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Right ventricular dysfunction, late gadolinium enhancement, and female gender predict poor outcome in patients with dilated cardiomyopathy
International Journal of Cardiology 177 (2014) 429–435
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International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Right ventricular dysfunction, late gadolinium enhancement, and female gender predict poor outcome in patients with dilated cardiomyopathy Christina Doesch a,c,⁎, Désirée-Marie Dierks a, Dariusch Haghi a,c, Rainer Schimpf a,c, Jürgen Kuschyk a,c, Tim Suselbeck a,c, Stefan O. Schoenberg b,c, Martin Borggrefe a,c, Theano Papavassiliu a,c a b c
1st Department of Medicine, Cardiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Germany Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Germany DZHK (German Centre for Cardiovascular Research) partner site, Mannheim, Germany
a r t i c l e
i n f o
Article history: Received 21 December 2013 Received in revised form 31 July 2014 Accepted 15 September 2014 Available online 7 October 2014 Keywords: Dilated cardiomyopathy Cardiovascular magnetic resonance imaging Right ventricular ejection fraction Risk stratification
a b s t r a c t Aims: Dilated cardiomyopathy (DCM) shows a variable disease course and is associated with significant morbidity and mortality. So far, left ventricular function (LVF) is the major determinant for risk stratification. However, since it has shown to be a poor guide to individual outcome, we studied the prognostic value of cardiovascular magnetic resonance imaging (CMR) parameters, late gadolinium enhancement (LGE) and epicardial adipose tissue (EAT). Methods and results: 140 patients with DCM underwent late gadolinium enhancement (LGE) CMR. During a median follow-up of 3 years, 22 patients (16%) died and another 51 (36%) were hospitalized due to congestive heart failure (CHF). Female gender and right ventricular ejection fraction (RV-EF) below the median of 38% were independent predictors of all-cause mortality in multivariable analysis. In patients who were hospitalized due to CHF, RV-EF below the median of 38% was the only independent predictor in multivariable analysis. When patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF to predict mortality and cardiac morbidity remained unchanged. Looking at DCM patients who died during follow-up compared to those who were hospitalized due to CHF, the former presented with a higher prevalence of LGE as well as reduced indexed EAT. Conclusion: Female gender, RV-EF and the presence of LGE are of prognostic importance in patients with DCM. Therefore, the present study underlines the role of CMR as an important tool for risk stratification in patients with DCM. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Dilated cardiomyopathy (DCM) shows a variable disease course and is associated with significant morbidity and mortality [1–3]. So far, left ventricular ejection fraction (LV-EF) is the major determinant for risk stratification and the current guidelines recommend implantation of a defibrillator for primary prevention in symptomatic patients (New York Heart Association functional class II/III) with a LV-EF less than 35% [4,5]. However, LV-EF alone has shown to be a poor guide to outcome. Cardiovascular magnetic resonance imaging (CMR) is the gold standard for non-invasive, accurate, and reproducible assessment of left and right ventricular function, cardiac mass and morphology
[6]. Due to the use of late gadolinium enhancement (LGE) technique, CMR allows in vivo quantification of regions of replacement fibrosis in patients with DCM that has shown a good correlation with histological data [7]. In prior studies [7–10], the presence of LGE in patients with DCM was also associated with an unfavorable prognosis. Additionally, CMR allows the quantification of epicardial adipose tissue (EAT) that has been shown to be reduced in patients with DCM [11–13] and became also suspect to be associated with a poor prognosis [13]. Since one single parameter does not seem to be sufficient to predict the clinical outcome of patients with NICM, we studied the prognostic value of CMR parameters, LGE and EAT. 2. Methods
⁎ Corresponding author at: 1st Department of Medicine, Cardiology affiliated at the DZHK (German Centre for Cardiovascular Resarch) partner site Mannheim, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Tel.: + 49 621 383 2204; fax: + 49 621 383 3821. E-mail address: [email protected] (C. Doesch).
http://dx.doi.org/10.1016/j.ijcard.2014.09.004 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
2.1. Study population Between February 2003 and February 2011, 150 consecutive patients with DCM that underwent late gadolinium enhancement CMR to quantify left ventricular (LV) function and myocardial scarring as part of their routine clinical work-up were enrolled at our tertiary referral hospital.
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The diagnosis of DCM was based on the 1995 WHO/International Society and Federation of Cardiology criteria [14]. All patients had undergone coronary angiography and were classified as non-ischemic if they had no history of myocardial infarction or revascularization and no evidence of coronary artery stenoses N50% of 2 or more epicardial vessels or left main or proximal anterior descending coronary artery N50% [15]. Patients with a normal CMR-derived left ventricular ejection fraction (LV-EF N 55%) were not included in the study. Other exclusion criteria were standard contraindications to CMR examination. CMR examination was not possible in 5 (3.3%) patients due to claustrophobia and in 3 (2.0%) patients due to severe obesity. 2 (1.3%) patients were lost during follow-up so that the final study population consisted of 140 patients. Of these 140 patients, 19 patients were included in an earlier study [13] and are now reported with an extended follow-up. The study was approved by the local ethics committee and informed consent was obtained from all patients.
2.2. Image acquisition All studies were performed using 1.5 Tesla whole-body imaging systems (Magnetom Sonata and Avanto, Siemens Healthcare Sector, Erlangen, Germany). To evaluate functional parameters, electrocardiogram-gated cine images were acquired using a segmented steady-state free precession [fast imaging with steady-state precession (true-FISP)] sequence (time to echo/time of repetition 1.6/3.2 ms, temporal resolution 35 ms, in-plane spatial resolution 1.4 × 1.8 mm, slice thickness 8 mm, interslice gap 2 mm). For the assessment of the epicardial adipose tissue, we used a dark blood prepared T1-weighted multislice turbo spin-echo pulse sequence with a water suppression prepulse (time of repetition = 800 ms, time to echo = 24 ms, slice thickness = 6 mm, interslice gap = 2 mm, and field of view = 30 to 34 cm) in the same orientations as the short-axis images.
