Abstract
Objectives
The aim of this study was to evaluate the prognostic value of Cardiac-Magnetic-Resonance (CMR) feature-tracking derived global-longitudinal-strain (GLS) in a large multicenter population of patients with ischemic and non-ischemic dilated cardiomyopathy.
Background
Direct assessment of myocardial fiber deformation with GLS using echocardiography or CMR feature-tracking has shown promise in providing prognostic information incremental to ejection-fraction in single-center studies. Given the growing use of CMR for assessing individuals with left ventricular dysfunction, we hypothesized that feature-tracking derived GLS may provide independent prognostic information in a multi-center population of ischemic and non-ischemic dilated cardiomyopathy patients.
Methods
Consecutive patients at four US medical centers undergoing CMR with EF<50% and ischemic or non-ischemic dilated cardiomyopathy were included in this study. Feature-tracking GLS was calculated from 3 long axis cine-views. The primary endpoint was all-cause-death. Cox proportional hazards regression modeling was used to examine the association between GLS and death. Incremental prognostic value of GLS was assessed in nested-models.
Results
Of the 1012 patients in this study, 133 died during median follow-up of 4.4years. By Kaplan-Meier analysis, the risk of death increased significantly with worsening GLS tertiles (log-rank p<0.0001). Each 1% worsening in GLS was associated with an 89.1% increase risk-of-death after adjustment for clinical and imaging risk factors including ejection-fraction and late-gadolinium-enhancement (HR=1.891 per%; p<0.001). Addition of GLS in this model resulted in significant improvement in the C-statistic (0.628 to 0.867;p<0.0001). Continuous net-reclassification-improvement (NRI) was 1.148 (95%CI, 0.996-1.318). GLS was independently associated with death after adjustment for clinical and imaging risk factors (including ejection-fraction and late-gadolinium-enhancement) in both ischemic (HR=1.942 per%; p<0.001) and non-ischemic dilated cardiomyopathy subgroups (HR=2.101 per%; p<0.001).
Conclusions
CMR feature-tracking derived GLS is a powerful independent predictor of mortality in a multi-center population of patients with ischemic or non-ischemic dilated cardiomyopathy, incremental to common clinical and CMR risk-factors, including ejection-fraction and late-gadolinium-enhancement.
Keywords: cardiac magnetic resonance imaging, prognosis, mortality, cardiomyopathy, left ventricular function, global longitudinal strain, feature tracking
INTRODUCTION
Ejection fraction is the principal measure used in clinical practice to assess cardiac mechanics. It provides significant prognostic information and is used widely for many clinical and therapeutic decisions, particularly in patients with left ventricular dysfunction. More recently, direct assessment of myocardial fiber deformation with echocardiographic global longitudinal strain (GLS) imaging has shown promise in providing diagnostic and prognostic information that is incremental to ejection fraction(1,2).
Cardiac Magnetic Resonance (CMR) imaging has evolved into a major tool for assessment of patients with left-ventricular-dysfunction, providing precise measurements of ejection fraction and tissue characterization with late gadolinium enhancement (LGE)(3). LGE can help establish the underlying cause of left-ventricular-dysfunction and is a powerful predictor of adverse cardiovascular outcomes(3). Recent developments in CMR feature tracking techniques now allow assessment of GLS from standard cine-CMR images(4).
We have recently reported the prognostic association of GLS with mortality in a small population of mixed cardiomyopathy patients from a single center(5). However, the prognostic value of GLS in patients with ischemic vs non-ischemic cardiomyopathy is unknown. Moreover, the robustness of these associations as well as the variability of feature-tracking GLS measurements in a multicenter setting remains unclear. The aim of this study was to evaluate the prognostic value of CMR feature tracking derived GLS in a large multicenter population of patients with ischemic and non-ischemic cardiomyopathy undergoing CMR at several centers in the United States.
METHODS
Study Design
Four geographically diverse medical centers in the United States participated in this observational, multicenter study. The University of Illinois in Chicago served as the data-coordinating center using a cloud-based database (CloudCMR, www.cloudCMR.com) containing de-identified searchable data from consecutive patients with full DICOM datasets from the participating centers. Institutional review board approval was obtained at each center.
Study Population
Consecutive patients (n=1047) with an ejection fraction <50% and ischemic or non-ischemic dilated cardiomyopathy who had undergone clinical CMR in 2011 with both cine and late gadolinium enhancement (LGE) imaging formed the study population. A subgroup of patients from a single center in this study were used in a prior report(5). Patients with uninterpretable image quality for GLS assessment (n=35) were excluded leaving 1012 patients, which formed the study population. Baseline demographics were obtained by local site investigators at the time of the clinical study.
CMR Acquisition
Images were acquired with phased-array receiver coils according to the routine scan protocol at each site using a variety of scanners from all three major vendors (Siemens, Philips and General Electric) at both 1.5 and 3 Tesla. A typical protocol included steady-state free-precession cine images acquired in multiple short-axis and three long-axis views with short-axis views obtained every 1cm to cover the entire left ventricle. Typical temporal resolution of cine images was <45msec. LGE imaging was performed 10-15 minutes after Gadolinium contrast (0.15 mmol/kg) administration using a 2D segmented gradient echo inversion-recovery sequence in the same views used for cine-CMR. Inversion delay times were typically 280 to 360 ms.
