ESC 2022: High-risk aortic stenosis could be picked up using AI

537
Geoffrey Strange

Late-breaking research presented at the European Society of Cardiology (ESC) annual congress (26–29 September, Barcelona, Spain) has demonstrated the use of an artificial intelligence diagnostic tool—ECHO IQ—in identifying patients with moderate-to-severe aortic stenosis.

Investigator Geoffrey Strange (University of Notre Dame, Fremantle, Australia) the principal investigator in the AI-ENHANCED AS trial told ESC attendees that the AI algorithm could be used in clinical practice to identify patients who may be at high risk of aortic stenosis and require further assessment for treatments such as transcatheter aortic valve implantation (TAVI) or surgical valve replacement. His findings were presented during a hot line session at the ESC meeting.

The proprietary AI-decision support algorithm (AI-DSA) used in the study was trained using data from the National Echo Database of Australia (NEDA), which contains more than 1,000,000 echocardiograms from over 630,000 patients and is linked to mortality information, Strange detailed. The algorithm was also trained to ensure all guideline-defined severe aortic stenosis was detected. Training was performed using 70% of the NEDA data, which were randomly selected.

Using the remaining 30% of NEDA data, the researchers compared five-year death rates in patients with the moderate-to-severe and severe aortic stenosis phenotypes with five-year death rates in patients without significant risk of severe aortic stenosis. Out of 179,054 individuals, the AI-DSA identified 2,606 (1.4%) with a moderate-to-severe phenotype and 4,622 (2.5%) with a severe phenotype. Of those with a severe phenotype, 3,566 (77.2%) met guideline criteria for severe aortic stenosis.

The five-year mortality rate was 56.2% in patients with the moderate-to-severe phenotype and 67.9% in those with the severe phenotype. Those without either phenotype (the reference group) had a 22.9% five-year mortality rate. Compared with the reference group, the age- and sex- adjusted odds ratio (OR) for all-cause mortality was 1.82 (95% confidence interval [CI] 1.63–2.02) and 2.80 (95% CI 2.57–3.06) for patients with the moderate-to-severe and severe phenotypes, respectively.

Within the severe aortic stenosis phenotype identified by the AI-DSA (4,622; 2.5%), those that met current guidelines (77%) had a five-year mortality of 69.1%. The additional population identified by the AI-DSA with a severe phenotype, but who do not meet current guidelines, had a mortality rate of 64.4%.

Commenting on the findings, Strange said: “This proprietary AI algorithm picks up patients with a high risk (and all patients within current guidelines) of dying within five years that may be missed by conventional definitions. The findings suggest that the AI algorithm could be used in clinical practice to alert physicians to patients who should undergo further investigations to determine if they qualify for aortic valve replacement.”

Strange concluded: “Given the rising prevalence of aortic stenosis and its impact on mortality, it is time to revisit the practice of watchful waiting and consider more proactive attempts to identify those at risk. More research is needed to determine if aortic valve replacement improves survival and quality of life in patients identified by the AI-DSA as having a high risk of mortality, but who do not meet current guideline definitions.”


LEAVE A REPLY

Please enter your comment!
Please enter your name here