2.7. Statistical analysis Since we aimed to study to what extent CMR results, age and gender were associated with events, Cox proportional hazard regression models were constructed for age, gender and CMR parameters. To check the proportional hazard assumption for different categories, they were plotted against time to ensure that the curves were reasonably parallel. Those variables which appeared to be associated with events at a value of p ≤ 0.1 level in univariable analysis were eligible for multivariable analysis to predict hospitalization for CHF. Forward stepwise logrank regression (p b 0.2 for entry, p N 0.1 for removal) was used. Due to the limited number of all-cause mortality events, the number of candidate parameters for multivariable analysis was limited to two (the clinical and CMR parameter with the best performance in univariable analysis) to avoid model overfitting. Results are presented as hazard ratios with 95% confidence intervals (CIs). The follow-up duration was measured from the CMR study date. The performance of the final models was assessed with respect to discrimination. Discrimination is the model's ability to separate patients with different outcomes. To quantify the discrimination, we used the c-statistic (Harrell's C) [19]. The maximum value of the c-statistic is 1.0; indicating a perfect prediction model. A value of 0.5 indicates that patients are correctly classified in 50% of the cases, e.g. as good as chance. The performance of a prediction model is generally worse in new patients than initially expected. This “optimism” can be studied with internal validation techniques [20]. Internal validity of our models was assessed with standard bootstrapping techniques [20]. The c-statistic of the final multivariable model, corrected for optimism, was reported. Kaplan–Meier analysis was performed for the independent predictors of all-causemortality. For this analysis the study population was divided into two groups according to the median value of the entire study population. Difference in survival over time was evaluated by a log-rank test. Analysis was performed using SPSS statistical software (version 14.0, SPSS Inc., Chicago, Illinois), Stata version 11 (StataCorp), and R software (version 2.8.1, R foundation for statistical computing, Vienna, Austria).
2.3. LGE Ten minutes after contrast agent injection (BW gadoterate meglumine, Dotarem, Guerbet, France), late gadolinium enhancement (LGE) images were acquired. An inversion time (TI) scout was performed to choose the optimal TIs between 200 and 360 ms. LGE images were assessed using either an inversion recovery Turbo FLASH 2D sequence: field of view 300–340 mm, TR 9.56 ms, TE 4.38 ms, flip angle 25°; matrix 166 × 256 and slice thickness 6 mm or a phase-sensitive inversion recovery TrueFISP sequence [16]: field of view 290 mm × 260 mm, TR 2.2 ms, TE 1.1 ms, flip angle 50°; matrix, 140 × 192 and slice thickness 6 mm, in-plane resolution 1.4 × 1.9 × 6 mm. LGE was only considered to be present if it was also present in the same slice after swapping phase encoding, thus excluding artifacts.
3. Results 140 patients with DCM (77% men, mean age 59.2 ± 13.9) were included in the study. The baseline clinical characteristics are presented in Table 1. Most patients presented with symptomatic heart failure (NYHA N I). Median follow-up was 3 years (interquartile range 0.5– 5.0 years). 3.1. Outcome
2.4. Image analysis Image analysis and quantitative analysis were performed off-line using dedicated software (ARGUS, Siemens, Germany). The readers were blinded to patient data and outcome. On the four-chamber view, the tricuspid annular plane systolic excursion (TAPSE) was calculated as previously described [17]. The amount of EAT was determined according to the method described by Fluechter S et al. [18].
2.5. Extent of LGE The extent of LGE was assessed visually by two independent experienced readers. LGE was only considered to be present if it was also present in the same slice after swapping phase encoding, thus excluding artifacts. The pattern of LGE was characterized as midwall, patchy foci, epicardial, or diffuse [9,10]. For quantification of fibrosis, LGE was defined as areas with a signal intensity N 2 standard deviation (SD) above mean signal intensity of remote myocardium in the same short-axis slice [8]. Areas were measured by planimetry and expressed as percentage of the myocardial area using the VPT tool (Siemens Healthcare Systems Erlangen, Germany).
2.6. Follow-up data and definition of study endpoints The long-term follow-up was performed by patient interview at our outpatient clinic and by telephone contact. The observers were unaware of the CMR results and collected data with a standardized questionnaire. Reported clinical events were confirmed by review of the corresponding medical records in our electronic Hospital Information System, contact with the general practitioner, referring cardiologist, or the treating hospital. The definition of cardiac event required the documentation of significant ventricular arrhythmia or cardiac arrest or death attributable to congestive heart failure or myocardial infarction in the absence of any other precipitating factor. In case of out-of-hospital death not followed by autopsy, sudden unexpected death was classified as cardiac death. The primary study endpoint was a combined endpoint of allcause mortality including non cardiac and cardiac death as well as heart transplantation (HTX). The secondary endpoint was hospitalization due to worsening of CHF. Patients who were hospitalized and died over the course of follow-up were only counted regarding the primary endpoint and not the secondary endpoint.
During the follow-up, 22 (16%) patients died. Thereof 15 (11%) patients suffered cardiac death, 1 (0.7%) patient underwent HTX and non cardiac death was reported in 6 (4%) patients. 51 (36%) were hospitalized due to congestive heart failure (CHF). 67 (48%) showed an event-free survival. Table 1 Baseline demographic and clinical characteristics. All DCM patients n = 140 Male n (%) Age (yrs) NYHA functional class • I • II • II • IV Atrial fibrillation n (%) Family history of DCM n (%) Hypertension n (%) Smoking n (%) Hyperlipidemia n (%) Diabetes n (%) Medication n (%) • Beta-blocker • ACEI • ARB • Spironolactone • Diuretics • Digoxin • Amiodarone
108 (77.1) 59.3 ± 13.8 7 (5.0) 26 (18.6) 64 (45.7) 43 (30.7) 54 (38.6) 8 (5.7) 46 (32.9) 20 (14.3) 41 (29.3) 31 (22.1) 118 (84.3) 113 (80.7) 21 (15.0) 34 (24.3) 100 (71.4) 38 (27.1) 12 (8.6)
Abbreviations: ACEI: angiotensin-converting-enzyme inhibitor, ARB: angiotensin II receptor blockers, CHF: congestive heart failure, n: number, DCM: dilated cardiomyopathy, NYHA: New York Heart association functional class, yrs: years
C. Doesch et al. / International Journal of Cardiology 177 (2014) 429–435
3.2. Demographic, clinical and CMR parameters of all patients with DCM
Table 3 Cox proportional hazard analysis for the time to the occurrence of all-cause mortality.
Tables 1 and 2 show the demographic, clinical and CMR parameters in all patients with DCM.
3.3. Predictors of all-cause mortality compared to event-free survival By univariable analysis, significant associations were observed between age, female gender, RV-EF, TAPSE, RV-EDVI, RV-ESVI and the presence of LGE (Table 3). Due to the limited number of events only female gender and RV-EF as representative of right ventricular function and volumes were used in multivariable analysis (Table 3). The c-statistic of the final multivariable model including female gender and RV-EF was 0.73 (95% CI 0.72–0.92), while the bootstrapping procedure resulted in an optimism corrected c-statistic of 0.71.
