Right ventricular function assessed by 2D strain analysis predicts ventricular arrhythmias and sudden cardiac death in patients after acute myocardial infarction

Right ventricular function assessed by 2D strain analysis predicts ventricular arrhythmias and... Abstract Aims Left ventricular function is a well-established predictor of malignant ventricular arrhythmias, but little is known about the importance of right ventricular (RV) function. The aim of this study was to investigate the importance of RV function for prediction of sudden cardiac death (SCD) or malignant ventricular arrhythmias (VAs) after acute myocardial infarction (MI). Methods and results A total of 790 patients with acute MI were prospectively included. All patients had 2D strain echocardiography performed to evaluate right ventricular (RV) free wall strain (RVS) and RV mechanical dispersion (MD) defined as the standard deviation of time to peak negative strain in all myocardial segments. The primary composite end point [SCD, admission with VA or appropriate therapy from a primary prophylactic implantable cardioverter–defibrillator (ICD)] was analysed with Cox models. Mean age was 69 ± 12 years, and 74% were male. Thirty-one patients experienced the primary end point during a median follow-up of 898 days (Q1–Q3 704–981). RVS was independently associated with outcome in a multivariable model including age and left ventricular global longitudinal strain; pr 1% change [hazard ratio (HR) 1.08, 95% confidence interval (CI) 1.01–1.15; P = 0.038]. Patients in the lower tertile (poor strain) showed a 10-fold risk of an event compared with the upper tertile (HR 9.8, 95% CI 2.23–42.3; P = 0.002). RV MD was not independently associated with VA/SCD (HR 0.99, 95% CI 0.91–1.09; P = 0.93). RVS proved superior to tricuspid annular plane systolic excursion (TAPSE) (P = 0.03) in the multivariable model. Conclusion RVS, but not RV MD, was significantly and independently related to SCD/VA in patients with acute MI. Furthermore, RVS was shown to be superior to TAPSE. Introduction Patients who experience an acute myocardial infarction (MI) have an increased risk of malignant ventricular arrhythmias (VAs) that may lead to sudden cardiac death (SCD).1 Risk stratification in this population is important to determine whether patients may benefit from implantable cardioverter–defibrillator (ICD) therapy. Left ventricular (LV) dysfunction [ejection fraction (EF) < 35%] is the most established parameter for selection of ICD patients2 but has demonstrated limited ability in prediction of outcomes.3 Thus, the current selection criteria for ICD needs to be further refined. The importance of right ventricular (RV) function for the occurrence of VA/SCD in the post-MI population is largely unknown. An MI is often considered as an LV pathology, but studies have demonstrated that RV function is a critical determinant of outcome in conditions such as congestive heart failure4,5 and MI.6 Specifically with regard to arrhythmia, it has been proposed that patients with evidence of RV involvement after an inferior MI are at increased risk.6 Furthermore, in patients receiving cardiac resynchronization therapy, it has been proposed that favourable RV remodelling is related to reduced arrhythmic risk.7 It therefore seems plausible that RV dysfunction is an independent marker of VA/SCD in post-MI patients. For the purpose of evaluating RV function, deformation analysis by 2D strain echocardiography (2DSE) may be a valuable tool.8,9 Applied to the left ventricle, 2DSE-derived measures, such as the mechanical dispersion (MD) or global longitudinal strain (GLS), have been shown to improve risk stratification for the occurrence of arrhythmia.10,11 Recent studies have indicated that RV strain (RVS) may also be superior to traditional markers of global RV function in prediction of clinical outcomes.12,13 Based on a large contemporary cohort of patients with acute MI, we hypothesized that detailed analysis of RV deformation would provide important prognostic information in relation to malignant VA. Methods Study design and patient population As previously described,14 patients were prospectively enrolled from two tertiary cardiac centres when referred for invasive coronary angiography due to either ST-segment elevation or non–ST-segment elevation MI. Exclusion criteria were age <18 years, non-cardiac disease with a life expectancy <1 year and an inability to provide written informed consent. Patients with atrial fibrillation, severe valve disease and paced rhythm at the time of the echocardiography were excluded. In addition, the patients with suboptimal image quality for 2D strain analysis of either the right ventricle or the left ventricle were excluded (Figure 1). Figure 1 View largeDownload slide Flowchart displaying the selection of patients. MI, myocardial infarction; AS, aortic stenosis; MR, mitral valve regurgitation; LV, left ventricular; RV, right ventricular. Figure 1 View largeDownload slide Flowchart displaying the selection of patients. MI, myocardial infarction; AS, aortic stenosis; MR, mitral valve regurgitation; LV, left ventricular; RV, right ventricular. Demographics were acquired from chart reviews regarding diabetes, hypertension, history of ischaemic heart disease or MI and objective signs of heart failure (Killip class). Information related to the coronary angiography, including culprit lesion, number of diseased vessels, left main involvement and type of revascularization (percutaneous coronary intervention, coronary artery bypass grafting or no intervention) were registered. Peak troponin I level was measured in 220 (28%) patients, peak troponin T level was measured in 558 (71%) patients and 12 (1%) patients had missing values. All patients provided written informed consent. The study protocol was approved by the regional scientific ethics committee (reference no. H-D-2009-063). Echocardiography Echocardiography was performed within 48 h of admission to the tertiary centre. Three consecutive heart cycles were acquired. All examinations were performed on a Vivid e9 system (General Electric, Horten, Norway). Images were obtained at a frame rate of at least 50 frames/s and analysed offline (Echopac BT 11.1.0, General Electric). All analyses were performed by a single experienced operator (M.E.) who was blinded to follow-up information. Measurements of LV volumes and function, LVEF, LV end-diastolic volume (EDV) and LV end-systolic volume (ESV) were performed using biplane Simpson model. 