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

Kwak, Soongu; Everett, Russell J; Treibel, Thomas A; Yang, Seokhun; Hwang, Doyeon; Ko, Taehoon; Williams, Michelle C; Bing, Rong; Singh, Trisha; Joshi, Shruti; Lee, Heesun; Lee, Whal; Kim, Yong-Jin; Chin, Calvin W L; Fukui, Miho; Al Musa, Tarique; Rigolli, Marzia; Singh, Anvesha; Tastet, Lionel; Dobson, Laura E; Wiesemann, Stephanie; Ferreira, Vanessa M; Captur, Gabriella; Lee, Sahmin; Schulz-Menger, Jeanette; Schelbert, Erik B; Clavel, Marie-Annick; Park, Sung-Ji; Rheude, Tobias; Hadamitzky, Ma...     »

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.      «

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 acro...     »