3.4. Survival analysis for all-cause mortality Fig. 1 A–D illustrates the Kaplan–Meier curves for the independent predictors of all-cause mortality. After 2.5 years, female gender was associated with a significantly higher all-cause mortality rate (p = 0.02, Fig. 1 A). LGEpos DCM patients showed a higher rate of all-cause mortality that became particularly pronounced after 6 years (p = 0.047, Fig. 1 B). TAPSE below the median of 1.7 cm also was associated with a markedly higher all-cause mortality rate during the followup (p = 0.0003, Fig. 1 C). During the entire follow-up period, RVEF below the median 38% correlated significantly with a higher rate of all-cause mortality (p = 0.002, Fig. 1 D). When patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF remained unchanged. While LV-EF alone was not a significant discriminator Fig. 2 A), patients with a RV-EF above the median 38% showed a better survival irrespective of LV-EF (Fig. 2 B).
Table 2 CMR characteristics. All DCM patients (n = 140) LV-EF (%) EDM/BSA (g/m2) LV-EDVI (ml/m2) LV-ESVI (ml/m2) LVEDD (mm) RVEDD (mm) RAD (mm) TAPSE (cm) RV-EF (%) RV-EDVI (ml/m2) RV-ESVI (ml/m2) LVRI (g/ml) Indexed EAT mass (g/m2) Presence of LGE n (%) LGE extent (%) LGE pattern - midwall - patchy foci - epicardial - diffuse
27.8 ± 10.6 105.5 ± 30.1 146.2 ± 48.1 107.8 ± 46.6 63.3 ± 15.8 44.2 ± 7.8 48.2 ± 8.1 1.7 ± 0.8 36.9 ± 16.5 102.7 ± 39.1 66.9 ± 41.4 0.7 ± 0.2 23.2 ± 6.6 44 (31.4) 2.2 ± 4.2 21 (15.0) 13 (9.3) 7 (5.0) 3 (2.1)
Abbreviations: CHF: congestive heart failure, EAT: epicardial adipose tissue, g = gram, LGE: late gadolinium enhancement, LV: left ventricular, LV-EDD: left ventricular end diastolic diameter, LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, LVRI: left ventricular remodelling index, m2: square meter, ml: milliliter, n: number, RAD: right atrial diameter, RVEDD: right ventricular end diastolic diameter, RV-EDVI: right ventricular end diastolic volume index RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index, TAPSE: tricuspid annular systolic excursion
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Female gender Age ≥ 65 yrs LV-EF b 35% LV-EDVI ≥ 137 ml/m2 LV-ESVI ≥ 99 ml/m2 RV-EF b 38% TAPSE b 1.7 RV-EDVI ≥ 92 ml/m2 RV-ESVI ≥ 54 ml/m2 LGEpos Indexed EAT b 22 g/m2
Univariable analysis
Multivariable analysis
Hazard ratio (95% CI)
p value
Hazard ratio (95% CI)
p value
3.6 (1.2–10.7) 2.4 (1.0–5.7) 1.5 (0.5–4.4) 1.3 (0.6–3.0) 1.9 (0.8–4.5) 3.7 (1.5–9.2) 3.4 (1.5–8.0) 2.5 (1.0–5.9) 3.3 (1.3–8.1) 2.3 (1.0–5.2) 1.3 (0.5–3.2)
0.02 0.05 0.5 0.5 0.1 0.004 0.005 0.04 0.01 0.06 0.6
3.4 (1.4–8.3)
0.006
4.7 (1.8–12.0)
0.001
Abbreviations: EAT: epicardial adipose tissue, g = gram, LGE: late gadolinium enhancement, LV: left ventricular, LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, m2: square meter, ml: milliliter, n: number, RVEDD: right ventricular end diastolic diameter, RV-EDVI: right ventricular end diastolic volume index RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index, TAPSE: tricuspid annular systolic excursion
3.5. Predictors of hospitalization due to congestive heart failure compared to event-free survival CHF was associated with LVF, LV-EDVI, LVESVI, RV-EF and RVESVI by univariable analysis (Table 4). In multivariable analysis only RV-EF proved to be independently associated with CHF (Table 4). The c-statistic for RV-EF to predict hospitalization due to CHF was 0.65 (95% CI: 0.55–0.75). The optimism corrected c-statistics was 0.62. 3.6. Survival analysis for congestive heart failure Kaplan–Meier curve for the only independent predictor of CHF is shown in Fig. 3. During the entire follow-up period, RV-EF below the median 38% is associated with a markedly higher rate of hospitalization due to CHF (p = 0.001). Also with regard to hospitalization due to CHF, the prognostic value of RV-EF lasted even after stratification of patients according to systolic LV-EF. While LV-EF alone was not a significant discriminator Fig. 4 A), patients with a RV-EF above the median 38% showed a lower hospitalization rate irrespective of LV-EF (Fig. 4 B). 3.7. Comparison of patients who died and those hospitalized due to congestive heart failure Looking at the patients who died during the follow-up compared to those who were hospitalized due to CHF, the former presented with reduced indexed EAT (19.6 ± 6.0 g/m2 vs 23.2 ± 5 g/m2) as well as a higher prevalence of LGE (54.6% vs 27.5%). All other CMR characteristics were comparable between the two groups. 4. Discussion The first main result of the present study is that among the analyzed CMR parameters including LGE and EAT, RV-EF is the strongest independent predictor of all-cause mortality, and a modest predictor of cardiac morbidity due to CHF in patients with DCM. Secondly, even when patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF to predict all-cause mortality and cardiac morbidity remained unchanged. Thirdly, looking at the DCM patients who died during the follow-up compared to those who were hospitalized due to CHF, the former presented with a higher prevalence of LGE as well as reduced indexed EAT.
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Fig. 1. Kaplan–Meier curves for all-cause mortality. Kaplan–Meier curves for all-cause mortality according to gender (A), presence of LGE (B), TAPSE (C) and RV-EF (D).
4.1. Parameters to predict all-cause mortality in DCM In univariable and multivariable analysis female gender was predictive of all-cause mortality. Female gender had also shown to be an independent predictor of cardiovascular death in patients with CHF due to ICM or DCM in a prior study by Bistola V et al. [21]. Besides, in an Italian multicenter study [22] of patients with idiopathic dilated cardiomyopathy, women presented with more advanced disease with regard to symptoms and LV dimensions as well as a trend toward poorer prognosis. However, a limiting factor for evaluating gender-related aspects is the small number of women usually included in CHF studies. In our study population only 22.9% of patients included were female. Furthermore, the number of women with severely reduced LV-EF is significantly smaller than among men which certainly constitutes a certain inclusion bias in most studies selecting the patients according to LV-EF.