2D strain analysis The 2D strain analysis was performed on both the LV in the three apical (long-axis, 4-chamber, and 2-chamber) views15 and the RV four-chamber view.16 All analyses, for both RV and LV, were optimized to be performed at a frame rate of 50–90 frames/min. Aortic valve closure was identified from continuous-wave Doppler recordings through the aortic valve. The endocardial border was traced in end systole and the automatically generated region of interest (ROI) was adjusted to exclude the pericardium. The integrity of speckle tracking was automatically detected and visually ascertained. In case of poor tracking, the ROI tracing was readjusted. Segments with persistent inadequate tracking were excluded from analysis. LV strain analysis The LV was divided into 17 segments covering the entire myocardium, and GLS was calculated as the mean of the global peak systolic strain from each of the three views. If global peak systolic strain could be assessed only in two of the three apical projections, GLS was calculated as the mean of these two. If global peak systolic strain could not be assessed in at least two of the apical projections, the patient was excluded from the study due to suboptimal image quality. The MD was calculated as the standard deviation (SD) of the time-to-peak strain measured from the peak electrocardiographic (ECG) R-wave to peak negative strain for all segments. If six or more segments did not have sufficient tracking, then the measure was excluded.10 RV strain analysis The RVS was used as a measure of global RV function, because the septal wall was included in the analysis of LV GLS, which has previously been shown to be the strongest predictor of arrhythmia in the current cohort.10 The measurement of RVS required all three segments in the RV free wall (basal, midventricular and apical) to be reliably tracked or else it was excluded. RV MD included all the six RV segments and was calculated as the SD of the time-to-peak strain measured from the ECG peak R-wave to peak negative strain for each segment (Figure 2). If more than 2 segments were insufficiently tracked, the measurement was excluded. Figure 2 View largeDownload slide The 2D strain echocardiography applied to the right ventricle (left panel). The mid panel shows right ventricular free wall strain. The right panel shows right ventricular mechanical dispersion, white dots indicating peak deformation. Figure 2 View largeDownload slide The 2D strain echocardiography applied to the right ventricle (left panel). The mid panel shows right ventricular free wall strain. The right panel shows right ventricular mechanical dispersion, white dots indicating peak deformation. Intra- and interobserver reproducibility The intra- and inter-observer reproducibility has previously been described in detail12 and was good with coefficients of variation of 6% for intra-observer variability and 9% for inter-observer variability, respectively. Definition of end point The primary outcome was a composite of definite or suspected SCD, admission with documented VA or appropriate ICD discharge only in patients with a primary prophylactic ICD.10 The cause of death was ascertained from hospital and pre-hospital patient records by two independent reviewers who were blinded to echocardiographic data, and information on all-cause mortality was obtained from the Danish Civil Registration System. For patients with a primary prophylactic ICD, the device was interrogated by an experienced electrophysiologist blinded to echocardiographic data. Statistical analysis Descriptive statistics Relevant variables were tested for normality using visual inspection of histogram plots and are presented as mean ± SD or median [first and third quartiles (Q1–Q3)]. Continuous variables were compared using Student’s t-test. Proportional differences were tested using χ2 statistics or Fisher’s exact test where appropriate. All tests were two sided, and statistical significance was defined as P < 0.05. Primary composite end point The primary end point was a composite of VA or SCD. Proportional hazards assumptions were verified graphically. Cause-specific Cox regression models allowing for competing risks were used to identify univariate predictors of the primary composite outcome. The competing risk associated with the primary composite outcome was death from all causes other than SCD. Unadjusted cumulative incidence curves were calculated for the primary composite outcome stratified by tertiles of RVS and RV MD. Candidate variables with P-values of <0.05 in univariate analysis were included in the multivariable model using backward selection to test the independent association between outcome and RVS and RV MD, respectively. Receiver operating characteristic curve analysis with the use of a non-parametric estimate of the area under curve (AUC) and c-statistics with 95% confidence interval (CI) was performed for the multivariable model. For comparison between RVS and tricuspid annular plane systolic excursion (TAPSE), the strength of association with outcome for each parameter was compared using −2 log likelihood statistics. All statistical analyses were performed using SAS for Windows version 9.3 (SAS institute, Cary, NC, USA). Results Baseline characteristics and outcome Of the 1110 patients, 790 patients with complete 2DSE data sets were included. Figure 1 shows a flowchart for patient selection. Mean age was 69 ± 12 years, 588 (74%) patients were male and 541 (69%) patients had ST-elevation MI. The baseline characteristics according to whether the patients experienced an end point or not are presented in Table 1. Overall, patients who had an event were characterized by being older, more symptomatic (Killip class), having lower renal function and reduced myocardial function including both RV and LV function. With regard to RV function specifically, RVS and TAPSE were markedly lower in those with arrhythmic events compared with those without (−24.1 ± 5 vs. −19.8 ± 5, P < 0.0001 and 22 ± 4 vs. 20 ± 6, P < 0.0001), respectively. No significant differences were found in RV MD between patients with an event compared with those without (52 ± 23 vs. 58 ± 22, P = 0.2). Table 1 Baseline characteristics according to no event/VA or SCD   No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001    No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001  Values are mean SD, n (%) or median (Q1–Q3). eGFR, estimated glomerular filtration rate; HR, heart rate; 3VD, 3-vessel disease; CABG, coronary artery bypass grafting; ECG, electrocardiography; LV, left ventricular; RV, right ventricular; GLS, global longitudinal strain; LAD, left anterior descending; LVEF, left ventricular ejection fraction; MD, mechanical dispersion; MI, myocardial infarction; PCI, percutaneous coronary intervention; WMSI, wall motion score index; LVEF, left ventricular ejection fraction; RVS, right ventricle free wall strain. Table 1 Baseline characteristics according to no event/VA or SCD   No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001    No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001  Values are mean SD, n (%) or median (Q1–Q3). eGFR, estimated glomerular filtration rate; HR, heart rate; 3VD, 3-vessel disease; CABG, coronary artery bypass grafting; ECG, electrocardiography; LV, left ventricular; RV, right ventricular; GLS, global longitudinal strain; LAD, left anterior descending; LVEF, left ventricular ejection fraction; MD, mechanical dispersion; MI, myocardial infarction; PCI, percutaneous coronary intervention; WMSI, wall motion score index; LVEF, left ventricular ejection fraction; RVS, right ventricle free wall strain. Table 2 Univariate and multivariate risk analysis of VA/SCD Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        HR, hazard ratio; 95% CI, confidence interval. Abbreviations as in Table 1. Table 2 Univariate and multivariate risk analysis of VA/SCD Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        HR, hazard ratio; 95% CI, confidence interval. Abbreviations as in Table 1. The median follow-up was 898 (Q1–Q3 704–981) days, and no patients were lost to follow-up. Overall, 31 patients experienced the primary end point. Six (19%) patients experienced the primary end point during the first 3 months. Of the total 31 events, 24 (77%) were attributed to SCD. Of the entire cohort, a total of 29 (3.6%) patients had a primary prophylactic ICD implanted during follow-up, of whom 6 of the 29 (21%) patients received appropriate therapy: four had an ICD shock and 2 received anti-tachycardia pacing. Thus, of the 31 patients with a primary end point 24 died from SCD, 6 had appropriate ICD therapy and 1 had VA. Arrhythmia risk prediction by strain imaging of the right ventricle Right ventricular free wall strain RVS was a