Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

ECG left ventricular hypertrophy in aortic stenosis: Relationship with cardiac structure, invasive hemodynamics, and long-term mortality

BACKGROUND

In aortic stenosis (AS), left ventricular hypertrophy (LVH) is the response to pressure overload and represents the substrate for a maladaptive cascade, the so-called AS-related cardiac damage. We hypothesized that in AS patients electrocardiogram (ECG) LVH not only predicts echocardiography LVH but also other noninvasive and invasive markers of cardiac damage and prognosis after aortic valve replacement (AVR).

METHODS

In 279 patients with severe AS undergoing ECG, echocardiography, and cardiac catheterization before AVR, the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score were assessed.

RESULTS

The mean left ventricular mass index was 109 ± 34 g/m2 , and 131 (47%) patients had echocardiography LVH. The areas under the receiver operator characteristics curve (AUC) for the Sokolow-Lyon index, the Cornell product, the Romhilt-Estes score, and the Peguero-Lo Presti score for the prediction of echocardiography LVH were 0.59, 0.70, 0.63, and 0.65. The Peguero-Lo Presti score had the numerically greatest AUC for the prediction of left ventricular end-diastolic pressure >15 mmHg, mean pulmonary artery wedge pressure >15 mmHg, pulmonary vascular resistance >3 Wood units, mean right atrial pressure >14 mmHg, and stroke volume index <31 mL/m2 . After a median follow-up of 1365 (interquartile range: 931-1851) days after AVR only the Peguero-Lo Presti score was significantly associated with all-cause mortality [hazard ratio: 1.24 (95% confidence interval: 1.01-1.54); per 1 mV increase; p = .045].

CONCLUSIONS

Among severe AS patients, the Peguero-Lo Presti score is associated with abnormalities in cardiac structure including LVH, invasive measures of cardiac damage, and long-term mortality after AVR.