Abstract 17881: A Predictive Algorithm to Identify Late Gadolinium Enhancement Using Echocardiography

Abstract

Background: Late gadolinium enhancement (LGE) on cardiac MRI (CMR) correlates with fibrosis and arrhythmic risk, but cannot be used in several clinical settings compared the availability of echocardiography (TTE). Aim: To develop and evaluate a machine learning (ML) model that uses standard clinical and echocardiography (TTE) variables to identify LGE on paired CMR. Methods: Amongst 725 patients (mean age 56.9 ± 16.5 years, 40.3% female) with TTE and CMR within 3 months at our institution, 2018-2023, we derived and tested several ML models (recursive partitioning, random forest, boosted tree, neural networks, LASSO/Elastic Net) to identify LGE on CMR using age and 24 TTE features. Ten-fold cross validation was used for internal validation. Results: A total of 165 (23%) had the presence of LGE (40% ischemic, 60% nonischemic pattern). Of tested models, a boosted neural network performed best (validation area under the curve [AUC] = 0.98; Figure ). In the validation cohort, the algorithm had a sensitivity and specificity of 93.8% and 94.6% for identifying presence of LGE. Accuracy remained high regardless of gender (M vs. F, AUC, 0.987 vs. 0.988) or race (blacks vs. whites, AUC, 0.987 vs. 0.984) and was not related to time between studies (r = -0.06, p = 0.09). Accuracy was better for ischemic LGE (AUC = 0.94) than nonischemic LGE (AUC = 0.90). Left ventricular size, age, fractional shortening, and wall thickness, were amongst the top predictors of LGE presence in the model. Conclusions: In this single center study, a boosted neural network using TTE features identified LGE on paired CMR with high sensitivity and specificity. If confirmed in external data, this suggests possible utility of ML for improving the diagnostic value of TTE for LGE assessment.