strong predictor of arrhythmia. The better the RV function, the less likelihood of malignant arrhythmias. On Cox regression, a (numerical) reduction in negative strain of 1% was significantly associated with VA/SCD [hazard ratio (HR) 1.15, 95% CI 1.08–1.22; P < 0.001] and RVS remained independently associated with the outcome in a multivariable model including age and LV GLS (HR 1.08, 95% CI 1.01–1.15; P = 0.038) Table 2. Figure 3 shows the cumulative incidence function of the primary composite outcome SCD/VA stratified according to the tertiles of RVS. Patients in the lower tertile (poor strain) were highly prone to experience an event compared with patients in the upper tertile (HR 9.8, 95% CI 2.23–42.3; P = 0.002). Figure 3 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RVS. Reduced strain is associated with increased risk of an event. Figure 3 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RVS. Reduced strain is associated with increased risk of an event. None of the covariates Killip class >1, estimated glomerular filtration rate, QRS duration, heart rate, LV MD or e/eʹ were found to be independently associated with outcome when separately added to a model including age and LV GLS. LV MD was only borderline significant when added to a model including LV GLS, age and RVS (HR 1.15, 95% CI 0.99–1.34; P = 0.08). In a model with LVEF, (rather than LV GLS) and age, RVS was independently associated with outcome (HR 1.12, 95% CI 1.03–1.21; P = 0.005). The mean RVS in patients with an inferior MI (culprit lesion right coronary artery) was significantly reduced in comparison with patients with other culprit lesions (culprit lesion circumflex, left main or left anterior descending artery) −22.7 ± 5.8 vs. −24,4 ± 4.9, P < 0.001. However, an inferior MI was not significantly associated with an increased risk of VA/SCD (P = 0.11). RV mechanical dispersion RV MD as a continuous variable was not associated with outcome. However, patients with significantly increased dispersion >60 ms, corresponding to the upper tertile, had a 2.5-fold increased risk of VA/SCD compared with other patients. Figure 4 shows the cumulative incidence function of the primary composite outcome SCD/VA stratified according to tertiles of RV MD. However, a cut-off >60 ms for RV MD was not found to be independently associated with outcome. The correlation between RVS and mechanical dispersion was weak (r = 0.19, P < 0.001). Figure 4 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RV MD. The upper tertile of MD is associated with increased risk of VA or SCD. The mid and lower tertile are almost interposed. Figure 4 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RV MD. The upper tertile of MD is associated with increased risk of VA or SCD. The mid and lower tertile are almost interposed. RVS vs. conventional parameter TAPSE Overall, RVS showed a higher association with outcome compared with conventional assessment of RV function by TAPSE. By Cox regression analysis, TAPSE was univariately associated with VA/SCD (HR 1.12, 95% CI 1.03–1.22; P = 0.003), but in contrast to RVS, this parameter did not remain independently associated with the occurrence of arrhythmia in a model with LV GLS and age (HR 0.99, 95% CI 0.91–1.09; P = 0.93). RVS showed a significantly higher association with outcome in a multivariable model with LV GLS and age compared with the same model including TAPSE (P = 0.03). When comparing c-statistics, addition of RVS to a model including LV GLS, age and TAPSE caused an increase in AUC, although this difference was not statistically significant: AUC 0.80 (0.70–0.90) vs. 0.82 (0.72–0.91), P = 0.11. Figure 5 demonstrates the correlation between TAPSE and RVS in relation to the occurrence of VA/SCD. The correlation was not strong with a Spearman correlation of −0.445, indicating that these parameters may hold different information. Figure 5 View largeDownload slide The correlation between parameters of RV function; TAPSE and RVS in relation to whether patients experienced an event or not. Figure 5 View largeDownload slide The correlation between parameters of RV function; TAPSE and RVS in relation to whether patients experienced an event or not. Discussion This is the first study to demonstrate the importance of RV dysfunction for development of SCD or VA in a post-MI population. The main finding was that RV dysfunction, measured by RVS, was independently associated with VA/SCD and superior to TAPSE in the prediction of VA/SCD. Furthermore, RV MD was a significant predictor of VA/SCD in univariate but not in multivariable analyses. Although the RV is often referred to as ‘the forgotten ventricle’, recent studies have established the importance of RV function as an important determinant of clinical outcome in conditions such as pulmonary hypertension,4 heart failure4,5 and MI.12,13,17 RV function not only does reflect the intrinsic contractility but may also to some degree be viewed as a sensitive barometer of any ‘downstream pathology’ affecting RV afterload due to abnormal pulmonary vasculature, increased LV filling pressures and other causes.18 From the current study, it is evident that RV impairment is an important predictor of malignant arrhythmias independent of LV function. The relation between RV dysfunction and occurrence of VA/SCD may to some degree be explained by the presence of areas with interwoven scar tissue and viable myocardium constituting substrates for slowed conduction leading to malignant re-entry arrhythmias much similar to the LV. This is supported by a study investigating RV involvement (based on ECG) in patients after inferior MI, where patients had an increased risk of death, shock and arrhythmias compared with patients without RV involvement after MI.6 The results of the current study may also indicate that RV dysfunction in itself constitutes an arrhythmic substrate.7 In line with our findings, La Gerche et al.19 has reported that athletes with previous RV arrhythmia have RV contractile dysfunction that can be revealed by exercise testing. Previous evidence for the importance of RV dysfunction for the occurrence of VA/SCD is, however, sparse. In a smaller study of 222 patients with primary prophylactic ICD, Aktas et al.20 found RV dysfunction to be predictive of the combined end point of VA and all-cause death but not on ICD therapy alone. Doyle et al.7 suggested in a substudy from MADIT-CRT that enlarged RV size (as an indirect measure of RV function) was closely associated with the occurrence of VA or death and that this risk was reduced by resynchronization therapy when RV remodelling occurred. 