Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods

Abstract

Aortic stenosis (AS) is the most commonly diagnosed valvular heart disease, and its prevalence increases with the aging of the general population. However, AS is often diagnosed at a severe stage, necessitating surgical treatment, due to its long asymptomatic period. The objective of this study was to analyze the frequency of AS in a population of cardiovascular patients using echocardiography (ECHO) and to identify clinical factors and features associated with these patient groups. We utilized machine learning methods to analyze 84,851 echocardiograms performed between 2010 and 2018 at the National Medical Research Center named after V.A. Almazov. The primary indications for ECHO were coronary artery disease (CAD) and hypertension (HP), accounting for 33.5% and 14.2% of the cases, respectively. The frequency of AS was found to be 13.26% among the patients (n = 11,252). Within our study, 1544 patients had a bicuspid aortic valve (BAV), while 83,316 patients had a tricuspid aortic valve (TAV). BAV patients were observed to be younger compared to TAV patients. AS was more prevalent in the BAV group (59%) compared to the TAV group (12%), with a p-value of <0.0001. By employing a machine learning algorithm, we randomly identified significant features present in AS patients, including age, hypertension (HP), aortic regurgitation (AR), ascending aortic dilatation (AscAD), and BAV. These findings could serve as additional indications for earlier observation and more frequent ECHO in specific patient groups for the earlier detection of developing AS.

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

“…A recent article has discussed screening for cardiovascular disease in women using AI [ 259 ], this being a subcategory analyzed by the authors within the wide range of areas in which AI has proven effective in screening (e.g., congenital disease screening from both ECG analysis [ 90 ] to heart sound analysis [ 91 ] and fetal ultrasound [ 93 ], screening for reduced fraction heart failure [ 109 ], screening for hypertension [ 138 ], screening for valvular disease [ 218 , 219 , 220 , 222 , 227 , 228 ], and screening for rare diseases such as Fabry disease [ 60 ]).…”

“…Artificial intelligence seems to be promising in valvular diseases; in this review, we focused our attention mainly on aortic diseases [ 216 , 217 , 222 , 223 , 224 , 225 , 228 ] and aortic dissection [ 226 , 227 ] as well as aortic aneurysm [ 229 ] and rheumatic diseases, focusing on mitral regurgitation [ 220 ].…”