Machine Learning Evaluation of LV Outflow Obstruction in Hypertrophic Cardiomyopathy Using Three-Chamber Cardiovascular Magnetic Resonance

TY - JOUR

T1 - Machine Learning Evaluation of LV Outflow Obstruction in Hypertrophic Cardiomyopathy Using Three-Chamber Cardiovascular Magnetic Resonance

AU - Sahota, Manisha

AU - Saraskani, Sepas

AU - Xu, Hao

AU - Li, Liandong

AU - Majeed, Abdul Wahab

AU - Hermida, Uxio

AU - Neubauer, Stefan

AU - Desai, Milind Y.

AU - Weintraub, William

AU - Desvigne-Nickens, Patrice

AU - Schulz-Menger, Jeanette

AU - Kwong, Raymond Y.

AU - Kramer, Christopher M.

AU - Young, Alistair

AU - Lamata de la Orden, Pablo

PY - 2022/8/30

Y1 - 2022/8/30

N2 - Purpose: 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.Methods: 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).Results: 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, pConclusions: 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.

AB - Purpose: 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.Methods: 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).Results: 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, pConclusions: 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.

M3 - Article

SN - 1569-5794

JO - INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING

JF - INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING

ER -