2D strain analysis of the right ventricle RV function was evaluated by 2DSE. This technique has previously been proved to provide a valuable tool in the prediction of arrhythmic events when applied to the LV. In particular, GLS has been demonstrated to be a critical determinant of VA as well as superior to conventional parameters of LV function, such as LVEF, for risk stratification.10,14 The idea is that GLS includes information regarding subtle changes in myocardial function caused by the early deterioration of endocardial longitudinal fibre function and therefore is a more sensitive marker than conventional measures. Several previous studies have demonstrated that 2DSE can be applied to the RV in a simple and feasible manner similar to the LV.8,9 Two studies in post-MI cohorts recently reported that RV dysfunction assessed by 2DSE was closely associated with long-term survival and major cardiac events.13,21 One study by Park et al.13 included the entire RV in the analysis based on vector velocity imaging for deformation analysis, while the other by Antoni et al.21 addressed the RV free wall only by speckle tracking. There has been some contention over whether the interventricular septum should be included in the measurement of global RV function. We decided to use RVS, because the interventricular septum was included in LV GLS assessment. RV mechanical dispersion As part of RV strain evaluation, the prognostic value of RV mechanical dispersion was investigated. The mechanical dispersion is a measure of contractile heterogeneity and is determined by multiple factors such as the presence of scar tissue, fibrosis, electrical activation and loading conditions. An abnormal mechanical dispersion may reflect heterogeneities in the electrical conduction, which may lead to ventricular arrhythmias. Several studies, across different patient cohorts, have proposed that mechanical dispersion can be used in the prediction of arrhythmias when applied to the LV, although the clinical utility of this marker has been questioned lately.10,22,23 Only one study has reported on the predictive ability of RV mechanical dispersion.16 Sarvari et al.16 showed that arrhythmogenic right ventricular cardiomyopathy (ARVC) patients have increased RV MD compared with the healthy controls and that this is associated with increased occurrence of ventricular arrhythmias. The current cohort represents a very different pathophysiology, which may be the reason we do not find an important role for RV MD in post-AMI patients. Patients with RV MD in the upper tertile did have more than a 2-fold increased risk of VA/SCD compared with other patients. However, RV MD was not independently associated with outcome. RVS vs. conventional TAPSE Most previous studies have investigated RV dysfunction by TAPSE or right ventricular fractional area change (RVFAC), but recent studies indicate that the use of RVS may have a superior prognostic value compared to these traditional measures.6,7 The current study supports the use of RVS as a robust parameter of RV dysfunction. We found RVS to be superior to TAPSE for arrhythmia risk stratification. As opposed to RVS, TAPSE was not an independent risk marker in the multivariable model and when comparing TAPSE to RVS by log-rank statistics, a significant difference in performance between the two could be demonstrated in a multivariable model. Although not statically significant, c-statistics were in support of an incremental value of RVS to TAPSE. RVS and TAPSE hold different information regarding RV function. The primary reason is likely that RVS is less angle dependent than TAPSE, which is important due to the curvature of the RV. If RVS is available, it therefore appears to provide a better option than traditional measures. Limitations The primary outcome VA/SCD is relatively rare in a post-AMI cohort. This may to some degree have compromised the statistical analysis due to potential overfitting of the multivariable models and covariates with prognostic value may have been discarded. This is, however, the largest study to date addressing this topic. The time from presentation can be important, and surely, it is unknown whether an echo performed at another time would show similar results. The approach used in the present study ensured that patient echoes were comparable and the timing just before discharge, is in our opinion, a clinically relevant time for assessment of patient prognosis. It was only possible to obtain RVS in 80.5% of cases. This was primarily due to our strict criteria regarding RV free wall assessment, which required reliable tracking in all three segments (basal, midventricular and apical) to be included. It may be that reliable tracking in two segments can be a useful approach. This was not tested in the present study. In retrospect, not all studies were focused on the RV—four chambers only but some also included the right atrium.24 However, images were always optimized at frame rates between 50 and 90 frames/min. In addition, it could be argued that RVOT flow recordings would be more correct than using aortic flow recordings to define end systole, and this may have caused minor variations in RVS measurements. For evaluation of RV function, RVFAC is often reported.12,13 This measure was not included in the current study, because it was not part of the predefined echo analysis protocol. Instead, RVS was compared with TAPSE, which has been reported to be a strong clinical parameter of equal value for prediction of clinical outcome. We find that the lack of RVFAC is of limited importance for the overall conclusions, but surely no conclusions involving this parameter should be drawn based on the current study. Conclusion Assessment of RV function by RVS may improve the prediction of SCD/VA in patients with acute MI, but RV mechanical dispersion is not independently associated with outcome. The use of 2DSE for the assessment of RV function was found to have an incremental value in comparison with the conventional measure TAPSE for the prediction of VA or SCD. Conflict of interest: N.R. has delivered lectures at symposia sponsored by GE Healthcare. M.E. has delivered lectures at symposia sponsored by GE Healthcare. P.S. is a speaker for GE Healthcare and coordinating investigator for Biotronik. L.K. has delivered lectures at symposia sponsored by Servier. All other authors have reported that they have no relationships relevant to the contents of this article to disclose. Funding The authors thank the following for providing financial assistance with echocardiographic equipment and analytical software: Foundation Juchum, Switzerland; Beckett Fonden, Denmark; Toyota Fonden, Denmark and Aase og Ejnar Danielsens Fond, Denmark. References 1 Moss AJ, Zareba W, Hall WJ, Klein H, Wilber DJ, Cannom DS et al.   Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med  2002; 346: 877– 83. Google Scholar CrossRef Search ADS PubMed  2 Epstein AE, DiMarco JP, Ellenbogen KA, Estes NAIII, Freedman RA, Gettes LS et al.   2012 ACCF/AHA/HRS focused update incorporated into the ACCF/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol  2013; 61: e6– 75. Google Scholar CrossRef Search ADS PubMed  3 Buxton AE, Lee KL, Hafley GE, Pires LA, Fisher JD, Gold MR et al.   Limitations of ejection fraction for prediction of sudden death risk in patients with coronary artery disease: lessons from the MUSTT study. J Am Coll Cardiol  2007; 50: 1150– 7. Google Scholar CrossRef Search ADS PubMed  4 Ghio S, Temporelli PL, Klersy C, Simioniuc A, Girardi B, Scelsi L et al.   Prognostic relevance of a non-invasive evaluation of right ventricular function and pulmonary artery pressure in patients with chronic heart failure. Eur J Heart Fail  2013; 15: 408– 14. Google Scholar CrossRef Search ADS PubMed  5 Gulati A, Ismail TF, Jabbour A, Alpendurada F, Guha K, Ismail NA et al.   The prevalence and prognostic significance of right ventricular systolic dysfunction in nonischemic dilated cardiomyopathy. Circulation  2013; 128: 1623– 33. Google Scholar CrossRef Search ADS PubMed  6 Mehta SR, Eikelboom JW, Natarajan MK, Diaz R, Yi C, Gibbons RJ et al.   Impact of right ventricular involvement on mortality and morbidity in patients with inferior myocardial infarction. J Am Coll Cardiol  2001; 37: 37– 43. Google Scholar CrossRef Search ADS PubMed  7 Doyle CL, Huang DT, Moss AJ, Solomon SD, Campbell P, McNitt S et al.   Response of right ventricular size to treatment with cardiac resynchronization therapy and the risk of ventricular tachyarrhythmias in MADIT-CRT. Heart Rhythm  2013; 10: 1471– 7. Google Scholar CrossRef Search ADS PubMed  8 Rajagopal S, Forsha DE, Risum N, Hornik CP, Poms AD, Fortin TA et al.   