A novel cardiovascular magnetic resonance risk score for predicting mortality following surgical aortic valve replacement

Vassilios Vassiliou, Menelaos Pavlou, Tamir Malley, Brian P. Halliday, Vasiliki Tsampasian, Claire E. Raphael, Gary Tse, Miguel Silva Vieira, Dominique Auger, Russell J. Everett, Calvin W. L. Chin, Francisco Alpendurada, John R. Pepper, Dudley J. Pennell, David Newby, Andrew Jabbour, Marc R. Dweck, Sanjay K. Prasad

Research output: Contribution to journalArticlepeer-review

Abstract

The increasing prevalence of patients with aortic stenosis worldwide highlights a clinical need for improved and accurate prediction of clinical outcomes following surgery. We investigated patient demographic and cardiovascular magnetic resonance (CMR) characteristics to formulate a dedicated risk score estimating long-term survival following surgery. We recruited consecutive patients undergoing CMR with gadolinium administration prior to surgical aortic valve replacement from 2003 to 2016 in two UK centres. The outcome was overall mortality. A total of 250 patients were included (68 ± 12 years, male 185 (60%), with pre-operative mean aortic valve area 0.93 ± 0.32cm2, LVEF 62 ± 17%) and followed for 6.0 ± 3.3 years. Sixty-one deaths occurred, with 10-year mortality of 23.6%. Multivariable analysis showed that increasing age (HR 1.04, P = 0.005), use of antiplatelet therapy (HR 0.54, P = 0.027), presence of infarction or midwall late gadolinium enhancement (HR 1.52 and HR 2.14 respectively, combined P = 0.12), higher indexed left ventricular stroke volume (HR 0.98, P = 0.043) and higher left atrial ejection fraction (HR 0.98, P = 0.083) associated with mortality and developed a risk score with good discrimination. This is the first dedicated risk prediction score for patients with aortic stenosis undergoing surgical aortic valve replacement providing an individualised estimate for overall mortality. This model can help clinicians individualising medical and surgical care.

Trial Registration ClinicalTrials.gov Identifier: NCT00930735 and ClinicalTrials.gov Identifier: NCT01755936.
Original languageEnglish
Article number20183
JournalScientific Reports
Volume11
DOIs
Publication statusPublished - 12 Oct 2021

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