Machine learning evaluation of LV outflow obstruction in hypertrophic cardiomyopathy using three-chamber cardiovascular magnetic resonance

Abstract

Left ventricular outflow tract obstruction (LVOTO) is common in hypertrophic cardiomyopathy (HCM), but relationships between anatomical metrics and obstruction are poorly understood. We aimed to develop machine learning methods to evaluate LVOTO in HCM patients and quantify relationships between anatomical metrics and obstruction. This retrospective analysis of 1905 participants of the HCM Registry quantified 11 anatomical metrics derived from 14 landmarks automatically detected on the three-chamber long axis cine CMR images. Linear and logistic regression was used to quantify strengths of relationships with the presence of LVOTO (defined by resting Doppler pressure drop of > 30 mmHg), using the area under the receiver operating characteristic (AUC). Intraclass correlation coefficients between the network predictions and three independent observers showed similar agreement to that between observers. The distance from anterior mitral valve leaflet tip to basal septum (AML-BS) was most highly correlated with Doppler pressure drop (R² = 0.19, p < 10^-5). Multivariate stepwise regression found the best predictive model included AML-BS, AML length to aortic valve diameter ratio, AML length to LV width ratio, and midventricular septal thickness metrics (AUC 0.84). Excluding AML-BS, metrics grouped according to septal hypertrophy, LV geometry, and AML anatomy each had similar associations with LVOTO (AUC 0.71, 0.71, 0.68 respectively, p = ns), significantly less than their combination (AUC 0.77, p < 0.05 for each). Anatomical metrics derived from a standard three-chamber CMR cine acquisition can be used to highlight risk of LVOTO, and suggest further investigation if necessary. A combination of geometric factors is required to provide the best risk prediction.

Keywords: Atlas shape analysis; Hypertrophic cardiomyopathy; LV outflow tract obstruction.

Fig. 1

The image analysis pipeline

Fig. 2

Anatomical metrics based on the collection of 14 landmarks. Panel A: Anatomical metrics defined as distances between landmarks (end-diastole). Panel B: The 14 landmarks in 3 key frames (different case from panel A, illustrating how image orientation is different between cases)

Fig. 3

Bar chart showing the intraclass correlation coefficients for the intra- and inter- observers and network performance. Bars represent the intraclass correlation coefficient and error bars represent the 95% CI

Fig. 4

Ability of anatomical markers to predict the presence of LVOTO. Model 1 includes the anatomy of the obstruction, i.e. the AML to BS distance, whereas Model 2 excludes it. The other three bars report the ability of individual mechanisms to predict the presence of obstruction. Error bars show 95% confidence interval in the AUC. *p < 0.05 between model AUCs