CMR Analysis and GLS Assessment
The study site investigators analyzed images on locally available workstations and were blinded to follow-up data. Delayed enhancement was assessed as described previously(6-9). In brief, LGE was scored visually on a 17-segment model with a 5-point scale for each segment (0 = no LGE, 1 = 1% to 25%, 2 = 26% to 50%, 3 = 51% to 75%, 4 = 76% to 100%). LGE extent as a percentage of LV myocardium was calculated by summing the regional scores, each weighted by the LGE range midpoint (i.e., 1 = 13%, 2 = 38%, 3 = 63%, 4 = 88%) and dividing by 17. Left ventricular volumes and mass were manually quantified at the data coordinating center from short-axis cine images. At the data-coordinating center, endocardial left ventricular contours were manually traced (by a single physician who was blinded to patient information and outcomes) in all 3 long-axis cine views to derive GLS using the Qstrain feature tracking package (Medis Medical Imaging Systems, Leiden, the Netherlands) (Figure 1). In 100 randomly selected patients, a second blinded CMR physician measured GLS for assessment of inter-observer variability. In another 100 randomly selected patients, the same physician re-measured GLS in a blinded fashion for assessment of intra-observer variability.
Figure 1. Measurement of GLS.
Top panel shows a patient that survived and bottom panel shows a patient that subsequently died.
Follow-up
Patients were followed for the primary outcome of all cause mortality using the United States Social Security Death Index. Time to event was calculated as the period between the CMR study and death. Patients who did not experience the primary outcome were censored at the time of assessment.
Statistical Analysis
Normally distributed data were expressed as mean ± SD. Differences in baseline characteristics were compared with the use of ANOVA for continuous variables and the chi-squared test for dichotomous variables, as appropriate. Inter and intra observer variability was analyzed using the Bland-Altman method. Kaplan-Meier methods were used to evaluate the relationship between GLS and time to the primary outcome of all cause mortality. We used Cox proportional hazards regression modeling to examine the association between GLS and all cause mortality. All models were assessed for collinearity and proportional hazards assumption. For the multivariable models, clinical and imaging risk factors which were univariate predictors (at p≤0.20) were considered as covariates. There was no evidence of problematic strong collinearity between GLS and any of the co-variates in the multivariable models. The Variance Inflation Factor (VIF) was < 1.5 for all the co-variates in all the models. To assess the added prognostic value of GLS, the final model was compared with a model in which GLS was not included. Model discrimination was compared by calculating the C-statistic as well as the integrated discrimination improvement (IDI)(10). Formal risk reclassification analyses were conducted with calculation of continuous net reclassification improvement (NRI)(10). A p value of <0.05 was considered statistically significant. Analyses were performed using STATA (StataCorp, TX).
RESULTS
Patient Characteristics
Table 1 summarizes baseline patient characteristics stratified by tertiles of GLS. The mean age of the study population was 60(±16) years. Sixty-five percent of patients were male and 31% had diabetes mellitus. The mean ejection fraction was 33.7 ± 10.0%. Mean GLS for the population was -10.9%. Median GLS was -11.0% (interquartile range: -7.8 to -14.0 %). Five-hundred and five (49.9%) patients had an ischemic cardiomyopathy, and 507 (51.1%) had a non-ischemic dilated cardiomyopathy. Four-hundred and eighty four patients (47.8%) had an ischemic LGE pattern (involving the subendocardium) and 144 (14.2%) had a non-ischemic pattern (mid-myocardial or epicardial), with 384 (38.0%) having no LGE. The distribution of GLS with ejection fraction and LGE is shown in the supplementary figures.
Table 1.
Baseline characteristics stratified by tertiles of GLS.
| CHARACTERISTICS | Total | GLS <-13.0% | GLS -8.7 to -13.0% | GLS >-8.7% | P Value |
|---|---|---|---|---|---|
| Age (±SD), years | 59.8 (±15.7) | 56.3 (±16.6) | 60.2 (±14.7) | 62.8 (±15.1) | <0.001 |
| Male % | 65.3 | 66.7 | 62.5 | 66.9 | 0.427 |
| BMI (±SD), kg/m2 | 28.9 (±8.6) | 28.2 (±6.5) | 28.9 (±6.6) | 29.4 (±11.6) | 0.199 |
| Diabetes % | 30.6 | 24.9 | 30.2 | 36.7 | 0.004 |
| Hyperlipidemia % | 54.7 | 50.2 | 54.9 | 58.8 | 0.078 |
| Smoking % | 14.8 | 12.9 | 13.1 | 18.3 | 0.085 |
| Hypertension % | 66.7 | 61.1 | 66.8 | 72.2 | 0.010 |
| History of MI % | 28.9 | 26.1 | 25.4 | 35.1 | 0.010 |
| Ischemic CM % | 49.9 | 43.8 | 43.7 | 62.1 | <0.001 |
| Non-ischemic Dilated CM % | 50.1 | 56.2 | 56.3 | 37.9 | <0.001 |
| Aspirin % | 63.2 | 57.1 | 60.3 | 72.3 | 0.001 |
| Statin % | 55.9 | 53.5 | 56.4 | 57.8 | 0.544 |
| ACE inhibitor or ARB % | 66.2 | 57.0 | 70.5 | 70.0 | 0.001 |
| Beta Blocker % | 49.6 | 40.8 | 54.3 | 53.4 | 0.001 |
| Diuretic % | 44.6 | 31.8 | 48.8 | 53.0 | 0.001 |
| Heart Rate (±SD), beats/min | 76.5 (±16.5) | 70.4 (±13.8) | 77.5 (±16.3) | 81.3 (±17.4) | <0.001 |
| Systolic BP (±SD), mmHg | 123.1(±20.7) | 124.9(±20.8) | 124.7(±20.6) | 119.9(±20.3) | 0.010 |
| Diastolic BP (±SD), mmHg | 72.7(±13.5) | 72.0(±12.9) | 73.7(±13.3) | 72.4(±14.3) | 0.371 |
| LVEDV index (±SD), ml/m2 | 117.9 (±54.6) | 102.3 (±40.6) | 116.7 (±55.9) | 134.5 (±60.3) | <0.001 |
| LVESV index (±SD), ml/m2 | 67.1 (±44.2) | 48.7 (±26.8) | 67.1 (±42.2) | 85.2 (±51.9) | <0.001 |
| LV mass index (±SD), g/m2 | 113.7 (±26.4) | 110.6 (±27.0) | 112.9 (±52.1) | 117.5 (±45.1) | 0.850 |
| LGE present % | 62.1 | 51.7 | 59.5 | 74.9 | <0.001 |
| LGE extent (±SD), % | 8.4 (±10.1) | 6.3 (±8.4) | 7.4 (±9.3) | 11.6 (±11.6) | <0.001 |
| LVEF (±SD), % | 33.7 (±10.0) | 40.3 (±6.8) | 34.0 (±8.6) | 26.8 (±9.3) | <0.001 |
ACE=Angiotensin Converting Enzyme, ARB=Angiotensin Receptor Blocker, BMI=Body Mass Index, CM=Cardiomyopathy, GLS=Global Longitudinal Strain, LGE=Late Gadolinium Enhancement, LVEDV =Left Ventricular End Diastolic Volume Index, LVEF=Left Ventricular Ejection Fraction, LVESV =Left Ventricular End Systolic Volume Index, MI=Myocardial Infarction, SD=standard deviation.
Inter and Intra Observer Variability
Bland-Altman analysis of interobserver repeatability for GLS showed a bias of 0.16%. Ninety-five % limits of agreement were −2.08 to 2.40 % (Figure 2). Bland-Altman analysis of intraobserver repeatability for GLS showed a bias of –0.07 %. Ninety-five % limits of agreement were −1.59 to 1.45 % (Figure 2).
Figure 2. Bland-Altman analysis of GLS for interobserver (left panel) and intraobserver (right panel) variability.
Solid line represents the bias. Dashed lines represents the 95% limits of agreement.
Primary Outcome
Of the 1012 patients in the study, 133 (13.1%) died during a median follow-up of 4.4 years (interquartile range: 3.6-5.1 years).
Outcomes Stratified by GLS, LVEF and LGE
By Kaplan-Meier analysis, the risk of death increased significantly with worsening tertiles of GLS (log-rank p<0.0001) (Figure 3). LVEF as a continuous variable was significantly associated with death (HR=0.980, p=0.022). In other words every 1% decrease in LVEF was associated with a 2% increased risk of death. Kaplan-Meier analysis of patients with EF≤35% vs those with EF>35% stratified by the highest and lowest tertiles of GLS shows that mortality was significantly higher in patients in the poorest GLS tertile, irrespective of LVEF (Figure 4). Patients within the poorest GLS tertile and LVEF ≤35% had significantly reduced survival compared to those patients in the poorest GLS tertile with LVEF >35% (log-rank p=0.003). However, in patients within the best-preserved GLS tertile, survival was high and unaffected by LVEF (Figure 4).
Figure 3. Kaplan-Meier survival curves.
Patients are stratified by tertiles of GLS.
Figure 4. Kaplan-Meier survival curves.
Patients with EF≤35% vs those with EF>35% are stratified by the highest and lowest tertiles of GLS.
LGE extent was significantly associated with all-cause-mortality (HR=1.030 per %; p<0.001; 95% CI=1.015-1.050). Thus every 1% increase in LGE extent was associated with a 3% increased risk of death. Likewise, by Kaplan-Meier analysis, the presence of LGE was associated with a significantly increased risk of death (log-rank p=0.0001) (Figure 5). Kaplan-Meier analysis of patients with LGE vs those without LGE stratified by the highest and lowest tertiles of GLS shows that mortality was highest for patients in the poorest GLS tertile irrespective of LGE (Figure 6). Thus, amongst patients within the poorest GLS tertile, the presence of LGE did not significantly affect survival (log-rank p=0.328). Likewise for patients within the best preserved GLS tertile, presence of LGE did not significantly affect survival (log-rank p=0.379) (Figure 6).
Figure 5. Kaplan-Meier survival curves.
Patients are stratified by presence or absence of LGE.
Figure 6. Kaplan-Meier survival curves.
Patients with LGE vs those without LGE are stratified by the highest and lowest tertiles of GLS.
Multivariable Analysis and Incremental Prognostic Value
After adjustment for clinical and imaging risk factors, which were univariate predictors at p≤0.20 (age, body mass index, diabetes, hyperlipidemia, LV end-diastolic volume index, LGE extent, EF), GLS remained a significant independent predictor of death (HR=1.891 per %; p<0.001) i.e. each 1% worsening in GLS was associated with an 89.1% increase risk of death (Table 2). Addition of GLS into the model with clinical and imaging predictors resulted in significant increase in the C-statistic (from 0.628 to 0.870 p<0.0001) and an integrated discrimination improvement of 0.256 (95% CI, 0.189-0.329), with a continuous NRI of 1.148 (95% CI, 0.996-1.318). GLS remained a significant independent predictor of death (HR=1.890 per %; p<0.001) when presence of LGE was used instead of LGE extent in the multivariable model (Table 2).