Comprehensive assessment of right ventricular function in patients with pulmonary hypertension with global longitudinal peak systolic strain derived from multiple right ventricular views. J Am Soc Echocardiogr  2014; 27: 657– 65. Google Scholar CrossRef Search ADS PubMed  9 Forsha D, Risum N, Kropf PA, Rajagopal S, Smith PB, Kanter RJ et al.   Right ventricular mechanics using a novel comprehensive three-view echocardiographic strain analysis in a normal population. J Am Soc Echocardiogr  2014; 27: 413– 22. Google Scholar CrossRef Search ADS PubMed  10 Ersboll M, Valeur N, Andersen MJ, Mogensen UM, Vinther M, Svendsen JH et al.   Early echocardiographic deformation analysis for the prediction of sudden cardiac death and life-threatening arrhythmias after myocardial infarction. JACC Cardiovasc Imaging  2013; 6: 851– 60. Google Scholar CrossRef Search ADS PubMed  11 Haugaa KH, Grenne BL, Eek CH, Ersboll M, Valeur N, Svendsen JH et al.   Strain echocardiography improves risk prediction of ventricular arrhythmias after myocardial infarction. JACC Cardiovasc Imaging  2013; 6: 841– 50. Google Scholar CrossRef Search ADS PubMed  12 Antoni ML, Scherptong RW, Atary JZ, Boersma E, Holman ER, van der Wall EE et al.   Prognostic value of right ventricular function in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. Circ Cardiovasc Imaging  2010; 3: 264– 71. Google Scholar CrossRef Search ADS PubMed  13 Park SJ, Park JH, Lee HS, Kim MS, Park YK, Park Y et al.   Impaired RV STEMI. JACC Cardiovasc Imaging  2015; 8: 161– 9. Google Scholar CrossRef Search ADS PubMed  14 Ersboll M, Valeur N, Mogensen UM, Andersen MJ, Moller JE, Velazquez EJ et al.   Prediction of all-cause mortality and heart failure admissions from global left ventricular longitudinal strain in patients with acute myocardial infarction and preserved left ventricular ejection fraction. J Am Coll Cardiol  2013; 61: 2365– 73. Google Scholar CrossRef Search ADS PubMed  15 Risum N, Ali S, Olsen NT, Jons C, Khouri MG, Lauridsen TK et al.   Variability of global left ventricular deformation analysis using vendor dependent and independent two-dimensional speckle-tracking software in adults. J Am Soc Echocardiogr  2012; 25: 1195– 203. Google Scholar CrossRef Search ADS PubMed  16 Sarvari SI, Haugaa KH, Anfinsen OG, Leren TP, Smiseth OA, Kongsgaard E et al.   Right ventricular mechanical dispersion is related to malignant arrhythmias: a study of patients with arrhythmogenic right ventricular cardiomyopathy and subclinical right ventricular dysfunction. Eur Heart J  2011; 32: 1089– 96. Google Scholar CrossRef Search ADS PubMed  17 Zornoff LA, Skali H, Pfeffer MA, St John SM, Rouleau JL, Lamas GA et al.   Right ventricular dysfunction and risk of heart failure and mortality after myocardial infarction. J Am Coll Cardiol  2002; 39: 1450– 5. Google Scholar CrossRef Search ADS PubMed  18 La GA, Roberts TJ. Straining the RV to predict the future. JACC Cardiovasc Imaging  2015; 8: 170– 1. Google Scholar CrossRef Search ADS PubMed  19 La Gerche A, Claessen G, Dymarkowski S, Voigt JU, De BF, Vanhees L et al.   Exercise-induced right ventricular dysfunction is associated with ventricular arrhythmias in endurance athletes. Eur Heart J  2015; 36: 1998– 2010. Google Scholar CrossRef Search ADS PubMed  20 Aktas MK, Kim DD, McNitt S, Huang DT, Rosero SZ, Hall BW et al.   Right ventricular dysfunction and the incidence of implantable cardioverter-defibrillator therapies. Pacing Clin Electrophysiol  2009; 32: 1501– 8. Google Scholar CrossRef Search ADS PubMed  21 Antoni ML, Boden H, Delgado V, Boersma E, Fox K, Schalij MJ et al.   Relationship between discharge heart rate and mortality in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. Eur Heart J  2012; 33: 96– 102. Google Scholar CrossRef Search ADS PubMed  22 Biering-Sorensen T, Olsen FJ, Storm K, Fritz-Hansen T, Olsen NT, Jons C et al.   Prognostic value of tissue Doppler imaging for predicting ventricular arrhythmias and cardiovascular mortality in ischaemic cardiomyopathy. Eur Heart J Cardiovasc Imaging  2016; 17: 722– 31. Google Scholar CrossRef Search ADS PubMed  23 Kutyifa V, Pouleur AC, Knappe D, Al-Ahmad A, Gibinski M, Wang PJ et al.   Dyssynchrony and the risk of ventricular arrhythmias. JACC Cardiovasc Imaging  2013; 6: 432– 44. Google Scholar CrossRef Search ADS PubMed  24 Muraru D, Onciul S, Peluso D, Soriani N, Cucchini U, Aruta P, Romeo G, Cavalli G, Iliceto S, Badano LP. Sex- and method-specific reference values for right ventricular strain by 2-dimensional speckle-tracking echocardiography. Circ Cardiovasc Imaging  2016;doi: 10.1161/CIRCIMAGING.115.003866. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Heart Journal – Cardiovascular Imaging Oxford University Press

Right ventricular function assessed by 2D strain analysis predicts ventricular arrhythmias and sudden cardiac death in patients after acute myocardial infarction

Loading next page...
 
/lp/ou_press/right-ventricular-function-assessed-by-2d-strain-analysis-predicts-tPC7ueG025
Publisher
Oxford University Press
Copyright
Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.
ISSN
2047-2404
D.O.I.
10.1093/ehjci/jex184
Publisher site
See Article on Publisher Site

Abstract

Abstract Aims Left ventricular function is a well-established predictor of malignant ventricular arrhythmias, but little is known about the importance of right ventricular (RV) function. The aim of this study was to investigate the importance of RV function for prediction of sudden cardiac death (SCD) or malignant ventricular arrhythmias (VAs) after acute myocardial infarction (MI). Methods and results A total of 790 patients with acute MI were prospectively included. All patients had 2D strain echocardiography performed to evaluate right ventricular (RV) free wall strain (RVS) and RV mechanical dispersion (MD) defined as the standard deviation of time to peak negative strain in all myocardial segments. The primary composite end point [SCD, admission with VA or appropriate therapy from a primary prophylactic implantable cardioverter–defibrillator (ICD)] was analysed with Cox models. Mean age was 69 ± 12 years, and 74% were male. Thirty-one patients experienced the primary end point during a median follow-up of 898 days (Q1–Q3 704–981). RVS was independently associated with outcome in a multivariable model including age and left ventricular global longitudinal strain; pr 1% change [hazard ratio (HR) 1.08, 95% confidence interval (CI) 1.01–1.15; P = 0.038]. Patients in the lower tertile (poor strain) showed a 10-fold risk of an event compared with the upper tertile (HR 9.8, 95% CI 2.23–42.3; P = 0.002). RV MD was not independently associated with VA/SCD (HR 0.99, 95% CI 0.91–1.09; P = 0.93). RVS proved superior to tricuspid annular plane systolic excursion (TAPSE) (P = 0.03) in the multivariable model. Conclusion RVS, but not RV MD, was significantly and independently related to SCD/VA in patients with acute MI. Furthermore, RVS was shown to be superior to TAPSE. Introduction Patients who experience an acute myocardial infarction (MI) have an increased risk of malignant ventricular arrhythmias (VAs) that may lead to sudden cardiac death (SCD).1 Risk stratification in this population is important to determine whether patients may benefit from implantable cardioverter–defibrillator (ICD) therapy. Left ventricular (LV) dysfunction [ejection fraction (EF) < 35%] is the most established parameter for selection of ICD patients2 but has demonstrated limited ability in prediction of outcomes.3 Thus, the current selection criteria for ICD needs to be further refined. The importance of right ventricular (RV) function for the