Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement

• Published in: Journal of interventional cardiology Vol. 2022; pp. 1368878 - 9
• Main Authors: Evertz, Ruben, Lange, Torben, Backhaus, Sören J., Schulz, Alexander, Beuthner, Bo Eric, Topci, Rodi, Toischer, Karl, Puls, Miriam, Kowallick, Johannes T., Hasenfuß, Gerd, Schuster, Andreas
• Format: Journal Article
• Language: English
• Published: United States Hindawi 20-04-2022; John Wiley & Sons, Inc; Hindawi-Wiley
• Subjects: Aortic Valve - surgery; Aortic valve stenosis; Aortic Valve Stenosis - diagnostic imaging; Aortic Valve Stenosis - surgery; Artificial Intelligence; Comparative analysis; Humans; Imaging systems; Information management; Mortality; Risk Assessment; Risk factors; Stroke Volume; Transcatheter Aortic Valve Replacement - adverse effects; Ventricular Function, Left
• ISSN: 0896-4327; 1540-8183; 1540-8183
• DOI: 10.1155/2022/1368878

Background. Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS). Methods. Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed. Results. Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943–0.997) p=0.032; automated: HR 0.967 (95% CI 0.939–0.995) p=0.022) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938–0.999) p=0.043; automated: HR 0.963 [95% CI 0.933–0.995] p=0.024; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920–0.996) p=0.027; automated: HR 0.954 (95% CI 0.920–0.989) p=0.011). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p=0.21). Absolute values of LV volumes differed significantly between automated and manual approaches (p<0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient. Conclusion. Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR.