Machine learning prediction of progressive subclinical myocardial dysfunction in moderate aortic stenosis

Mayooran Namasivayam; Thomas Meredith; David W M Muller; David A Roy; Andrew K Roy; Jason C Kovacic; Christopher S Hayward; Michael P Feneley

doi:10.3389/fcvm.2023.1153814

Machine learning prediction of progressive subclinical myocardial dysfunction in moderate aortic stenosis

Front Cardiovasc Med. 2023 May 30:10:1153814. doi: 10.3389/fcvm.2023.1153814. eCollection 2023.

Authors

Mayooran Namasivayam^{1

2

3}, Thomas Meredith^{1

2

3}, David W M Muller^{1

2}, David A Roy^{1

2}, Andrew K Roy¹, Jason C Kovacic^{1

2

4

5}, Christopher S Hayward^{1

2

6}, Michael P Feneley^{1

2

6}

Affiliations

¹ Department of Cardiology, St Vincent's Hospital, Sydney, NSW, Australia.
² Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
³ Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.
⁴ Vascular Biology Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.
⁵ Icahn School of Medicine at Mount Sinai, Cardiovascular Research Institute, New York, NY, United States.
⁶ Cardiac Mechanics Laboratory, Victor Chang Cardiac Research Institute, Sydney, NSW, Australia.

Abstract

Background: Moderate severity aortic stenosis (AS) is poorly understood, is associated with subclinical myocardial dysfunction, and can lead to adverse outcome rates that are comparable to severe AS. Factors associated with progressive myocardial dysfunction in moderate AS are not well described. Artificial neural networks (ANNs) can identify patterns, inform clinical risk, and identify features of importance in clinical datasets.

Methods: We conducted ANN analyses on longitudinal echocardiographic data collected from 66 individuals with moderate AS who underwent serial echocardiography at our institution. Image phenotyping involved left ventricular global longitudinal strain (GLS) and valve stenosis severity (including energetics) analysis. ANNs were constructed using two multilayer perceptron models. The first model was developed to predict change in GLS from baseline echocardiography alone and the second to predict change in GLS using data from baseline and serial echocardiography. ANNs used a single hidden layer architecture and a 70%:30% training/testing split.

Results: Over a median follow-up interval of 1.3 years, change in GLS (≤ or >median change) could be predicted with accuracy rates of 95% in training and 93% in testing using ANN with inputs from baseline echocardiogram data alone (AUC: 0.997). The four most important predictive baseline features (reported as normalized % importance relative to most important feature) were peak gradient (100%), energy loss (93%), GLS (80%), and DI < 0.25 (50%). When a further model was run including inputs from both baseline and serial echocardiography (AUC 0.844), the top four features of importance were change in dimensionless index between index and follow-up studies (100%), baseline peak gradient (79%), baseline energy loss (72%), and baseline GLS (63%).

Conclusions: Artificial neural networks can predict progressive subclinical myocardial dysfunction with high accuracy in moderate AS and identify features of importance. Key features associated with classifying progression in subclinical myocardial dysfunction included peak gradient, dimensionless index, GLS, and hydraulic load (energy loss), suggesting that these features should be closely evaluated and monitored in AS.

Keywords: LV dysfunction; aortic stenosis; echocadiography; machine learning; neural network.

Grants and funding

MN has received the National Heart Foundation of Australia Postdoctoral Fellowship, St Vincent's Clinic Foundation/St Vincent's Applied Medical Research Institute Clinician Grant, the New South Wales Ministry of Health Early-Mid Career Investigator Award, the Ramaciotti Foundation Health Investment Grant, and the Nvidia Corporation Academic Hardware Grant. MN's laboratory has received the Nvidia Corporation Academic Hardware Grant, but the company had no role in the study design, data collection, data analysis, or manuscript writeup. TM has received the Cardiac Society of Australia and New Zealand Research Scholarship and the National Heart Foundation of Australia PhD Scholarship. JK is the recipient of the Agilent Thought Leader Award (January 2022), which includes funding for research that is unrelated to the current manuscript.