occurrence of VA/SCD in the post-MI population is largely unknown. An MI is often considered as an LV pathology, but studies have demonstrated that RV function is a critical determinant of outcome in conditions such as congestive heart failure4,5 and MI.6 Specifically with regard to arrhythmia, it has been proposed that patients with evidence of RV involvement after an inferior MI are at increased risk.6 Furthermore, in patients receiving cardiac resynchronization therapy, it has been proposed that favourable RV remodelling is related to reduced arrhythmic risk.7 It therefore seems plausible that RV dysfunction is an independent marker of VA/SCD in post-MI patients. For the purpose of evaluating RV function, deformation analysis by 2D strain echocardiography (2DSE) may be a valuable tool.8,9 Applied to the left ventricle, 2DSE-derived measures, such as the mechanical dispersion (MD) or global longitudinal strain (GLS), have been shown to improve risk stratification for the occurrence of arrhythmia.10,11 Recent studies have indicated that RV strain (RVS) may also be superior to traditional markers of global RV function in prediction of clinical outcomes.12,13 Based on a large contemporary cohort of patients with acute MI, we hypothesized that detailed analysis of RV deformation would provide important prognostic information in relation to malignant VA. Methods Study design and patient population As previously described,14 patients were prospectively enrolled from two tertiary cardiac centres when referred for invasive coronary angiography due to either ST-segment elevation or non–ST-segment elevation MI. Exclusion criteria were age <18 years, non-cardiac disease with a life expectancy <1 year and an inability to provide written informed consent. Patients with atrial fibrillation, severe valve disease and paced rhythm at the time of the echocardiography were excluded. In addition, the patients with suboptimal image quality for 2D strain analysis of either the right ventricle or the left ventricle were excluded (Figure 1). Figure 1 View largeDownload slide Flowchart displaying the selection of patients. MI, myocardial infarction; AS, aortic stenosis; MR, mitral valve regurgitation; LV, left ventricular; RV, right ventricular. Figure 1 View largeDownload slide Flowchart displaying the selection of patients. MI, myocardial infarction; AS, aortic stenosis; MR, mitral valve regurgitation; LV, left ventricular; RV, right ventricular. Demographics were acquired from chart reviews regarding diabetes, hypertension, history of ischaemic heart disease or MI and objective signs of heart failure (Killip class). Information related to the coronary angiography, including culprit lesion, number of diseased vessels, left main involvement and type of revascularization (percutaneous coronary intervention, coronary artery bypass grafting or no intervention) were registered. Peak troponin I level was measured in 220 (28%) patients, peak troponin T level was measured in 558 (71%) patients and 12 (1%) patients had missing values. All patients provided written informed consent. The study protocol was approved by the regional scientific ethics committee (reference no. H-D-2009-063). Echocardiography Echocardiography was performed within 48 h of admission to the tertiary centre. Three consecutive heart cycles were acquired. All examinations were performed on a Vivid e9 system (General Electric, Horten, Norway). Images were obtained at a frame rate of at least 50 frames/s and analysed offline (Echopac BT 11.1.0, General Electric). All analyses were performed by a single experienced operator (M.E.) who was blinded to follow-up information. Measurements of LV volumes and function, LVEF, LV end-diastolic volume (EDV) and LV end-systolic volume (ESV) were performed using biplane Simpson model. 2D strain analysis The 2D strain analysis was performed on both the LV in the three apical (long-axis, 4-chamber, and 2-chamber) views15 and the RV four-chamber view.16 All analyses, for both RV and LV, were optimized to be performed at a frame rate of 50–90 frames/min. Aortic valve closure was identified from continuous-wave Doppler recordings through the aortic valve. The endocardial border was traced in end systole and the automatically generated region of interest (ROI) was adjusted to exclude the pericardium. The integrity of speckle tracking was automatically detected and visually ascertained. In case of poor tracking, the ROI tracing was readjusted. Segments with persistent inadequate tracking were excluded from analysis. LV strain analysis The LV was divided into 17 segments covering the entire myocardium, and GLS was calculated as the mean of the global peak systolic strain from each of the three views. If global peak systolic strain could be assessed only in two of the three apical projections, GLS was calculated as the mean of these two. If global peak systolic strain could not be assessed in at least two of the apical projections, the patient was excluded from the study due to suboptimal image quality. The MD was calculated as the standard deviation (SD) of the time-to-peak strain measured from the peak electrocardiographic (ECG) R-wave to peak negative strain for all segments. If six or more segments did not have sufficient tracking, then the measure was excluded.10 RV strain analysis The RVS was used as a measure of global RV function, because the septal wall was included in the analysis of LV GLS, which has previously been shown to be the strongest predictor of arrhythmia in the current cohort.10 The measurement of RVS required all three segments in the RV free wall (basal, midventricular and apical) to be reliably tracked or else it was excluded. RV MD included all the six RV segments and was calculated as the SD of the time-to-peak strain measured from the ECG peak R-wave to peak negative strain for each segment (Figure 2). If more than 2 segments were insufficiently tracked, the measurement was excluded. Figure 2 View largeDownload slide The 2D strain echocardiography applied to the right ventricle (left panel). The mid panel shows right ventricular free wall strain. The right panel shows right ventricular mechanical dispersion, white dots indicating peak deformation. Figure 2 View largeDownload slide The 2D strain echocardiography applied to the right ventricle (left panel). The mid panel shows right ventricular free wall strain. The right panel shows right ventricular mechanical dispersion, white dots indicating peak deformation. Intra- and interobserver reproducibility The intra- and inter-observer reproducibility has previously been described in detail12 and was good with coefficients of variation of 6% for intra-observer variability and 9% for inter-observer variability, respectively. Definition of end point The primary outcome was a composite of definite or suspected SCD, admission with documented VA or appropriate ICD discharge only in patients with a primary prophylactic ICD.10 The cause of death was ascertained from hospital and pre-hospital patient records by two independent reviewers who were blinded to echocardiographic data, and information on all-cause mortality was obtained from the Danish Civil Registration System. For patients with a primary prophylactic ICD, the device was interrogated by an experienced electrophysiologist blinded to echocardiographic data. Statistical analysis Descriptive statistics Relevant variables were tested for normality using visual inspection of histogram plots and are presented as mean ± SD or median [first and third quartiles (Q1–Q3)]. Continuous variables were compared using Student’s t-test. Proportional differences were