease were excluded. A time to first event analysis was performed with a composite primary outcome of cardiovascular death and aortic valve intervention (AVI). Of the 13 313 patients, 8973 had no CAVD, 3436 had ASc, and 455 had mild AS. The remainder had moderate or worse stenosis. Over a median follow-up period of 4.2 (interquartile range 1.8-6.7) years (and after adjustment for age and sex), those with ASc were at greater risk of the primary outcome [hazard ratio (HR) 2.9, 95% confidence interval (CI) 2.1-4.0] and need for AVI (HR 26.8, 95% CI 9.1-79.1) compared with those with no CAVD. Clinical event rates accelerated after ∼5 years in those with mild AS.

CONCLUSION

Patients with ASc are >25 times more likely to require AVI than those with no CAVD, and follow-up echocardiography should be considered within 3-4 years in those with mild AS.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.