RV-EF had an important prognostic impact on survival in univariable and multivariable analysis. RV function and volumes are parameters that are rarely taken under consideration when analyzing prognosis in patients with DCM. However, in patients with DCM, RV function has not only shown to be impaired as consequence of LV dysfunction [23, 24] but histopathologically also a direct right ventricular involvement could be proved in patients with DCM [25–27]. In line with our results, a previous echocardiography study by Juillière Y et al. [28] also found that in addition to LV-EF, RV-EF appeared to be a complementary predictor of survival in 62 patients with idiopathic DCM. Another echocardiography study by Meluzin J et al. also observed a poor prognosis in 177 patients with either ischemic or idiopathic DCM and RV systolic and diastolic dysfunction. TAPSE represents a quick semi-quantitative approach to assess information about the RV-EF [29], was found to be correlated to markers
Fig. 2. Event-free survival according to LV-EF and RV-EF. Kaplan–Meier analysis shows that LV-EF alone is not a significant discriminator (A) but patients with a RV-EF N38% had a significantly better prognosis irrespective of LV-EF (B).
C. Doesch et al. / International Journal of Cardiology 177 (2014) 429–435 Table 4 Cox proportional hazard analysis for the time to the occurrence of hospitalization due to congestive heart failure.
Female gender Age ≥ 65 yrs LV-EF b 35% LV-EDVI ≥ 137 ml/m2 LV-ESVI ≥ 99 ml/m2 RV-EF b 38% RV-ESVI ≥ 54 ml/m2 LGEpos Indexed EAT b 22 g/m2
Univariable analysis
Multivariable analysis
Hazard ratio (95% CI)
p value
Hazard ratio (95% CI)
0.8 (0.4–1.6) 1.0 (0.6–1.8) 1.9 (0.9–4.3) 1.7 (1.0–3.1) 2.0 (1.2–3.6) 2.6 (1.4–4.6) 2.0 (1.1–3.5) 0.9 (0.5–1.7) 1.2 (0.7–2.2)
0.6 0.9 0.1 0.04 0.02 0.001 0.02 0.7 0.5
2.6 (1.4–4.6)
p value
0.6 0.2 0.2 0.001 0.9
Abbreviations: LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, m2: square meter, ml: milliliter, RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index
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worse outcome. Kaplan–Meier curves significantly differed for patients with TAPSE above the median (TAPSE ≥ 1.7 cm) and below the median (TAPSE b 1.7 cm). In multivariable analysis TAPSE had no independent prognostic impact. In line with a previous study by Hombach V et al. [31] LGE was only univariably associated with all-cause mortality but failed to play an independent prognostic role. Kaplan–Meier survival estimates for all-cause mortality showed a better probability of survival for patients without LGE compared to those with LGE. Studies that identified LGE as the most important indicator of outcome in patients with DCM did not include either RV-EF [7,9] or RV volumes [8,10]. Besides, the study by Wu et al. [9] only included DCM patients with an LV-EF b 35% and therefore did not represent the whole spectrum of patients with DCM studied by Hombach V et al. [31] and in the present study. Additionally, the present study also took into consideration the effect of a new parameter EAT that has also shown to have an impact on survival in a prior study [13] of patients with heart failure due to ICM or DCM and severely reduced LV-EF. The performance of the final multivariable model including female gender and RV-EF was good with an optimism corrected c-statistic of 0.80. Therefore, these parameters can be used to elaborate risk stratification in patients with DCM. 4.2. Parameters to predict hospitalization due to CHF in DCM
Fig. 3. Kaplan–Meier curve for hospitalization due to CHF. Kaplan–Meier curve for hospitalization due to CHF dependent on RV-EF.
of diastolic dysfunction and has been proven to be a valuable prognostic marker in various cardiac diseases, including heart failure [30]. Our data also showed a univariable association between reduced TAPSE and a
With regard to hospitalization due to CHF in patients with DCM, RV-EF was the only independent predictor in multivariable analysis. This result is in line with a study by Meluzin J. et al. [32] who also found a better event-free survival in patients with DCM and preserved RV-EF estimated by echocardiographic peak systolic tricuspid annular velocity. RV and LV volumes above the median were both associated with hospitalization due to CHF in univariable analysis but did not prove to be independent predictors in multivariable analysis. Although left ventricular dilatation is the hallmark of disease, there exists a huge variability in the degree of right ventricular dilatation. Lewis JF et al. [33] also showed a poorer survival in patients with a more severe right dilation compared to patients with a predominant left dilatation. In the study by Hombach V et al. [31], RV dilatation was also a risk factor to reach the composite endpoint of cardiac death and hospitalization due to CHF. As expected, patients with a reduced LV-EF and RV-EF had the worst prognosis with regard to all cause mortality, HTX and hospitalization due to CHF. Interestingly, patients with a LV-EF ≤ 35% and a RV-EF above the median 38% did better with regard to both endpoints than those with a LV-EF N 35% and a RV-EF below 38%. The optimism corrected c-statistics of 0.62 showed that RVEF is a
Fig. 4. Survival free from hospitalization due to CHF according to LV-EF and RV-EF. Kaplan–Meier analysis only showed a trend toward a better prognosis in patients with LV-EF N35% (A), whereas patients with a RV-EF N38% revealed a significantly higher probability of event-free survival irrespective of LV-EF (B). Abbreviations: CHF: congestive heart failure, LGE: late gadolinium enhancement, LV-EF: left ventricular ejection fraction, RV-EF: right ventricular ejection fraction.
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modest predictor of hospitalization due to CHF. These results once again highlight the prognostic importance to assess RV-EF in patients with DCM. Looking at the discriminators between those patients with a severely reduced LV-EF and RV-EF who died as opposed to those hospitalized due to CHF, a reduced EAT and the presence of LGE designated those that were more likely to die.
[5]
4.3. Limitations
[6]
One limitation of our study is the limited sample size and the small number of events. Another limitation is that the data presented were observed only at a single center. Therefore, larger multicenter trials are needed to confirm our results and to allow studying further parameters that might be of interest in these patients.
[7]
5. Conclusion Female gender, RV-EF and the presence of LGE are of prognostic importance in patients with DCM. In the present study, RV-EF proved to be an important independent predictor of all-cause mortality and a modest predictor of hospitalization due to CHF in patients with DCM. Further stratification of patients according to systolic LV-EF showed that the prognostic value of RV-EF to predict mortality and cardiac morbidity remained unchanged. Therefore, the present study underlines the role of CMR as an important risk stratification tool in patients with DCM. Funding sources This study was supported by grants from the DZHK (“Deutsches Zentrum für Herz-Kreislauf-Forschung”—German Centre for Cardiovascular Research, HD/MA 6.1 IM Multimodal Image Fusion) and by the BMBF (German Ministry of Education and Research, HD/MA 6.1 IM Multimodal Image Fusion).
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] [16]
[17]
Disclosures None. Conflict of interest
[18]
[19] [20]
The authors report no relationships that could be construed as a conflict of interest.
[21]
Acknowledgment The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology.
[22]
[23]
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Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Right ventricular dysfunction, late gadolinium enhancement, and female gender predict poor outcome in patients with dilated cardiomyopathy Christina Doesch a,c,⁎, Désirée-Marie Dierks a, Dariusch Haghi a,c, Rainer Schimpf a,c, Jürgen Kuschyk a,c, Tim Suselbeck a,c, Stefan O. Schoenberg b,c, Martin Borggrefe a,c, Theano Papavassiliu a,c a b c
1st Department of Medicine, Cardiology, University Medical Centre Mannheim, Medical Faculty Mannheim, University of Heidelberg, Germany Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Germany DZHK (German Centre for Cardiovascular Research) partner site, Mannheim, Germany
a r t i c l e
i n f o
Article history: Received 21 December 2013 Received in revised form 31 July 2014 Accepted 15 September 2014 Available online 7 October 2014 Keywords: Dilated cardiomyopathy Cardiovascular magnetic resonance imaging Right ventricular ejection fraction Risk stratification
a b s t r a c t Aims: Dilated cardiomyopathy (DCM) shows a variable disease course and is associated with significant morbidity and mortality. So far, left ventricular function (LVF) is the major determinant for risk stratification. However, since it has shown to be a poor guide to individual outcome, we studied the prognostic value of cardiovascular magnetic resonance imaging (CMR) parameters, late gadolinium enhancement (LGE) and epicardial adipose tissue (EAT). Methods and results: 140 patients with DCM underwent late gadolinium enhancement (LGE) CMR. During a median follow-up of 3 years, 22 patients (16%) died and another 51 (36%) were hospitalized due to congestive heart failure (CHF). Female gender and right ventricular ejection fraction (RV-EF) below the median of 38% were independent predictors of all-cause mortality in multivariable analysis. In patients who were hospitalized due to CHF, RV-EF below the median of 38% was the only independent predictor in multivariable analysis. When patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF to predict mortality and cardiac morbidity remained unchanged. Looking at DCM patients who died during follow-up compared to those who were hospitalized due to CHF, the former presented with a higher prevalence of LGE as well as reduced indexed EAT. Conclusion: Female gender, RV-EF and the presence of LGE are of prognostic importance in patients with DCM. Therefore, the present study underlines the role of CMR as an important tool for risk stratification in patients with DCM. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Dilated cardiomyopathy (DCM) shows a variable disease course and is associated with significant morbidity and mortality [1–3]. So far, left ventricular ejection fraction (LV-EF) is the major determinant for risk stratification and the current guidelines recommend implantation of a defibrillator for primary prevention in symptomatic patients (New York Heart Association functional class II/III) with a LV-EF less than 35% [4,5]. However, LV-EF alone has shown to be a poor guide to outcome. Cardiovascular magnetic resonance imaging (CMR) is the gold standard for non-invasive, accurate, and reproducible assessment of left and right ventricular function, cardiac mass and morphology
[6]. Due to the use of late gadolinium enhancement (LGE) technique, CMR allows in vivo quantification of regions of replacement fibrosis in patients with DCM that has shown a good correlation with histological data [7]. In prior studies [7–10], the presence of LGE in patients with DCM was also associated with an unfavorable prognosis. Additionally, CMR allows the quantification of epicardial adipose tissue (EAT) that has been shown to be reduced in patients with DCM [11–13] and became also suspect to be associated with a poor prognosis [13]. Since one single parameter does not seem to be sufficient to predict the clinical outcome of patients with NICM, we studied the prognostic value of CMR parameters, LGE and EAT. 2. Methods
⁎ Corresponding author at: 1st Department of Medicine, Cardiology affiliated at the DZHK (German Centre for Cardiovascular Resarch) partner site Mannheim, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Tel.: + 49 621 383 2204; fax: + 49 621 383 3821. E-mail address: [email protected] (C. Doesch).
http://dx.doi.org/10.1016/j.ijcard.2014.09.004 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
2.1. Study population Between February 2003 and February 2011, 150 consecutive patients with DCM that underwent late gadolinium enhancement CMR to quantify left ventricular (LV) function and myocardial scarring as part of their routine clinical work-up were enrolled at our tertiary referral hospital.
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The diagnosis of DCM was based on the 1995 WHO/International Society and Federation of Cardiology criteria [14]. All patients had undergone coronary angiography and were classified as non-ischemic if they had no history of myocardial infarction or revascularization and no evidence of coronary artery stenoses N50% of 2 or more epicardial vessels or left main or proximal anterior descending coronary artery N50% [15]. Patients with a normal CMR-derived left ventricular ejection fraction (LV-EF N 55%) were not included in the study. Other exclusion criteria were standard contraindications to CMR examination. CMR examination was not possible in 5 (3.3%) patients due to claustrophobia and in 3 (2.0%) patients due to severe obesity. 2 (1.3%) patients were lost during follow-up so that the final study population consisted of 140 patients. Of these 140 patients, 19 patients were included in an earlier study [13] and are now reported with an extended follow-up. The study was approved by the local ethics committee and informed consent was obtained from all patients.
2.2. Image acquisition All studies were performed using 1.5 Tesla whole-body imaging systems (Magnetom Sonata and Avanto, Siemens Healthcare Sector, Erlangen, Germany). To evaluate functional parameters, electrocardiogram-gated cine images were acquired using a segmented steady-state free precession [fast imaging with steady-state precession (true-FISP)] sequence (time to echo/time of repetition 1.6/3.2 ms, temporal resolution 35 ms, in-plane spatial resolution 1.4 × 1.8 mm, slice thickness 8 mm, interslice gap 2 mm). For the assessment of the epicardial adipose tissue, we used a dark blood prepared T1-weighted multislice turbo spin-echo pulse sequence with a water suppression prepulse (time of repetition = 800 ms, time to echo = 24 ms, slice thickness = 6 mm, interslice gap = 2 mm, and field of view = 30 to 34 cm) in the same orientations as the short-axis images.
