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@article{Zaidi:2023:10.3389/fcvm.2023.1082778,
author = {Zaidi, HA and Jones, RE and Hammersley, DJ and Hatipoglu, S and Balaban, G and Mach, L and Halliday, BP and Lamata, P and Prasad, SK and Bishop, MJ},
doi = {10.3389/fcvm.2023.1082778},
journal = {Frontiers in Cardiovascular Medicine},
title = {Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events},
url = {http://dx.doi.org/10.3389/fcvm.2023.1082778},
volume = {10},
year = {2023}
}

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TY  - JOUR
AB  - Background: Machine learning analysis of complex myocardial scar patterns affords the potential to enhance risk prediction of life-threatening arrhythmia in stable coronary artery disease (CAD).Objective: To assess the utility of computational image analysis, alongside a machine learning (ML) approach, to identify scar microstructure features on late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) that predict major arrhythmic events in patients with CAD.Methods: Patients with stable CAD were prospectively recruited into a CMR registry. Shape-based scar microstructure features characterizing heterogeneous (‘peri-infarct’) and homogeneous (‘core’) fibrosis were extracted. An ensemble of machine learning approaches were used for risk stratification, in addition to conventional analysis using Cox modeling.Results: Of 397 patients (mean LVEF 45.4 ± 16.0) followed for a median of 6 years, 55 patients (14%) experienced a major arrhythmic event. When applied within an ML model for binary classification, peri-infarct zone (PIZ) entropy, peri-infarct components and core interface area outperformed a model representative of the current standard of care (LVEF<35% and NYHA>Class I): AUROC (95%CI) 0.81 (0.81–0.82) vs. 0.64 (0.63–0.65), p = 0.002. In multivariate cox regression analysis, these features again remained significant after adjusting for LVEF<35% and NYHA>Class I: PIZ entropy hazard ratio (HR) 1.88, 95% confidence interval (CI) 1.38–2.56, p < 0.001; number of PIZ components HR 1.34, 95% CI 1.08–1.67, p = 0.009; core interface area HR 1.6, 95% CI 1.29–1.99, p = <0.001.Conclusion: Machine learning models using LGE-CMR scar microstructure improved arrhythmic risk stratification as compared to guideline-based clinical parameters; highlighting a potential novel approach to identifying candidates for implantab
AU  - Zaidi,HA
AU  - Jones,RE
AU  - Hammersley,DJ
AU  - Hatipoglu,S
AU  - Balaban,G
AU  - Mach,L
AU  - Halliday,BP
AU  - Lamata,P
AU  - Prasad,SK
AU  - Bishop,MJ
DO  - 10.3389/fcvm.2023.1082778
PY  - 2023///
SN  - 2297-055X
TI  - Machine learning analysis of complex late gadolinium enhancement patterns to improve risk prediction of major arrhythmic events
T2  - Frontiers in Cardiovascular Medicine
UR  - http://dx.doi.org/10.3389/fcvm.2023.1082778
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000935453900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.frontiersin.org/articles/10.3389/fcvm.2023.1082778/full
UR  - http://hdl.handle.net/10044/1/106582
VL  - 10
ER  -

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