Quantification of left ventricular mass by echocardiography compared to cardiac magnet resonance imaging in hemodialysis patients

All echocardiography and CMR data were acquired in the context of the Mineralocorticoid Receptor Antagonists in End-Stage Renal Disease (MiREnDa), which was a prospective, randomized, placebo-controlled, double-blind, parallel group, phase 2 intervention study investigating the efficacy and safety of spironolactone in hemodialysis patients. The study design, flow, and endpoints were reported previously [19, 20]. Briefly, patients were enrolled at 20 dialysis centers. Cardiac imaging was assessed in one of the three participating university centers (Frankfurt, Erlangen-Nuremberg, and Würzburg) according to predefined standardized procedures. The patients underwent three dialysis sessions per week. To avoid inaccuracies in measuring LVM, CMR and echocardiography were performed immediately after each other for all subjects on dialysis-free days. Data from CMR and echocardiography were collected after the placebo run-in phase and before randomization (week 0 visit). The coordinating university for the study was the University Hospital of Würzburg. The Center for Clinical Trials at the University Hospital of Würzburg was responsible for implementing the study and the statistical analyses. Blinded data assessment was performed by trained research staff [19, 20]. The data used in this analysis were collected before the double-blind treatment phase.

All patients underwent a comprehensive echocardiographic study by an experienced cardiologist blinded to CMR findings. The protocol based on the current ASE recommendations utilized linear measurements derived by 2D TTE-guided M-mode approach [5, 13]. LVM was calculated from the TTE measurements using the following equations:

ASE formula [5, 21]

Th formula [18]

Where LVIDd is the LV internal diameter at end-diastole, PWTd is the posterior wall thickness at end-diastole, and IVSTd is the interventricular septal thickness at end-diastole. LVM indexation to anthropometry was calculated by body surface area (BSA) using the Mosteller formula [22]. In addition, the indexation by height^2.7 was used [23]. In this case, the LVMI is marked accordingly as LVM/height^2.7.

CMR images were acquired using both 1.5 or 3 Tesla magnetic resonance imaging scanners. Imaging was performed using an electrocardiogram-gated, breath-hold, 2D, steady-state, free precession cine with contiguous, left ventricular short-axis stack of images acquired from above the base to below the apex of the left ventricle with a slice thickness of 8.0 mm, pixel size smaller than 1.4 mm × 1.4 mm, field of view ~ 320 mm (adapted to patient size), and temporal resolution < 50 ms.

Image analysis was performed using commercial software (Circle cvi42®). LV myocardial area slices were measured planimetrically by delineating endocardial and epicardial LV borders at end-diastole and end-systole, allowing calculation of end-diastolic and end-systolic volumes, as well as myocardial volumes. LVM was calculated by summing the short axis slice volumes at end-diastole. Papillary muscles and trabeculae mass (PMT) were included in the LV cavity volume. LVM was mainly indexed to BSA using the Mosteller formula (see above). LVH was defined as indexed LVM ± 2 standard deviations of CMR references [24].

Scans were analyzed by an experienced radiologist (J.D.) blinded to the results of echocardiography. To validate CMR data, a second independent radiologist analyzed a random subsample of 26 (13%) CMR scans according to the same predefined procedures [20].

Mahalanobis distance was used to detect outliers in the data set and data from one subject was excluded from analysis due to its strong statistical divergence from the other LVMI measurements in population. A further exclusion criterion was the absence of data from CMR or echocardiography. Continuous data were expressed as median (quartiles) and categorical data as frequencies (percentages). If data matched the assumption of normal distribution (Shapiro-Wilk test, p > 0.05), continuous data in independent groups in paired samples were compared by Student’s unpaired or paired t test (k = 2) or single factor variance analysis (k > 2 independent groups), otherwise Mann-Whitney U/Wilcoxon sign rank test (k = 2) or the Kruskal Wallis test (k > 2) was used. Distributions for categorical data were compared using χ² or Fisher’s exact test if appropriate. The degree of correlation and its confidence interval were computed using Concordance Correlation Coefficient [25]. The mean difference in LVMI between CMR and echocardiography was calculated according to the formula \( \overline{\varDelta} \) = \( \frac{1}{n}\sum \limits_{i=1}^n{\left( Echo- CMR\right)}_i \). Agreement between the CMR technique and echocardiographic methods was analyzed by the Bland-Altman plot. The bias was estimated by the mean difference between CMR and echocardiography. In the case of constant bias, evaluated by the regression line, we specified the 95% limits of agreement. The stratification into LVMI quartiles was based on CMR measurements of LVMI. To compare the diagnostic performance of both echocardiographic formulas, we performed receiver operating characteristics (ROCs) to calculate the area under the curve (AUC). To test its significance, we used the method by Hanley et al. If z ≥ 1.96, the null hypothesis AUC₁ = AUC₂ was rejected by means of the z-test with a significance level of 0.05 and the so-called true ROC areas taken as significantly different [26]. Cut-off values for classifying LVH status were calculated by the Youden index. A two-tailed p-value < 0.05 was considered significant. For this purely exploratory analysis, no corrections for multiple comparisons were made. Statistical analyses were conducted using SPSS Version 19.0 (IBM Corp., Armonk, NY, USA) and Excel 2016 (Microsoft Corp., Redmond, USA).