tested using χ2 statistics or Fisher’s exact test where appropriate. All tests were two sided, and statistical significance was defined as P < 0.05. Primary composite end point The primary end point was a composite of VA or SCD. Proportional hazards assumptions were verified graphically. Cause-specific Cox regression models allowing for competing risks were used to identify univariate predictors of the primary composite outcome. The competing risk associated with the primary composite outcome was death from all causes other than SCD. Unadjusted cumulative incidence curves were calculated for the primary composite outcome stratified by tertiles of RVS and RV MD. Candidate variables with P-values of <0.05 in univariate analysis were included in the multivariable model using backward selection to test the independent association between outcome and RVS and RV MD, respectively. Receiver operating characteristic curve analysis with the use of a non-parametric estimate of the area under curve (AUC) and c-statistics with 95% confidence interval (CI) was performed for the multivariable model. For comparison between RVS and tricuspid annular plane systolic excursion (TAPSE), the strength of association with outcome for each parameter was compared using −2 log likelihood statistics. All statistical analyses were performed using SAS for Windows version 9.3 (SAS institute, Cary, NC, USA). Results Baseline characteristics and outcome Of the 1110 patients, 790 patients with complete 2DSE data sets were included. Figure 1 shows a flowchart for patient selection. Mean age was 69 ± 12 years, 588 (74%) patients were male and 541 (69%) patients had ST-elevation MI. The baseline characteristics according to whether the patients experienced an end point or not are presented in Table 1. Overall, patients who had an event were characterized by being older, more symptomatic (Killip class), having lower renal function and reduced myocardial function including both RV and LV function. With regard to RV function specifically, RVS and TAPSE were markedly lower in those with arrhythmic events compared with those without (−24.1 ± 5 vs. −19.8 ± 5, P < 0.0001 and 22 ± 4 vs. 20 ± 6, P < 0.0001), respectively. No significant differences were found in RV MD between patients with an event compared with those without (52 ± 23 vs. 58 ± 22, P = 0.2). Table 1 Baseline characteristics according to no event/VA or SCD   No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001    No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001  Values are mean SD, n (%) or median (Q1–Q3). eGFR, estimated glomerular filtration rate; HR, heart rate; 3VD, 3-vessel disease; CABG, coronary artery bypass grafting; ECG, electrocardiography; LV, left ventricular; RV, right ventricular; GLS, global longitudinal strain; LAD, left anterior descending; LVEF, left ventricular ejection fraction; MD, mechanical dispersion; MI, myocardial infarction; PCI, percutaneous coronary intervention; WMSI, wall motion score index; LVEF, left ventricular ejection fraction; RVS, right ventricle free wall strain. Table 1 Baseline characteristics according to no event/VA or SCD   No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001    No event (n = 759)  SCD or VA (n = 31)  P-value  Age  62.1 ± 12  69.4 ± 10  <0.0001  Male  568 (75)  20 (65)  0.19  Hypertension  327 (43)  19 (61)  0.05  Diabetes  98 (13)  6 (19)  0.28  Previous MI  96 (13)  9 (29)  0.008  Killip class  1.2 ± 0.6  1.6 ± 0.9  <0.0001  eGFR  89 ± 27  72 ± 31  <0.0001  ECG findings   QRS duration  98 ± 20  107 ± 22  0.02   QTc duration  425 ± 33  423 ± 32  0.87   QRS >120 ms  45 (5.9)  5 (16.1)  0.02   HR  75 ± 15  84 ± 19  0.002  Infarct/intervention   CABG  65 (9)  3 (10)  0.82   pPCI  477 (63)  16 (52)  0.43   PCI  122 (16)  6 (19)   noPCI  160 (21)  9 (29)   3VD  119 (16)  9 (29)  0.048   RCA culprit  261 (34)  5 (16)  0.08   LAD culprit  311 (41)  17 (55)  0.12   tnt  2.3 (0.5–5.7)  6.4 (0.8–11.6)  0.05   tni  39 (7–168)  75 (7–271)  0.44  Echocardiography   TAPSE, mm  22 ± 4  20 ± 6  0.005   e/eʹ  10 ± 4  16 ± 7  <0.0001   RV mechanical dispersion  52 ± 23  58 ± 22  0.2   Free wall RVS  −24.1 ± 5  −19.8 ± 5  <0.0001   LV GLS  −13.7 ± 3  −9.6 ± 4  <0.0001   WMSI  1.4 ± 0.3  1.7 ± 0.4  <0.0001   LVEF  50 ± 10  41 ± 13  <0.0001   LV MD  56.0 ± 15  69.0 ± 29  <0.0001  Values are mean SD, n (%) or median (Q1–Q3). eGFR, estimated glomerular filtration rate; HR, heart rate; 3VD, 3-vessel disease; CABG, coronary artery bypass grafting; ECG, electrocardiography; LV, left ventricular; RV, right ventricular; GLS, global longitudinal strain; LAD, left anterior descending; LVEF, left ventricular ejection fraction; MD, mechanical dispersion; MI, myocardial infarction; PCI, percutaneous coronary intervention; WMSI, wall motion score index; LVEF, left ventricular ejection fraction; RVS, right ventricle free wall strain. Table 2 Univariate and multivariate risk analysis of VA/SCD Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        HR, hazard ratio; 95% CI, confidence interval. Abbreviations as in Table 1. Table 2 Univariate and multivariate risk analysis of VA/SCD Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        Covariates  Univariate analysis   Multivariate analysis   HR  95% CI  P-value  HR  95% CI  P-value  Age pr year  1.06  1.04–1.09  <0.0001  1.04  1.01–1.08  0.02  Male  0.58  0.27–1.21  0.15        Killip class >1  4.02  1.94–8.36  < 0.001        RCA involvement  2.26  0.84–6.1  0.11        3VD or LM  2.27  1.04–4.95  0.04        Diabetes  1.68  0.69–4.10  0.26        Previous MI  2.53  1.12–5.68  0.02        QRS >120 ms  2.98  1.14–7.76  0.03        eGFR  0.97  0.96–0.99  0.002        LVEF  0.93  0.90–0.96  <0.001        LV GLS  1.39  1.25–1.55  <0.001  1.32  1.17–1.49  <0.001  WMSI/0.1  1.39  1.23–1.56  <0.001        TAPSE/1 mm  1.13  1.04–1.23  0.003        RVS/1%  1.15  1.08–1.22  <0.0001  1.08  1.00–1.15  0.04  RV MD > 60 ms  2.50  1.25–5.2  0.01        LV MD/10 ms  1.35  1.20–1.52  <0.0001        HR, hazard ratio; 95% CI, confidence interval. Abbreviations as in Table 1. The median follow-up was 898 (Q1–Q3 704–981) days, and no patients were lost to follow-up. Overall, 31 patients experienced the primary end point. Six (19%) patients experienced the primary end point during the first 3 months. Of the total 31 events, 24 (77%) were attributed to SCD. Of the entire cohort, a total of 29 (3.6%) patients had a primary prophylactic ICD implanted during follow-up, of whom 6 of the 29 (21%) patients received appropriate therapy: four had an ICD shock and 2 received anti-tachycardia pacing. Thus, of the 31 patients with a primary end point 24 died from SCD, 6 had appropriate ICD therapy and 1 had VA. Arrhythmia risk prediction by strain imaging of the right ventricle Right ventricular free wall strain RVS was a strong predictor of arrhythmia. The better the RV function, the less likelihood of malignant arrhythmias. On Cox regression, a (numerical) reduction in negative strain of 1% was significantly associated with VA/SCD [hazard ratio (HR) 1.15, 95% CI 1.08–1.22; P < 0.001] and RVS remained independently associated with the outcome in a multivariable model including age and LV GLS (HR 1.08, 95% CI 1.01–1.15; P = 0.038) Table 2. Figure 3 shows the cumulative incidence function of the primary composite outcome SCD/VA stratified according to the tertiles of RVS. Patients in the lower tertile (poor strain) were highly prone to experience an event compared with patients in the upper tertile (HR 9.8, 95% CI 2.23–42.3; P = 0.002). Figure 3 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RVS. Reduced strain is associated with increased risk of