Table 2.
Multivariable models of mortality with GLS adjusted to univariate clinical and imaging predictors (at p≤0.20) for the entire population.
| VARIABLES | Univariable | Multivariable Using LGE extent | Multivariable Using LGE presence | |||
|---|---|---|---|---|---|---|
| Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | |
| Age | 1.026 (1.014-1.038) | <0.001 | 1.014 (1.002-1.027) | 0.048 | 1.013 (1.001-1.026) | 0.049 |
| BMI | 1.010 (0.998-1.022) | 0.190 | 1.000 (0.990-1.009) | 0.954 | 1.000 (0.990-1.010) | 0.994 |
| Diabetes | 1.771 (1.255-2.499) | 0.001 | 1.574 (1.075-2.304) | 0.020 | 1.574 (1.074-2.308) | 0.020 |
| Hyperlipidemia | 1.210 (0.859-1.719) | 0.160 | 0.772 (0.512-1.164) | 0.217 | 0.759 (0.502-1.147) | 0.191 |
| Hypertension | 1.194 (0.885-1.737) | 0.192 | 0.905 (0.599-1.367) | 0.636 | 0.886 (0.585-1.342) | 0.567 |
| LVEDV index | 1.012 (1.001-1.022) | 0.049 | 0.999 (0.995-1.003) | 0.623 | 0.999 (0.995-1.003) | 0.578 |
| GLS | 1.347 (1.285-1.411) | <0.001 | 1.891 (1.546-2.313) | <0.001 | 1.890 (1.547-2.308) | <0.001 |
| LGE present | 2.253 (1.493-3.401) | <0.001 | - | - | 1.342 (0.872-2.065) | 0.182 |
| LGE extent | 1.030 (1.014-1.045) | <0.001 | 1.007 (0.990-1.024) | 0.430 | - | - |
| LVEF | 0.980 (0.964-0.997) | 0.022 | 1.005 (0.969-1.043) | 0.765 | 1.005 (0.969-1.042) | 0.787 |
BMI=Body Mass Index, GLS=Global Longitudinal Strain, LGE=Late Gadolinium Enhancement, LVEDV =Left Ventricular End Diastolic Volume Index, LVEF=Left Ventricular Ejection Fraction.
Prognostic Value of GLS in ischemic and non-ischemic dilated cardiomyopathy
By Kaplan-Meier analysis, the risk of death increased significantly with worsening tertiles of GLS (log-rank p<0.0001) in both the ischemic and non-ischemic dilated cardiomyopathy subgroups (Figure 7). In patients with ischemic cardiomyopathy, each 1% worsening in GLS was associated with a 94.2% increase risk-of-death after adjustment for clinical and imaging risk factors (HR=1.942 per%; p<0.001) (Table 3). Similarly in patients with non-ischemic dilated cardiomyopathy, GLS remained significantly associated with death after adjustment for clinical and imaging risk factors (HR=2.101 per%; p<0.001) (Table 4). GLS remained a significant independent predictor of death when presence of LGE was used instead of LGE extent in the multivariable models for both ischemic and non-ischemic cardiomyopathy subgroups (Tables 3 and 4).
Figure 7. Kaplan-Meier survival curves.
Ischemic cardiomyopathy (left panel) and non-ischemic dilated cardiomyopathy (right panel) patients are stratified by tertiles of GLS.
Table 3.
Multivariable models of mortality with GLS adjusted to univariate clinical and imaging predictors (at p≤0.20) for patients with ischemic cardiomyopathy.
| VARIABLES | Univariable | Multivariable Model Using LGE extent | Multivariable Model Using LGE presence | |||
|---|---|---|---|---|---|---|
| Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | |
| Age | 1.025 (1.006-1.044) | 0.008 | 1.022 (1.002-1.043) | 0.031 | 1.022 (1.002-1.041) | 0.029 |
| BMI | 1.011 (1.001-1.022) | 0.038 | 1.001 (0.991-1.012) | 0.787 | 1.002 (0.992-1.012) | 0.732 |
| Diabetes | 1.574 (1.018-2.435) | 0.041 | 1.949 (1.188-3.199) | 0.008 | 1.933 (1.183-3.160) | 0.009 |
| Hyperlipidemia | 1.351 (0.890-1.816) | 0.153 | 0.834 (0.483-1.438) | 0.513 | 0.837 (0.486-1.442) | 0.521 |
| LVEDV index | 1.010 (1.001-1.020) | 0.049 | 1.000 (0.994-1.006) | 0.986 | 1.000 (0.994-1.006) | 0.987 |
| GLS | 1.312 (1.234-1.395) | <0.001 | 1.942 (1.466-2.573) | <0.001 | 1.952 (1.475-2.584) | <0.001 |
| LGE present | 2.142 (1.283-3.001) | <0.001 | - | - | 1.684 (0.228-12.46) | 0.609 |
| LGE extent | 1.017 (1.005-1.029) | 0.042 | 1.005 (0.980-1.030) | 0.720 | - | - |
| LVEF | 0.978 (0.957-0.998) | 0.048 | 1.012 (0.965-1.062) | 0.720 | 1.010 (0.964-1.058) | 0.678 |
BMI=Body Mass Index, GLS=Global Longitudinal Strain, LGE=Late Gadolinium Enhancement, LVEDV =Left Ventricular End Diastolic Volume Index, LVEF=Left Ventricular Ejection Fraction.
