1Kerckhoff Klinik GmbH Abteilung für Kardiologie Bad Nauheim, Deutschland; 2Justus-Liebig-Universität Giessen Franz-Groedel-Institut (FGI) Bad Nauheim, Deutschland; 3Universitätsklinikum Gießen und Marburg GmbH Medizinische Klinik I - Kardiologie und Angiologie Gießen, Deutschland
Background:
Cardiac magnetic resonance-based feature tracking (CMR-FT) left ventricular (LV) strain has been shown to reflect early functional impairment, even when visual estimation of LV ejection fraction is normal, and to aid in conferring prognosis. However, there is conflicting evidence concerning the afterload dependence of strain and to what extent afterload hampers the ability of CMR-FT strain to predict prognosis. The non-geometric LV end-systolic afterload index (NGI) and the effective arterial elastance (Ea) are well-established metrics to estimate afterload non-invasively. The effective ventricular elastance index (Ees) has emerged as a reliable estimator of LV elastance, which reflects contractility.
Objective:
To correlate CMR-FT LV global longitudinal strain (GLS) and strain rate (GLSR) with afterload and contractility indices and to determine whether the prognostic value of GLS and GLSR is independent of loading conditions.
Methods:
Between April 2017 and December 2022, we prospectively enrolled consecutive patients with clinically indicated CMR over a wide range of indications and diagnoses in our BioCVI imaging registry. The combined endpoint was defined as all-cause mortality and heart failure hospitalisations. The hemodynamic indices were defined as follows:
Ea= end-systolic pressure (ESP) / stroke volume (SV)
NGI= ESP x LV end-systolic volume (LVESV) / LV mass
Ees= ESP / LVESV
Univariate regression analysis was used to examine the relationship between afterload/contractility and GLS/GLSR. Univariate and multivariable Cox regression analyses were used to examine the predictive power of GLS and GLSR. ANOVA was used to test differences between tertiles of Ea, Ees, and NGI.
Results
A total of 927 patients (333 females [35.9%], median age 61 years) were included in the follow-up analysis, with 40 patients (4.3%) experiencing the combined endpoint over a median follow-up period of 14 months. A subset of 225 (24.2%) patients were considered healthy, whereas 337 (36.4%) had ischemic heart disease and 365 (39.3%) had non-ischemic heart disease. Ea exhibited a modest but significant negative correlation with GLS (β=-0.507, p<0.001) and GLSR (β=‑0.551, p<0.001, Figure 1). Similarly, Ees displayed a modest negative correlation with GLS (β=-0.497, p<0.001) and GLSR (β=-0.537, p<0.001). NGI showed a modest positive correlation with GLS (β=0.497, p<0.001) and GLSR (β=0.460, p<0.001). GLS, GLSR, Ea, Ees, and NGI were all significantly predictive of the combined endpoint. In multivariable analysis only GLS (HR 1.14 [95% CI 1.05 – 1.22]) and GLSR (HR 12.22 [95% CI 2.18 – 68.53]) remained predictive of the combined endpoint.
Conclusion
Our study demonstrates that GLS and GLSR reflect contractility but are both dependent on afterload; however, both GLS and GLSR are independently predictive of all-cause mortality and heart failure hospitalisations.