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. 2021 Aug 10;78(6):545-558.
doi: 10.1016/j.jacc.2021.05.047.

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

Soongu Kwak  1 , Russell J Everett  2 , Thomas A Treibel  3 , Seokhun Yang  1 , Doyeon Hwang  1 , Taehoon Ko  4 , Michelle C Williams  2 , Rong Bing  2 , Trisha Singh  2 , Shruti Joshi  2 , Heesun Lee  1 , Whal Lee  5 , Yong-Jin Kim  1 , Calvin W L Chin  6 , Miho Fukui  7 , Tarique Al Musa  8 , Marzia Rigolli  9 , Anvesha Singh  10 , Lionel Tastet  11 , Laura E Dobson  8 , Stephanie Wiesemann  12 , Vanessa M Ferreira  9 , Gabriella Captur  13 , Sahmin Lee  14 , Jeanette Schulz-Menger  12 , Erik B Schelbert  15 , Marie-Annick Clavel  11 , Sung-Ji Park  16 , Tobias Rheude  17 , Martin Hadamitzky  18 , Bernhard L Gerber  19 , David E Newby  2 , Saul G Myerson  9 , Phillipe Pibarot  11 , João L Cavalcante  15 , Gerry P McCann  10 , John P Greenwood  8 , James C Moon  3 , Marc R Dweck  20 , Seung-Pyo Lee  21
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Free article

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

Soongu Kwak et al. J Am Coll Cardiol. .
Free article

Abstract

Background: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined.

Objectives: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality.

Methods: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years' follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome.

Results: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort.

Conclusions: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR.

Keywords: aortic valve stenosis; magnetic resonance imaging; random survival forest.

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Conflict of interest statement

Funding Support and Author Disclosures The work was supported by a National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT; No. 2019R1A2C2084099) and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI18C2383). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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