2.7. Statistical analysis Since we aimed to study to what extent CMR results, age and gender were associated with events, Cox proportional hazard regression models were constructed for age, gender and CMR parameters. To check the proportional hazard assumption for different categories, they were plotted against time to ensure that the curves were reasonably parallel. Those variables which appeared to be associated with events at a value of p ≤ 0.1 level in univariable analysis were eligible for multivariable analysis to predict hospitalization for CHF. Forward stepwise logrank regression (p b 0.2 for entry, p N 0.1 for removal) was used. Due to the limited number of all-cause mortality events, the number of candidate parameters for multivariable analysis was limited to two (the clinical and CMR parameter with the best performance in univariable analysis) to avoid model overfitting. Results are presented as hazard ratios with 95% confidence intervals (CIs). The follow-up duration was measured from the CMR study date. The performance of the final models was assessed with respect to discrimination. Discrimination is the model's ability to separate patients with different outcomes. To quantify the discrimination, we used the c-statistic (Harrell's C) [19]. The maximum value of the c-statistic is 1.0; indicating a perfect prediction model. A value of 0.5 indicates that patients are correctly classified in 50% of the cases, e.g. as good as chance. The performance of a prediction model is generally worse in new patients than initially expected. This “optimism” can be studied with internal validation techniques [20]. Internal validity of our models was assessed with standard bootstrapping techniques [20]. The c-statistic of the final multivariable model, corrected for optimism, was reported. Kaplan–Meier analysis was performed for the independent predictors of all-causemortality. For this analysis the study population was divided into two groups according to the median value of the entire study population. Difference in survival over time was evaluated by a log-rank test. Analysis was performed using SPSS statistical software (version 14.0, SPSS Inc., Chicago, Illinois), Stata version 11 (StataCorp), and R software (version 2.8.1, R foundation for statistical computing, Vienna, Austria).
2.3. LGE Ten minutes after contrast agent injection (BW gadoterate meglumine, Dotarem, Guerbet, France), late gadolinium enhancement (LGE) images were acquired. An inversion time (TI) scout was performed to choose the optimal TIs between 200 and 360 ms. LGE images were assessed using either an inversion recovery Turbo FLASH 2D sequence: field of view 300–340 mm, TR 9.56 ms, TE 4.38 ms, flip angle 25°; matrix 166 × 256 and slice thickness 6 mm or a phase-sensitive inversion recovery TrueFISP sequence [16]: field of view 290 mm × 260 mm, TR 2.2 ms, TE 1.1 ms, flip angle 50°; matrix, 140 × 192 and slice thickness 6 mm, in-plane resolution 1.4 × 1.9 × 6 mm. LGE was only considered to be present if it was also present in the same slice after swapping phase encoding, thus excluding artifacts.
3. Results 140 patients with DCM (77% men, mean age 59.2 ± 13.9) were included in the study. The baseline clinical characteristics are presented in Table 1. Most patients presented with symptomatic heart failure (NYHA N I). Median follow-up was 3 years (interquartile range 0.5– 5.0 years). 3.1. Outcome
2.4. Image analysis Image analysis and quantitative analysis were performed off-line using dedicated software (ARGUS, Siemens, Germany). The readers were blinded to patient data and outcome. On the four-chamber view, the tricuspid annular plane systolic excursion (TAPSE) was calculated as previously described [17]. The amount of EAT was determined according to the method described by Fluechter S et al. [18].
2.5. Extent of LGE The extent of LGE was assessed visually by two independent experienced readers. LGE was only considered to be present if it was also present in the same slice after swapping phase encoding, thus excluding artifacts. The pattern of LGE was characterized as midwall, patchy foci, epicardial, or diffuse [9,10]. For quantification of fibrosis, LGE was defined as areas with a signal intensity N 2 standard deviation (SD) above mean signal intensity of remote myocardium in the same short-axis slice [8]. Areas were measured by planimetry and expressed as percentage of the myocardial area using the VPT tool (Siemens Healthcare Systems Erlangen, Germany).
2.6. Follow-up data and definition of study endpoints The long-term follow-up was performed by patient interview at our outpatient clinic and by telephone contact. The observers were unaware of the CMR results and collected data with a standardized questionnaire. Reported clinical events were confirmed by review of the corresponding medical records in our electronic Hospital Information System, contact with the general practitioner, referring cardiologist, or the treating hospital. The definition of cardiac event required the documentation of significant ventricular arrhythmia or cardiac arrest or death attributable to congestive heart failure or myocardial infarction in the absence of any other precipitating factor. In case of out-of-hospital death not followed by autopsy, sudden unexpected death was classified as cardiac death. The primary study endpoint was a combined endpoint of allcause mortality including non cardiac and cardiac death as well as heart transplantation (HTX). The secondary endpoint was hospitalization due to worsening of CHF. Patients who were hospitalized and died over the course of follow-up were only counted regarding the primary endpoint and not the secondary endpoint.
During the follow-up, 22 (16%) patients died. Thereof 15 (11%) patients suffered cardiac death, 1 (0.7%) patient underwent HTX and non cardiac death was reported in 6 (4%) patients. 51 (36%) were hospitalized due to congestive heart failure (CHF). 67 (48%) showed an event-free survival. Table 1 Baseline demographic and clinical characteristics. All DCM patients n = 140 Male n (%) Age (yrs) NYHA functional class • I • II • II • IV Atrial fibrillation n (%) Family history of DCM n (%) Hypertension n (%) Smoking n (%) Hyperlipidemia n (%) Diabetes n (%) Medication n (%) • Beta-blocker • ACEI • ARB • Spironolactone • Diuretics • Digoxin • Amiodarone
108 (77.1) 59.3 ± 13.8 7 (5.0) 26 (18.6) 64 (45.7) 43 (30.7) 54 (38.6) 8 (5.7) 46 (32.9) 20 (14.3) 41 (29.3) 31 (22.1) 118 (84.3) 113 (80.7) 21 (15.0) 34 (24.3) 100 (71.4) 38 (27.1) 12 (8.6)
Abbreviations: ACEI: angiotensin-converting-enzyme inhibitor, ARB: angiotensin II receptor blockers, CHF: congestive heart failure, n: number, DCM: dilated cardiomyopathy, NYHA: New York Heart association functional class, yrs: years
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3.2. Demographic, clinical and CMR parameters of all patients with DCM
Table 3 Cox proportional hazard analysis for the time to the occurrence of all-cause mortality.
Tables 1 and 2 show the demographic, clinical and CMR parameters in all patients with DCM.
3.3. Predictors of all-cause mortality compared to event-free survival By univariable analysis, significant associations were observed between age, female gender, RV-EF, TAPSE, RV-EDVI, RV-ESVI and the presence of LGE (Table 3). Due to the limited number of events only female gender and RV-EF as representative of right ventricular function and volumes were used in multivariable analysis (Table 3). The c-statistic of the final multivariable model including female gender and RV-EF was 0.73 (95% CI 0.72–0.92), while the bootstrapping procedure resulted in an optimism corrected c-statistic of 0.71.
3.4. Survival analysis for all-cause mortality Fig. 1 A–D illustrates the Kaplan–Meier curves for the independent predictors of all-cause mortality. After 2.5 years, female gender was associated with a significantly higher all-cause mortality rate (p = 0.02, Fig. 1 A). LGEpos DCM patients showed a higher rate of all-cause mortality that became particularly pronounced after 6 years (p = 0.047, Fig. 1 B). TAPSE below the median of 1.7 cm also was associated with a markedly higher all-cause mortality rate during the followup (p = 0.0003, Fig. 1 C). During the entire follow-up period, RVEF below the median 38% correlated significantly with a higher rate of all-cause mortality (p = 0.002, Fig. 1 D). When patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF remained unchanged. While LV-EF alone was not a significant discriminator Fig. 2 A), patients with a RV-EF above the median 38% showed a better survival irrespective of LV-EF (Fig. 2 B).