Table 4.
Multivariable models of mortality with GLS adjusted to univariate clinical and imaging predictors (at p≤0.20) for patients with non-ischemic dilated cardiomyopathy.
| VARIABLES | Univariable | Multivariable Using LGE extent | Multivariable Using LGE presence | |||
|---|---|---|---|---|---|---|
| Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | Hazard Ratio (95% CI) | P Value | |
| Age | 1.024 (1.008-1.041) | 0.004 | 1.003 (0.985-1.022) | 0.717 | 1.002 (0.984-1.021) | 0.791 |
| Diabetes | 1.779 (0.996-3.175) | 0.061 | 1.107 (0.588-2.086) | 0.752 | 0.955 (0.510-1.789) | 0.885 |
| LVEDV index | 1.012 (1.002-1.022) | 0.048 | 0.998 (0.993-1.003) | 0.376 | 0.997 (0.992-1.002) | 0.283 |
| GLS | 1.402 (1.299-1.513) | <0.001 | 2.101 (1.546-2.854) | <0.001 | 2.135 (1.564-2.913) | <0.001 |
| LGE present | 2.514 (1.249-3.715) | 0.007 | - | - | 1.914 (1.092-3.355) | 0.023 |
| LGE extent | 1.057 (1.030-1.085) | <0.001 | 1.044 (1.015-1.073) | 0.002 | - | - |
| LVEF | 0.978 (0.958-0.997) | 0.020 | 0.981 (0.927-1.039) | 0.511 | 0.981 (0.926-1.040) | 0.525 |
GLS=Global Longitudinal Strain, LGE=Late Gadolinium Enhancement, LVEDV =Left Ventricular End Diastolic Volume Index, LVEF=Left Ventricular Ejection Fraction.
DISCUSSION
This study shows that GLS measured by feature tracking CMR is a powerful independent predictor of mortality, in a large multicenter population of patients with ischemic and non-ischemic dilated cardiomyopathy. We have demonstrated that this parameter provides prognostic information incremental to common clinical and CMR imaging risk factors including EF and LGE extent. GLS was an independent predictor of mortality in both ischemic and non-ischemic dilated cardiomyopathy subgroups. Thus these findings have potentially broad application. To the best of our knowledge this is the largest study validating the use of feature tracking CMR for prognostic assessment of patients with LV dysfunction. These findings may have significant implications for management decisions based on risk stratification of these individuals.
Long axis function in cardiac mechanics
Long axis function plays a fundamental role in cardiac mechanics - contributing to ventricular ejection by reducing long axis LV cavity size as the mitral annulus is pulled towards the apex(11,12). CMR has been used to measure the contribution of long axis function to overall stroke volume in normal subjects, elite athletes and patients with dilated cardiomyopathy(13). Theses studies suggest that as much as 60% of stroke volume maybe explained by long axis function. In diastole, the mitral annulus springs back to its equilibrium position moving around the column of blood passing through the mitral valve, thus aiding ventricular filling(11,12). Possibly because of their subendocardial location, the more longitudinal myocardial fibers seem to be exquisitely sensitive to disturbance by various pathologies, as evidenced by rapid reduction of mitral annular motion with ischemia induction in experimental models(11).
Assessment of long axis function
Since the cardiac apex is fixed with respect to the chest wall, long axis function was initially assessed by measuring changes in the position of the mitral annulus(12). These studies have used M-mode echocardiography, and more recently CMR, to directly follow the position of the mitral annulus and measure mitral annular plane systolic excursion(14-16). However, over the last few years, enormous interest has been generated by development of echocardiographic 2D speckle tracking techniques to assess long axis function in a more global manner by measuring GLS(1,2). There is now a large body of literature demonstrating the diagnostic and prognostic utility of echo derived GLS in various cardiovascular disorders(1,2). However, these echo strain techniques are dependent on attainment of good quality imaging(1,2). CMR feature-tracking provides an alternative means to obtain GLS in these patients albeit with some-limitations in those with CMR contraindications such as implanted-devices and severe claustrophobia. We have shown that additional measurement of feature tracking GLS in patients already undergoing CMR for evaluation of cardiomyopathy provides significant additive prognostic value.
CMR feature tracking
Recent development of CMR feature tracking technology shows promise in allowing measurement of longitudinal strain using routine cine images in the clinical setting(4). The underlying principle is based on recognition of ‘patterns of features’ or ‘irregularities’ in the image that are tracked and followed in successive frames(4). Importantly, this approach can be applied to routine cine CMR acquisitions thus avoiding the need for dedicated pulse sequences which are required for other specialized CMR strain techniques such as tagging, HARP (Harmonic Phase), DENSE (Displacement Encoding with Stimulated Echos) and SENC (Strain encoded CMR)(4). Similar to echo speckle tracking, there are inter-vendor differences in the exact algorithms used in CMR feature tracking. Moreover, measurements are not necessarily directly comparable between modalities, or with those based on dedicated CMR pulse sequences. Nevertheless, several recent studies comparing speckle tracking echo and CMR feature tracking have suggested good agreement(17,18).
