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Journal article

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 (R(2) = 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
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s10554-022-02724-7
Publication website:
http://edoc.mdc-berlin.de/22163/1/22163oa.pdf

Authors

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Role:
Author
ORCID:
0000-0002-3068-8171
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Role:
Author
ORCID:
0000-0002-0299-2440


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Funder identifier:
10.13039/100010269
Grant:
209450/Z/17/Z
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Funder identifier:
10.13039/100014013
Grant:
104691
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Funder identifier:
10.13039/100000050
Grant:
U01HL117006-01A1
More from this funder
Funder identifier:
10.13039/501100013373


Publisher:
Springer
Journal:
The International Journal of Cardiovascular Imaging More from this journal
Volume:
38
Issue:
12
Pages:
2695-2705
Publication date:
2022-10-06
DOI:
EISSN:
1573-0743
ISSN:
1569-5794


Language:
English
Keywords:
Pubs id:
1282703
Local pid:
pubs:1282703
Source identifiers:
W4302288218
Deposit date:
2026-04-29
ARK identifier:
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