Table 2 CMR characteristics. All DCM patients (n = 140) LV-EF (%) EDM/BSA (g/m2) LV-EDVI (ml/m2) LV-ESVI (ml/m2) LVEDD (mm) RVEDD (mm) RAD (mm) TAPSE (cm) RV-EF (%) RV-EDVI (ml/m2) RV-ESVI (ml/m2) LVRI (g/ml) Indexed EAT mass (g/m2) Presence of LGE n (%) LGE extent (%) LGE pattern - midwall - patchy foci - epicardial - diffuse
27.8 ± 10.6 105.5 ± 30.1 146.2 ± 48.1 107.8 ± 46.6 63.3 ± 15.8 44.2 ± 7.8 48.2 ± 8.1 1.7 ± 0.8 36.9 ± 16.5 102.7 ± 39.1 66.9 ± 41.4 0.7 ± 0.2 23.2 ± 6.6 44 (31.4) 2.2 ± 4.2 21 (15.0) 13 (9.3) 7 (5.0) 3 (2.1)
Abbreviations: CHF: congestive heart failure, EAT: epicardial adipose tissue, g = gram, LGE: late gadolinium enhancement, LV: left ventricular, LV-EDD: left ventricular end diastolic diameter, LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, LVRI: left ventricular remodelling index, m2: square meter, ml: milliliter, n: number, RAD: right atrial diameter, RVEDD: right ventricular end diastolic diameter, RV-EDVI: right ventricular end diastolic volume index RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index, TAPSE: tricuspid annular systolic excursion
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Female gender Age ≥ 65 yrs LV-EF b 35% LV-EDVI ≥ 137 ml/m2 LV-ESVI ≥ 99 ml/m2 RV-EF b 38% TAPSE b 1.7 RV-EDVI ≥ 92 ml/m2 RV-ESVI ≥ 54 ml/m2 LGEpos Indexed EAT b 22 g/m2
Univariable analysis
Multivariable analysis
Hazard ratio (95% CI)
p value
Hazard ratio (95% CI)
p value
3.6 (1.2–10.7) 2.4 (1.0–5.7) 1.5 (0.5–4.4) 1.3 (0.6–3.0) 1.9 (0.8–4.5) 3.7 (1.5–9.2) 3.4 (1.5–8.0) 2.5 (1.0–5.9) 3.3 (1.3–8.1) 2.3 (1.0–5.2) 1.3 (0.5–3.2)
0.02 0.05 0.5 0.5 0.1 0.004 0.005 0.04 0.01 0.06 0.6
3.4 (1.4–8.3)
0.006
4.7 (1.8–12.0)
0.001
Abbreviations: EAT: epicardial adipose tissue, g = gram, LGE: late gadolinium enhancement, LV: left ventricular, LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, m2: square meter, ml: milliliter, n: number, RVEDD: right ventricular end diastolic diameter, RV-EDVI: right ventricular end diastolic volume index RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index, TAPSE: tricuspid annular systolic excursion
3.5. Predictors of hospitalization due to congestive heart failure compared to event-free survival CHF was associated with LVF, LV-EDVI, LVESVI, RV-EF and RVESVI by univariable analysis (Table 4). In multivariable analysis only RV-EF proved to be independently associated with CHF (Table 4). The c-statistic for RV-EF to predict hospitalization due to CHF was 0.65 (95% CI: 0.55–0.75). The optimism corrected c-statistics was 0.62. 3.6. Survival analysis for congestive heart failure Kaplan–Meier curve for the only independent predictor of CHF is shown in Fig. 3. During the entire follow-up period, RV-EF below the median 38% is associated with a markedly higher rate of hospitalization due to CHF (p = 0.001). Also with regard to hospitalization due to CHF, the prognostic value of RV-EF lasted even after stratification of patients according to systolic LV-EF. While LV-EF alone was not a significant discriminator Fig. 4 A), patients with a RV-EF above the median 38% showed a lower hospitalization rate irrespective of LV-EF (Fig. 4 B). 3.7. Comparison of patients who died and those hospitalized due to congestive heart failure Looking at the patients who died during the follow-up compared to those who were hospitalized due to CHF, the former presented with reduced indexed EAT (19.6 ± 6.0 g/m2 vs 23.2 ± 5 g/m2) as well as a higher prevalence of LGE (54.6% vs 27.5%). All other CMR characteristics were comparable between the two groups. 4. Discussion The first main result of the present study is that among the analyzed CMR parameters including LGE and EAT, RV-EF is the strongest independent predictor of all-cause mortality, and a modest predictor of cardiac morbidity due to CHF in patients with DCM. Secondly, even when patients where further stratified according to systolic LV-EF, the prognostic value of RV-EF to predict all-cause mortality and cardiac morbidity remained unchanged. Thirdly, looking at the DCM patients who died during the follow-up compared to those who were hospitalized due to CHF, the former presented with a higher prevalence of LGE as well as reduced indexed EAT.
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Fig. 1. Kaplan–Meier curves for all-cause mortality. Kaplan–Meier curves for all-cause mortality according to gender (A), presence of LGE (B), TAPSE (C) and RV-EF (D).
4.1. Parameters to predict all-cause mortality in DCM In univariable and multivariable analysis female gender was predictive of all-cause mortality. Female gender had also shown to be an independent predictor of cardiovascular death in patients with CHF due to ICM or DCM in a prior study by Bistola V et al. [21]. Besides, in an Italian multicenter study [22] of patients with idiopathic dilated cardiomyopathy, women presented with more advanced disease with regard to symptoms and LV dimensions as well as a trend toward poorer prognosis. However, a limiting factor for evaluating gender-related aspects is the small number of women usually included in CHF studies. In our study population only 22.9% of patients included were female. Furthermore, the number of women with severely reduced LV-EF is significantly smaller than among men which certainly constitutes a certain inclusion bias in most studies selecting the patients according to LV-EF.
RV-EF had an important prognostic impact on survival in univariable and multivariable analysis. RV function and volumes are parameters that are rarely taken under consideration when analyzing prognosis in patients with DCM. However, in patients with DCM, RV function has not only shown to be impaired as consequence of LV dysfunction [23, 24] but histopathologically also a direct right ventricular involvement could be proved in patients with DCM [25–27]. In line with our results, a previous echocardiography study by Juillière Y et al. [28] also found that in addition to LV-EF, RV-EF appeared to be a complementary predictor of survival in 62 patients with idiopathic DCM. Another echocardiography study by Meluzin J et al. also observed a poor prognosis in 177 patients with either ischemic or idiopathic DCM and RV systolic and diastolic dysfunction. TAPSE represents a quick semi-quantitative approach to assess information about the RV-EF [29], was found to be correlated to markers
Fig. 2. Event-free survival according to LV-EF and RV-EF. Kaplan–Meier analysis shows that LV-EF alone is not a significant discriminator (A) but patients with a RV-EF N38% had a significantly better prognosis irrespective of LV-EF (B).