CMR feature tracking GLS and prognosis
There is a significant and growing body of literature demonstrating the prognostic value of GLS derived using speckle tracking echo in patients with LV dysfunction(1,19-21). In contrast, data regarding CMR derived GLS and prognosis has been very limited. Buss and colleagues recently demonstrated that feature tracking derived GLS was an independent predictor of the composite endpoint of cardiac death, heart transplantation, and aborted sudden cardiac death in a small single center population of 210 dilated non-ischemic cardiomyopathy patients followed for a median of 5.3 years (22).
We have recently reported the prognostic association of GLS with mortality in 470 patients with mixed cardiomyopathy from a single center(5). The findings in the current study are consistent with these earlier observations and build on prior data by demonstrating the independent and incremental prognostic value of CMR feature tracking GLS in a multicenter population - with a significantly greater number of patients and hard events. A major strength of our study is that they were made in a large multicenter group of patients with both ischemic and non-ischemic dilated cardiomyopathy. Thus these findings have potentially broad application to these important patient groups and greatly expand the evidence base for using CMR derived GLS to assess prognosis.
Role of CMR in assessment of LV dysfunction
CMR has evolved into a major tool for diagnosis and prognostic assessment of patients with LV dysfunction by providing data on morphology, function, perfusion, viability and tissue characterization(3,7,8). It is the reference standard for measurement of ventricular volumes, mass, and function, allowing serial assessment of disease progression or treatment response in individual patients(3,7). CMR tissue characterization can sometimes help establish the underlying cause of LV dysfunction(3,7). LGE assessment allows prediction of the likelihood of functional recovery after revascularization, medical therapy or cardiac resynchronization(3,7). In addition, LGE is a powerful predictor of adverse cardiovascular outcome in patients with LV dysfunction(3,7). In this study we have now shown that GLS provides independent prognostic information in patients with LV dysfunction being evaluated by CMR. Moreover, this was incremental to standard clinical and CMR variables including LGE and EF. How this information will impact clinical care requires further study. However, it is interesting to note that we found that patients with relatively preserved GLS had very few adverse events regardless of whether their EF was above or below 35%. Given that current guidelines recommend ICD placement based primarily on an EF≤35%, it will be interesting to examine the role of GLS on sudden cardiac death in future studies.
Limitations
Baseline demographics were obtained by local site investigators at the time of the clinical study and were limited to the prespecified variables presented in this manuscript, which do not represent a comprehensive list of all possible prognostic markers. For example, plasma BNP levels were not routinely measured at the time of scanning and were not included in our predictive models. Left atrial volumes measurements were not performed.
Information regarding future treatments like revascularization or ICD placement would be interesting to report but were not available. Patients underwent standard clinical care as practiced in the United States with treatments and therapies determined by their physicians. However, this does not detract from the main findings of this study, that feature-tracking GLS is a powerful predictor of death in patients with ischemic and non-ischemic dilated cardiomyopathy, independent of common clinical and imaging markers available at the time of CMR. The findings are reflective of and applicable to patients being evaluated by CMR in daily clinical practice – particularly given the multicenter “real-world” nature of this study.
Since this is a CMR study, there is a degree of selection bias related to being able to undergo a CMR exam, resulting in exclusion of patients with severe symptoms, large body size, severe renal impairment, severe claustrophobia or those with pacemakers and ICDs.
Information regarding specific cardiovascular outcomes such as myocardial infarction, sudden death, transplantation, revascularization or hospitalization was not available. Follow-up data in this study was limited to the primary endpoint of all cause death and the cause of death was not known. However, many have argued that all–cause mortality is an extremely important and appropriate study endpoint because it is objective, clinically relevant and unbiased, which is often not the case for cardiac mortality or softer outcomes such as revascularization or hospitalization(21,23,24). In a seminal review of this subject Lauer et al argued that use of cardiac-death instead of all-cause death as an end point in clinical investigation is hazardous for many reasons: 1) data obtained from death certificates or from medical records are haphazard, biased and often grossly inaccurate; 2) determination of cause of death is inherently difficult owing to the presence of concurrent comorbid illnesses, a low autopsy rate and inadequate understanding of complex disease processes; 3)coronary artery disease maybe present and significant at the time of death, and yet not be the primary reason a patient dies. They concluded that the ultimate result of using specific causes of death as end points is that ‘softness’ is introduced into a study that otherwise would be based on the strength of the ‘hardest’ end point of all, namely all-cause mortality(24). We therefore believe that all-cause mortality is a highly important and valid primary end-point for this study.
In this study we prospectively decided to measure GLS only; because this measure has the largest and most robust body of prognostic data from echocardiography. Future studies are needed to address the role of feature tracking derived radial or circumferential strain. Phase-sensitive inversion recovery imaging was not routinely used and was not available on all scanners during the study period, which is reflective of real-world practice and SCMR recommendations(25). In this study we used a semi-quantitative assessment of LGE extent. There is no current consensus on the best method of LGE quantification in non-ischemic cardiomyopathy(26). However, semi-quantitative visual LGE assessment has been prognostically validated in prior studies of non-ischemic cardiomyopathy and is rapidly performed in daily practice in many clinical laboratories (27,28). Moreover, we also assessed binary presence or absence of LGE in this study. Presence of LGE by visual analysis has extensive prognostic validation in non-ischemic cardiomyopathy from numerous prior studies(26). GLS remained an independent predictor of death regardless of whether LGE was assessed in a binary or semi-quantitative manner.
