eprintid: 10134293
rev_number: 27
eprint_status: archive
userid: 608
dir: disk0/10/13/42/93
datestamp: 2021-09-14 11:38:16
lastmod: 2024-10-26 13:45:30
status_changed: 2021-09-14 11:38:16
type: article
metadata_visibility: show
creators_name: Kwak, S
creators_name: Everett, RJ
creators_name: Treibel, TA
creators_name: Yang, S
creators_name: Hwang, D
creators_name: Ko, T
creators_name: Williams, MC
creators_name: Bing, R
creators_name: Singh, T
creators_name: Joshi, S
creators_name: Lee, H
creators_name: Lee, W
creators_name: Kim, Y-J
creators_name: Chin, CWL
creators_name: Fukui, M
creators_name: Al Musa, T
creators_name: Rigolli, M
creators_name: Singh, A
creators_name: Tastet, L
creators_name: Dobson, LE
creators_name: Wiesemann, S
creators_name: Ferreira, VM
creators_name: Captur, G
creators_name: Lee, S
creators_name: Schulz-Menger, J
creators_name: Schelbert, EB
creators_name: Clavel, M-A
creators_name: Park, S-J
creators_name: Rheude, T
creators_name: Hadamitzky, M
creators_name: Gerber, BL
creators_name: Newby, DE
creators_name: Myerson, SG
creators_name: Pibarot, P
creators_name: Cavalcante, JL
creators_name: McCann, GP
creators_name: Greenwood, JP
creators_name: Moon, JC
creators_name: Dweck, MR
creators_name: Lee, S-P
title: Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis
ispublished: pub
subjects: RFH
divisions: UCL
divisions: B02
divisions: D14
divisions: GA4
divisions: GA3
divisions: G17
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2021-08-10
date_type: published
publisher: ELSEVIER SCIENCE INC
official_url: https://doi.org/10.1016/j.jacc.2021.05.047
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1881226
doi: 10.1016/j.jacc.2021.05.047
lyricists_name: Captur, Gabriella
lyricists_name: Moon, James
lyricists_name: Treibel, Thomas Alexander
lyricists_id: GCAPT58
lyricists_id: JMOON31
lyricists_id: TATRE28
actors_name: Barczynska, Patrycja
actors_id: PBARC91
actors_role: owner
full_text_status: public
publication: Journal of the American College of Cardiology
volume: 78
number: 6
pagerange: 545-558
pages: 14
citation:        Kwak, S;    Everett, RJ;    Treibel, TA;    Yang, S;    Hwang, D;    Ko, T;    Williams, MC;                                                                                                                                     ... Lee, S-P; + view all <#>        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;  Al Musa, T;  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;   - view fewer <#>    (2021)    Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis.                   Journal of the American College of Cardiology , 78  (6)   pp. 545-558.    10.1016/j.jacc.2021.05.047 <https://doi.org/10.1016/j.jacc.2021.05.047>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10134293/1/Treibel_Markers%20of%20Myocardial%20Damage%20Predict%20Mortality%20in%20Patients%20With%20Aortic%20Stenosis.pdf