Ventricular deformation analysis in cardiac magnetic resonance imaging: Feature tracking a supplementary parameter to improve the risk stratification in patients with ischemic cardiomyopathy

Zielsetzung This study aims to evaluate the prognostic value of feature tracking (FT) derived cardiac magnetic resonance (CMR) strain parameters of both ventricles regarding cardiovascular mortality (CVM) and appropriate therapy (shock/antitachycardia pacing) in a population with ICD implantation in ischemic cardiomyopathy with reduced ejection fraction (EF).

Material und Methoden Ischemic cardiomyopathy (ICM) patients (n=242) who underwent CMR imaging prior ICD implantation between 2005 and 2016 were divided into subgroups with EF≤35% (n=188) and >35% (n=54). FT parameters were acquired from short-axis and two long axis (4-Chamber; left 2-Chamber) cine (SSFP- Sequences) views with dedicated software (cvi42, Circle Cardiovascular Imaging Inc., Calgary, Canada). Primary endpoint was composite of cardiovascular mortality and/or appropriate ICD therapy.

Ergebnisse Composite of CVM and appropriate therapy was present in 53 patients with EF≤35% and in 13 patients with EF>35%. Follow up took place for mean 1615 days (interquartile range 716- 2269 days). No differences were encountered for FT- or standard CMR LV-/RV- function parameters in subgroup EF≤35%. For EF>35% event and no-event cohorts showed significant differences in all FT parameters except for RV-GLS, whereas no significant differences for standard CMR LV-/RV- function parameters were encountered including LV-EF. Left-Vetricular Global Longitundial Strain (LV-GLS) and Right-Ventricular Global Radial Strain (RV-GRS) revealed the highest AUC in ROC-curve analysis for subgroup EF>35%. The combination of LV-GLS and RV-GRS showed sensitivity of 85% and a specificity of 76% for discrimination between event and no-event group.

Schlußfolgerungen The combination of impaired LV-GLS and RV-GRS seems to be an predictor of cardiovascular mortality and/or appropriate ICD therapy for a subgroup of ICD Patients with EF>35%. This yields the potential for optimization in clinical decision making.