In similar fashion to echo speckle tracking, there are algorithmic differences between various CMR feature tracking software platforms, which may result in differing values(4). Thus the applicability of our findings to other feature tracking vendors requires further investigation.
Conclusions
In this large multicenter study, GLS measured during routine cine-CMR is a powerful independent predictor of mortality in patients with ischemic and non-ischemic dilated cardiomyopathy, incremental to common clinical and imaging risk factors including EF and LGE. Each 1% worsening in GLS was associated with an 89.1% increase risk of death adjusted to clinical and imaging risk factors. To the best of our knowledge, this study is the largest CMR analysis of GLS in patients with LV dysfunction. The total number of hard events (n=133) in our population is significantly higher than prior CMR studies of GLS, which greatly increases the robustness of our findings. A major strength of these findings is that they were made in a large multicenter group of patients. Thus these observations have potentially broad application to these important patient groups. Our findings highlight the role of long-axis function in individuals with LV dysfunction and suggest that consideration should be given to measurement of this parameter in these patients. Future studies are warranted to explore the role of CMR feature tracking derived GLS in clinical decision making for these patients.
Supplementary Material
Clinical Perspectives.
Competency in Medical Knowledge
In this large multicenter study, GLS measured during routine cine-CMR is a powerful independent predictor of mortality in patients with ischemic and non-ischemic dilated cardiomyopathy, incremental to common clinical and imaging risk factors including EF and LGE.
Translational Outlook
Future studies are warranted to explore the role of CMR feature tracking derived GLS in clinical decision making for patients with ischemic and non-ischemic dilated cardiomyopathy. Given that current guidelines recommend ICD placement based primarily on an EF≤35%, it will be interesting to examine the association of feature tracking derived GLS with sudden cardiac death.
Acknowledgments
SOURCES OF FUNDING
Dr R.J. Kim was funded in part by an NIH grant (RO1-HL64726)
Dr Shenoy was funded by an NIH grant (K23HL132011-01)
Abbreviations
- CMR
Cardiac Magnetic Resonance
- GLS
Global Longitudinal Strain
- ICD
Implantable Cardioverter Defibrillator
- LGE
Late Gadolinium Enhancement
- LV
Left Ventricle
- NRI
Net Reclassification Improvement
Footnotes
DISCLOSURES
Drs R.J. Kim and R.M. Judd are inventors on a US patent on delayed-enhancement MRI owned by Northwestern University.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- 1.Kalam K, Otahal P, Marwick TH. Prognostic implications of global LV dysfunction: a systematic review and meta-analysis of global longitudinal strain and ejection fraction. Heart. 2014;100:1673–80. doi: 10.1136/heartjnl-2014-305538. [DOI] [PubMed] [Google Scholar]
- 2.Shah AM, Solomon SD. Myocardial deformation imaging: current status and future directions. Circulation. 2012;125:e244–8. doi: 10.1161/CIRCULATIONAHA.111.086348. [DOI] [PubMed] [Google Scholar]
- 3.Karamitsos TD, Francis JM, Myerson S, Selvanayagam JB, Neubauer S. The role of cardiovascular magnetic resonance imaging in heart failure. J Am Coll Cardiol. 2009;54:1407–24. doi: 10.1016/j.jacc.2009.04.094. [DOI] [PubMed] [Google Scholar]
- 4.Pedrizzetti G, Claus P, Kilner PJ, Nagel E. Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use. Journal of cardiovascular magnetic resonance. 2016;18:51. doi: 10.1186/s12968-016-0269-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Romano S, Judd RM, Kim RJ, et al. Association of Feature-Tracking Cardiac Magnetic Resonance Imaging Left Ventricular Global Longitudinal Strain With All-Cause Mortality in Patients With Reduced Left Ventricular Ejection Fraction. Circulation. 2017;135:2313–2315. doi: 10.1161/CIRCULATIONAHA.117.027740. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Wu E, Judd RM, Vargas JD, Klocke FJ, Bonow RO, Kim RJ. Visualisation of presence, location, and transmural extent of healed Q-wave and non-Q-wave myocardial infarction. Lancet. 2001;357:21–8. doi: 10.1016/S0140-6736(00)03567-4. [DOI] [PubMed] [Google Scholar]
- 7.Kim HW, Farzaneh-Far A, Kim RJ. Cardiovascular magnetic resonance in patients with myocardial infarction: current and emerging applications. J Am Coll Cardiol. 2009;55:1–16. doi: 10.1016/j.jacc.2009.06.059. [DOI] [PubMed] [Google Scholar]
- 8.Abbasi SA, Ertel A, Shah RV, et al. Impact of cardiovascular magnetic resonance on management and clinical decision-making in heart failure patients. Journal of cardiovascular magnetic resonance. 2013;15:89. doi: 10.1186/1532-429X-15-89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kim RJ, Wu E, Rafael A, et al. The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med. 2000;343:1445–53. doi: 10.1056/NEJM200011163432003. [DOI] [PubMed] [Google Scholar]
- 10.Pencina MJ, D’Agostino RB, Sr, D’Agostino RB, Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine. 2008;27:157–72. doi: 10.1002/sim.2929. [DOI] [PubMed] [Google Scholar]
