Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis

Soongu  Kwak; Russell J.  Everett; Thomas A. Treibel; Seokhun  Yang; Doyeon  Hwang; Taehoon  Ko; Michelle C Williams; Rong Bing; Trisha Singh; Shruti Joshi; Heesun  Lee; Whal  Lee; Yong-Jin  Kim; Calvin W.L.  Chin; Miho Fukui; Tarique A. Musa; Marzia Rigolli; Anvesha Singh; Lionel Tastet; Laura E. Dobson; Stephanie  Wiesemann; Vanessa M Ferreira; Gabriella Captur; Sahmin Lee; Jeanette Schulz-Menger; Erik B Schelbert; Marie-Annick Clavel; Sung-Jin Park; Tobias  Rheude; Martin  Hadamitzky; Bernhard L.  Gerber; David E Newby; Saul G Myerson; Phillipe  Pibarot; João L Cavalcante; Gerry P McCann; John P Greenwood; James C. Moon; Marc R Dweck; Seung-Pyo Lee

doi:10.1016/j.jacc.2021.05.047

Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis

Soongu Kwak, Russell J. Everett, Thomas A. Treibel, Seokhun Yang, Doyeon Hwang, Taehoon Ko, Michelle C Williams, Rong Bing, Trisha Singh, Shruti Joshi, Heesun Lee, Whal Lee, Yong-Jin Kim, Calvin W.L. Chin, Miho Fukui, Tarique A. Musa, Marzia Rigolli, Anvesha Singh, Lionel Tastet, Laura E. DobsonStephanie Wiesemann, Vanessa M Ferreira, Gabriella Captur, Sahmin Lee, Jeanette Schulz-Menger, Erik B Schelbert, Marie-Annick Clavel, Sung-Jin Park, Tobias Rheude, Martin Hadamitzky, Bernhard L. Gerber, David E Newby, Saul G Myerson, Phillipe Pibarot, João L Cavalcante, Gerry P McCann, John P Greenwood, James C. Moon, Marc R Dweck, Seung-Pyo Lee

Research output: Contribution to journal › Article › peer-review

Abstract

Using machine learning, we identified four CMR markers of myocardial damage (ECV%, RVEF, LGE%, and LVEDVi) as major predictors of post-AVR mortality. Each parameter demonstrated a distinct non-linear association with mortality in the random survival forest model, most notably an ECV%>27% being associated with increased risk. These markers significantly improved risk prediction when added to the prediction model based on clinical risk factors and also showed effective risk stratification when combined into the AS-CMR risk score. The results were externally validated in a large independent cohort. These myocardial damage markers may offer major potential in optimizing the timing of AVR.

Original language	English
Journal	Journal of the American College of Cardiology
Early online date	2 Aug 2021
DOIs	https://doi.org/10.1016/j.jacc.2021.05.047
Publication status	Published - 10 Aug 2021

Keywords

Aortic valve stenosis
magnetic resonance imaging
random survival forest

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https://www.jacc.org/doi/abs/10.1016/j.jacc.2021.05.047

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Kwak, S., Everett, R. J., Treibel, T. A., Yang, S., Hwang, D., Ko, T., Williams, M. C., Bing, R., Singh, T., Joshi, S., Lee, H., Lee, W., Kim, Y-J., Chin, C. W. L., Fukui, M., Musa, T. A., Rigolli, M., Singh, A., Tastet, L., ... Lee, S-P. (2021). Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis. Journal of the American College of Cardiology. https://doi.org/10.1016/j.jacc.2021.05.047

@article{24461cfb0bbc4dc085d72b145459949a,

title = "Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis",

abstract = "Using machine learning, we identified four CMR markers of myocardial damage (ECV%, RVEF, LGE%, and LVEDVi) as major predictors of post-AVR mortality. Each parameter demonstrated a distinct non-linear association with mortality in the random survival forest model, most notably an ECV%>27% being associated with increased risk. These markers significantly improved risk prediction when added to the prediction model based on clinical risk factors and also showed effective risk stratification when combined into the AS-CMR risk score. The results were externally validated in a large independent cohort. These myocardial damage markers may offer major potential in optimizing the timing of AVR.",

keywords = "Aortic valve stenosis, magnetic resonance imaging, random survival forest",

author = "Soongu Kwak and Everett, {Russell J.} and Treibel, {Thomas A.} and Seokhun Yang and Doyeon Hwang and Taehoon Ko and Williams, {Michelle C} and Rong Bing and Trisha Singh and Shruti Joshi and Heesun Lee and Whal Lee and Yong-Jin Kim and Chin, {Calvin W.L.} and Miho Fukui and Musa, {Tarique A.} and Marzia Rigolli and Anvesha Singh and Lionel Tastet and Dobson, {Laura E.} and Stephanie Wiesemann and Ferreira, {Vanessa M} and Gabriella Captur and Sahmin Lee and Jeanette Schulz-Menger and Schelbert, {Erik B} and Marie-Annick Clavel and Sung-Jin Park and Tobias Rheude and Martin Hadamitzky and Gerber, {Bernhard L.} and Newby, {David E} and Myerson, {Saul G} and Phillipe Pibarot and Cavalcante, {Jo{\~a}o L} and McCann, {Gerry P} and Greenwood, {John P} and Moon, {James C.} and Dweck, {Marc R} and Seung-Pyo Lee",

year = "2021",

month = aug,

day = "10",

doi = "10.1016/j.jacc.2021.05.047",

language = "English",

journal = "Journal of the American College of Cardiology",

issn = "0735-1097",

publisher = "ELSEVIER SCIENCE INC",

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Kwak, S, Everett, RJ, Treibel, TA, Yang, S, Hwang, D, Ko, T, Williams, MC, Bing, R, Singh, T, Joshi, S, Lee, H, Lee, W, Kim, Y-J, Chin, CWL, Fukui, M, Musa, TA, Rigolli, M, Singh, A, Tastet, L, Dobson, LE, Wiesemann, S, Ferreira, VM, Captur, G, Lee, S, Schulz-Menger, J, Schelbert, EB, Clavel, M-A, Park, S-J, Rheude, T, Hadamitzky, M, Gerber, BL, Newby, DE, Myerson, SG, Pibarot, P, Cavalcante, JL, McCann, GP, Greenwood, JP, Moon, JC, Dweck, MR & Lee, S-P 2021, 'Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis', Journal of the American College of Cardiology. https://doi.org/10.1016/j.jacc.2021.05.047

TY - JOUR

T1 - Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis

AU - Kwak, Soongu

AU - Everett, Russell J.

AU - Treibel, Thomas A.

AU - Yang, Seokhun

AU - Hwang, Doyeon

AU - Ko, Taehoon

AU - Williams, Michelle C

AU - Bing, Rong

AU - Singh, Trisha

AU - Joshi, Shruti

AU - Lee, Heesun

AU - Lee, Whal

AU - Kim, Yong-Jin

AU - Chin, Calvin W.L.

AU - Fukui, Miho

AU - Musa, Tarique A.

AU - Rigolli, Marzia

AU - Singh, Anvesha

AU - Tastet, Lionel

AU - Dobson, Laura E.

AU - Wiesemann, Stephanie

AU - Ferreira, Vanessa M

AU - Captur, Gabriella

AU - Lee, Sahmin

AU - Schulz-Menger, Jeanette

AU - Schelbert, Erik B

AU - Clavel, Marie-Annick

AU - Park, Sung-Jin

AU - Rheude, Tobias

AU - Hadamitzky, Martin

AU - Gerber, Bernhard L.

AU - Newby, David E

AU - Myerson, Saul G

AU - Pibarot, Phillipe

AU - Cavalcante, João L

AU - McCann, Gerry P

AU - Greenwood, John P

AU - Moon, James C.

AU - Dweck, Marc R

AU - Lee, Seung-Pyo

PY - 2021/8/10

Y1 - 2021/8/10

N2 - Using machine learning, we identified four CMR markers of myocardial damage (ECV%, RVEF, LGE%, and LVEDVi) as major predictors of post-AVR mortality. Each parameter demonstrated a distinct non-linear association with mortality in the random survival forest model, most notably an ECV%>27% being associated with increased risk. These markers significantly improved risk prediction when added to the prediction model based on clinical risk factors and also showed effective risk stratification when combined into the AS-CMR risk score. The results were externally validated in a large independent cohort. These myocardial damage markers may offer major potential in optimizing the timing of AVR.

AB - Using machine learning, we identified four CMR markers of myocardial damage (ECV%, RVEF, LGE%, and LVEDVi) as major predictors of post-AVR mortality. Each parameter demonstrated a distinct non-linear association with mortality in the random survival forest model, most notably an ECV%>27% being associated with increased risk. These markers significantly improved risk prediction when added to the prediction model based on clinical risk factors and also showed effective risk stratification when combined into the AS-CMR risk score. The results were externally validated in a large independent cohort. These myocardial damage markers may offer major potential in optimizing the timing of AVR.

KW - Aortic valve stenosis

KW - magnetic resonance imaging

KW - random survival forest

U2 - 10.1016/j.jacc.2021.05.047

DO - 10.1016/j.jacc.2021.05.047

M3 - Article

JO - Journal of the American College of Cardiology

JF - Journal of the American College of Cardiology

SN - 0735-1097

ER -

Markers of Myocardial Damage Predict Mortality in Patients with Aortic Stenosis

Abstract

Keywords

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