C. Doesch et al. / International Journal of Cardiology 177 (2014) 429–435 Table 4 Cox proportional hazard analysis for the time to the occurrence of hospitalization due to congestive heart failure.
Female gender Age ≥ 65 yrs LV-EF b 35% LV-EDVI ≥ 137 ml/m2 LV-ESVI ≥ 99 ml/m2 RV-EF b 38% RV-ESVI ≥ 54 ml/m2 LGEpos Indexed EAT b 22 g/m2
Univariable analysis
Multivariable analysis
Hazard ratio (95% CI)
p value
Hazard ratio (95% CI)
0.8 (0.4–1.6) 1.0 (0.6–1.8) 1.9 (0.9–4.3) 1.7 (1.0–3.1) 2.0 (1.2–3.6) 2.6 (1.4–4.6) 2.0 (1.1–3.5) 0.9 (0.5–1.7) 1.2 (0.7–2.2)
0.6 0.9 0.1 0.04 0.02 0.001 0.02 0.7 0.5
2.6 (1.4–4.6)
p value
0.6 0.2 0.2 0.001 0.9
Abbreviations: LV-EDVI: left ventricular end diastolic volume index, LV-EF: left ventricular ejection fraction, LV-ESVI: left ventricular end-systolic volume index, m2: square meter, ml: milliliter, RV-EF: right ventricular ejection fraction, RV-ESVI: right ventricular end-systolic volume index
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worse outcome. Kaplan–Meier curves significantly differed for patients with TAPSE above the median (TAPSE ≥ 1.7 cm) and below the median (TAPSE b 1.7 cm). In multivariable analysis TAPSE had no independent prognostic impact. In line with a previous study by Hombach V et al. [31] LGE was only univariably associated with all-cause mortality but failed to play an independent prognostic role. Kaplan–Meier survival estimates for all-cause mortality showed a better probability of survival for patients without LGE compared to those with LGE. Studies that identified LGE as the most important indicator of outcome in patients with DCM did not include either RV-EF [7,9] or RV volumes [8,10]. Besides, the study by Wu et al. [9] only included DCM patients with an LV-EF b 35% and therefore did not represent the whole spectrum of patients with DCM studied by Hombach V et al. [31] and in the present study. Additionally, the present study also took into consideration the effect of a new parameter EAT that has also shown to have an impact on survival in a prior study [13] of patients with heart failure due to ICM or DCM and severely reduced LV-EF. The performance of the final multivariable model including female gender and RV-EF was good with an optimism corrected c-statistic of 0.80. Therefore, these parameters can be used to elaborate risk stratification in patients with DCM. 4.2. Parameters to predict hospitalization due to CHF in DCM
Fig. 3. Kaplan–Meier curve for hospitalization due to CHF. Kaplan–Meier curve for hospitalization due to CHF dependent on RV-EF.
of diastolic dysfunction and has been proven to be a valuable prognostic marker in various cardiac diseases, including heart failure [30]. Our data also showed a univariable association between reduced TAPSE and a
With regard to hospitalization due to CHF in patients with DCM, RV-EF was the only independent predictor in multivariable analysis. This result is in line with a study by Meluzin J. et al. [32] who also found a better event-free survival in patients with DCM and preserved RV-EF estimated by echocardiographic peak systolic tricuspid annular velocity. RV and LV volumes above the median were both associated with hospitalization due to CHF in univariable analysis but did not prove to be independent predictors in multivariable analysis. Although left ventricular dilatation is the hallmark of disease, there exists a huge variability in the degree of right ventricular dilatation. Lewis JF et al. [33] also showed a poorer survival in patients with a more severe right dilation compared to patients with a predominant left dilatation. In the study by Hombach V et al. [31], RV dilatation was also a risk factor to reach the composite endpoint of cardiac death and hospitalization due to CHF. As expected, patients with a reduced LV-EF and RV-EF had the worst prognosis with regard to all cause mortality, HTX and hospitalization due to CHF. Interestingly, patients with a LV-EF ≤ 35% and a RV-EF above the median 38% did better with regard to both endpoints than those with a LV-EF N 35% and a RV-EF below 38%. The optimism corrected c-statistics of 0.62 showed that RVEF is a
Fig. 4. Survival free from hospitalization due to CHF according to LV-EF and RV-EF. Kaplan–Meier analysis only showed a trend toward a better prognosis in patients with LV-EF N35% (A), whereas patients with a RV-EF N38% revealed a significantly higher probability of event-free survival irrespective of LV-EF (B). Abbreviations: CHF: congestive heart failure, LGE: late gadolinium enhancement, LV-EF: left ventricular ejection fraction, RV-EF: right ventricular ejection fraction.
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modest predictor of hospitalization due to CHF. These results once again highlight the prognostic importance to assess RV-EF in patients with DCM. Looking at the discriminators between those patients with a severely reduced LV-EF and RV-EF who died as opposed to those hospitalized due to CHF, a reduced EAT and the presence of LGE designated those that were more likely to die.
[5]
4.3. Limitations
[6]
One limitation of our study is the limited sample size and the small number of events. Another limitation is that the data presented were observed only at a single center. Therefore, larger multicenter trials are needed to confirm our results and to allow studying further parameters that might be of interest in these patients.
[7]
5. Conclusion Female gender, RV-EF and the presence of LGE are of prognostic importance in patients with DCM. In the present study, RV-EF proved to be an important independent predictor of all-cause mortality and a modest predictor of hospitalization due to CHF in patients with DCM. Further stratification of patients according to systolic LV-EF showed that the prognostic value of RV-EF to predict mortality and cardiac morbidity remained unchanged. Therefore, the present study underlines the role of CMR as an important risk stratification tool in patients with DCM. Funding sources This study was supported by grants from the DZHK (“Deutsches Zentrum für Herz-Kreislauf-Forschung”—German Centre for Cardiovascular Research, HD/MA 6.1 IM Multimodal Image Fusion) and by the BMBF (German Ministry of Education and Research, HD/MA 6.1 IM Multimodal Image Fusion).
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] [16]
[17]
Disclosures None. Conflict of interest
[18]
[19] [20]
The authors report no relationships that could be construed as a conflict of interest.
[21]
Acknowledgment The authors of this manuscript have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology.
[22]
[23]
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