- 11.Henein MY, Gibson DG. Long axis function in disease. Heart. 1999;81:229–31. doi: 10.1136/hrt.81.3.229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Henein MY, Gibson DG. Normal long axis function. Heart. 1999;81:111–3. doi: 10.1136/hrt.81.2.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Carlsson M, Ugander M, Mosen H, Buhre T, Arheden H. Atrioventricular plane displacement is the major contributor to left ventricular pumping in healthy adults, athletes, and patients with dilated cardiomyopathy. American journal of physiology Heart and circulatory physiology. 2007;292:H1452–9. doi: 10.1152/ajpheart.01148.2006. [DOI] [PubMed] [Google Scholar]
- 14.Rangarajan V, Chacko SJ, Romano S, et al. Left ventricular long axis function assessed during cine-cardiovascular magnetic resonance is an independent predictor of adverse cardiac events. Journal of cardiovascular magnetic resonance. 2016;18:35. doi: 10.1186/s12968-016-0257-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Zaky A, Grabhorn L, Feigenbaum H. Movement of the mitral ring: a study in ultrasoundcardiography. Cardiovascular research. 1967;1:121–31. doi: 10.1093/cvr/1.2.121. [DOI] [PubMed] [Google Scholar]
- 16.Romano S, Judd RM, Kim RJ, et al. Left Ventricular Long Axis Function Assessed using Cine-Cardiac Magnetic Resonance Imaging is an Independent Predictor of All Cause Mortality in Patients with Reduced Ejection Fraction: A Multicenter Study. Radiology. 2017 Sep 14;:170529. doi: 10.1148/radiol.2017170529. [Epub ahead of print] [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Amaki M, Savino J, Ain DL, et al. Diagnostic concordance of echocardiography and cardiac magnetic resonance-based tissue tracking for differentiating constrictive pericarditis from restrictive cardiomyopathy. Circ Cardiovasc Imaging. 2014;7:819–27. doi: 10.1161/CIRCIMAGING.114.002103. [DOI] [PubMed] [Google Scholar]
- 18.Padiyath A, Gribben P, Abraham JR, et al. Echocardiography and cardiac magnetic resonance-based feature tracking in the assessment of myocardial mechanics in tetralogy of Fallot: an intermodality comparison. Echocardiography. 2013;30:203–10. doi: 10.1111/echo.12016. [DOI] [PubMed] [Google Scholar]
- 19.Nahum J, Bensaid A, Dussault C, et al. Impact of longitudinal myocardial deformation on the prognosis of chronic heart failure patients. Circ Cardiovasc Imaging. 2010;3:249–56. doi: 10.1161/CIRCIMAGING.109.910893. [DOI] [PubMed] [Google Scholar]
- 20.Sengelov M, Jorgensen PG, Jensen JS, et al. Global Longitudinal Strain Is a Superior Predictor of All-Cause Mortality in Heart Failure With Reduced Ejection Fraction. JACC Cardiovascular imaging. 2015;8:1351–9. doi: 10.1016/j.jcmg.2015.07.013. [DOI] [PubMed] [Google Scholar]
- 21.Stanton T, Leano R, Marwick TH. Prediction of all-cause mortality from global longitudinal speckle strain: comparison with ejection fraction and wall motion scoring. Circ Cardiovasc Imaging. 2009;2:356–64. doi: 10.1161/CIRCIMAGING.109.862334. [DOI] [PubMed] [Google Scholar]
- 22.Buss SJ, Breuninger K, Lehrke S, et al. Assessment of myocardial deformation with cardiac magnetic resonance strain imaging improves risk stratification in patients with dilated cardiomyopathy. European heart journal cardiovascular Imaging. 2015;16:307–15. doi: 10.1093/ehjci/jeu181. [DOI] [PubMed] [Google Scholar]
- 23.Klem I, Shah DJ, White RD, et al. Prognostic value of routine cardiac magnetic resonance assessment of left ventricular ejection fraction and myocardial damage: an international, multicenter study. Circ Cardiovasc Imaging. 2011;4:610–9. doi: 10.1161/CIRCIMAGING.111.964965. [DOI] [PubMed] [Google Scholar]
- 24.Lauer MS, Blackstone EH, Young JB, Topol EJ. Cause of death in clinical research: time for a reassessment? J Am Coll Cardiol. 1999;34:618–20. doi: 10.1016/s0735-1097(99)00250-8. [DOI] [PubMed] [Google Scholar]
- 25.Schulz-Menger J, Bluemke DA, Bremerich J, et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. Journal of cardiovascular magnetic resonance. 2013;15:35. doi: 10.1186/1532-429X-15-35. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kuruvilla S, Adenaw N, Katwal AB, Lipinski MJ, Kramer CM, Salerno M. Late gadolinium enhancement on cardiac magnetic resonance predicts adverse cardiovascular outcomes in nonischemic cardiomyopathy: a systematic review and meta-analysis. Circ Cardiovasc Imaging. 2014;7:250–258. doi: 10.1161/CIRCIMAGING.113.001144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Cheong BY, Muthupillai R, Wilson JM, et al. Prognostic significance of delayed-enhancement magnetic resonance imaging: survival of 857 patients with and without left ventricular dysfunction. Circulation. 2009;120:2069–76. doi: 10.1161/CIRCULATIONAHA.109.852517. [DOI] [PubMed] [Google Scholar]
- 28.Masci PG, Doulaptsis C, Bertella E, et al. Incremental prognostic value of myocardial fibrosis in patients with non-ischemic cardiomyopathy without congestive heart failure. Circulation Heart failure. 2014;7:448–56. doi: 10.1161/CIRCHEARTFAILURE.113.000996. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.