A total of 118 hemodialysis patients were enrolled in the MiREnDa trial. Valid data sets for CMR and echocardiographic were available for 95 patients (mean age: 60 (50.0–71.0) years; 76% males) with a mean body mass index of 26.3 kg/m². Patients had been on maintenance hemodialysis for a median duration of 42 (16.8–79.0) months, which was performed via an arteriovenous fistula in 93% of patients. Based on CMR measurements, LVH was present in 44% of patients (69% concentric and 31% eccentric hypertrophy). There was a high prevalence of hypertension (86%), whereas heart failure was infrequent (5%). Medical treatment included beta-blockers (67.4%), diuretics (54.7%), and angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker (52.6%). We found no significant differences between patients with and without LVH except for renin-angiotensin-system (RAS) blockade and ß-blocker intake (Table 1).

Echocardiographic and CMR parameters, including LVM and LVMI, are provided in Table 2. LVMI measured by echocardiography was significantly higher than that measured by CMR (p < 0.001 for both ASE and Th). LVMI according to CMR correlated only moderately with LVMI derived from both echocardiographic formulas (Table 3). Furthermore, both echocardiographic formulas significantly overestimated LVMI compared to CMR. The degree of LVMI overestimation by ASE was significantly greater than that of Th in the entire cohort and in patients with LVH, but not in those without LVH (Table 4). Compared to the \( \overline{\varDelta} \) LVMI between echocardiographic formulas and CMR according to LVMI quartiles, we found a significantly lower difference in higher (3rd and 4th) LVMI quartiles for the Th formula compared to the ASE formula, whereas the ASE formula indicated a significantly smaller mean difference in the first LVMI quartile. With regard to the change in \( \overline{\varDelta} \) LVMI according to LVMI quartiles, only the Th formula revealed diminishing \( \overline{\varDelta} \) LVMI with increasing LVMI quartile (1st quartile, 24.8 ± 15.0 g/m²; 2nd quartile, 19.4 ± 14.7 g/m²; 3rd quartile, 12.1 ± 13.1 g/m²; 4th quartile, 6.8 ± 17.4 g/m²; p < 0.001), whereas the ASE formula resulted in no significant difference between the quartile groups, suggesting a better performance of the Th formula with higher LVMI (Table 4, Fig. 1).

The agreement between LVMI derived from echocardiographic methods and CMR was further evaluated by the Bland-Altman analysis. The differences between LVMI were plotted against the average LVMI, which showed poor agreement between LVMI calculated by both echocardiographic formulas and the LVMI measured by CMR. Both echocardiographic formulas demonstrated systematic overestimation. However, in contrast to ASE, the Th formula had a constant bias that did not vary throughout the range of measurements. The 95% limits of agreement were 19.7 to 46.2 g/m². Conversely, the \( \overline{\varDelta} \) LVMI (ASE-CMR) had an ascending regression line with increasing LVMI, suggesting that the bias was related to the LVMI (Fig. 2).

The ROC-AUC analysis of diagnostic performance was good for both formulas. The AUC for Th was not statistically better than ASE (Th: 0.819 (0.737–0.901) vs. ASE: 0.808 (0.723–0.892), z = 0.039) [26]. The calculations for LVM/height^2.7 showed similar results (Th: 0.814 (0.730–0.898) vs. ASE: 0.801 (0.730–0.898), z = 0.047). The non-sex-specific cut-off value for the Th formula calculated by the Youden index was 83.5 g/m² and 42.1 g/m^2.7. Using this cut-off values, the prevalence of LVH was 68.4 and 49.5% respectively.