an event. Figure 3 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RVS. Reduced strain is associated with increased risk of an event. None of the covariates Killip class >1, estimated glomerular filtration rate, QRS duration, heart rate, LV MD or e/eʹ were found to be independently associated with outcome when separately added to a model including age and LV GLS. LV MD was only borderline significant when added to a model including LV GLS, age and RVS (HR 1.15, 95% CI 0.99–1.34; P = 0.08). In a model with LVEF, (rather than LV GLS) and age, RVS was independently associated with outcome (HR 1.12, 95% CI 1.03–1.21; P = 0.005). The mean RVS in patients with an inferior MI (culprit lesion right coronary artery) was significantly reduced in comparison with patients with other culprit lesions (culprit lesion circumflex, left main or left anterior descending artery) −22.7 ± 5.8 vs. −24,4 ± 4.9, P < 0.001. However, an inferior MI was not significantly associated with an increased risk of VA/SCD (P = 0.11). RV mechanical dispersion RV MD as a continuous variable was not associated with outcome. However, patients with significantly increased dispersion >60 ms, corresponding to the upper tertile, had a 2.5-fold increased risk of VA/SCD compared with other patients. Figure 4 shows the cumulative incidence function of the primary composite outcome SCD/VA stratified according to tertiles of RV MD. However, a cut-off >60 ms for RV MD was not found to be independently associated with outcome. The correlation between RVS and mechanical dispersion was weak (r = 0.19, P < 0.001). Figure 4 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RV MD. The upper tertile of MD is associated with increased risk of VA or SCD. The mid and lower tertile are almost interposed. Figure 4 View largeDownload slide Cumulative incidence plot of VA or SCD stratified according to tertiles of RV MD. The upper tertile of MD is associated with increased risk of VA or SCD. The mid and lower tertile are almost interposed. RVS vs. conventional parameter TAPSE Overall, RVS showed a higher association with outcome compared with conventional assessment of RV function by TAPSE. By Cox regression analysis, TAPSE was univariately associated with VA/SCD (HR 1.12, 95% CI 1.03–1.22; P = 0.003), but in contrast to RVS, this parameter did not remain independently associated with the occurrence of arrhythmia in a model with LV GLS and age (HR 0.99, 95% CI 0.91–1.09; P = 0.93). RVS showed a significantly higher association with outcome in a multivariable model with LV GLS and age compared with the same model including TAPSE (P = 0.03). When comparing c-statistics, addition of RVS to a model including LV GLS, age and TAPSE caused an increase in AUC, although this difference was not statistically significant: AUC 0.80 (0.70–0.90) vs. 0.82 (0.72–0.91), P = 0.11. Figure 5 demonstrates the correlation between TAPSE and RVS in relation to the occurrence of VA/SCD. The correlation was not strong with a Spearman correlation of −0.445, indicating that these parameters may hold different information. Figure 5 View largeDownload slide The correlation between parameters of RV function; TAPSE and RVS in relation to whether patients experienced an event or not. Figure 5 View largeDownload slide The correlation between parameters of RV function; TAPSE and RVS in relation to whether patients experienced an event or not. Discussion This is the first study to demonstrate the importance of RV dysfunction for development of SCD or VA in a post-MI population. The main finding was that RV dysfunction, measured by RVS, was independently associated with VA/SCD and superior to TAPSE in the prediction of VA/SCD. Furthermore, RV MD was a significant predictor of VA/SCD in univariate but not in multivariable analyses. Although the RV is often referred to as ‘the forgotten ventricle’, recent studies have established the importance of RV function as an important determinant of clinical outcome in conditions such as pulmonary hypertension,4 heart failure4,5 and MI.12,13,17 RV function not only does reflect the intrinsic contractility but may also to some degree be viewed as a sensitive barometer of any ‘downstream pathology’ affecting RV afterload due to abnormal pulmonary vasculature, increased LV filling pressures and other causes.18 From the current study, it is evident that RV impairment is an important predictor of malignant arrhythmias independent of LV function. The relation between RV dysfunction and occurrence of VA/SCD may to some degree be explained by the presence of areas with interwoven scar tissue and viable myocardium constituting substrates for slowed conduction leading to malignant re-entry arrhythmias much similar to the LV. This is supported by a study investigating RV involvement (based on ECG) in patients after inferior MI, where patients had an increased risk of death, shock and arrhythmias compared with patients without RV involvement after MI.6 The results of the current study may also indicate that RV dysfunction in itself constitutes an arrhythmic substrate.7 In line with our findings, La Gerche et al.19 has reported that athletes with previous RV arrhythmia have RV contractile dysfunction that can be revealed by exercise testing. Previous evidence for the importance of RV dysfunction for the occurrence of VA/SCD is, however, sparse. In a smaller study of 222 patients with primary prophylactic ICD, Aktas et al.20 found RV dysfunction to be predictive of the combined end point of VA and all-cause death but not on ICD therapy alone. Doyle et al.7 suggested in a substudy from MADIT-CRT that enlarged RV size (as an indirect measure of RV function) was closely associated with the occurrence of VA or death and that this risk was reduced by resynchronization therapy when RV remodelling occurred. 2D strain analysis of the right ventricle RV function was evaluated by 2DSE. This technique has previously been proved to provide a valuable tool in the prediction of arrhythmic events when applied to the LV. In particular, GLS has been demonstrated to be a critical determinant of VA as well as superior to conventional parameters of LV function, such as LVEF, for risk stratification.10,14 The idea is that GLS includes information regarding subtle changes in myocardial function caused by the early deterioration of endocardial longitudinal fibre function and therefore is a more sensitive marker than conventional measures. Several previous studies have demonstrated that 2DSE can be applied to the RV in a simple and feasible manner similar to the LV.8,9 Two studies in post-MI cohorts recently reported that RV dysfunction assessed by 2DSE was closely associated with long-term survival and major cardiac events.13,21 One study by Park et al.13 included the entire RV in the analysis based on vector velocity imaging for deformation analysis, while the other by Antoni et al.21 addressed the RV free wall only by speckle tracking. There has been some contention over whether the interventricular septum should be included in the measurement of global RV function. We decided to use RVS, because the interventricular septum was included in LV GLS assessment. RV mechanical dispersion As part of RV strain evaluation, the prognostic value of RV mechanical dispersion was investigated. The mechanical dispersion is a measure of contractile heterogeneity and is determined by multiple factors such as the presence of scar tissue, fibrosis, electrical activation and loading conditions. An abnormal mechanical dispersion may reflect heterogeneities in the electrical conduction, which may lead to ventricular arrhythmias. Several studies, across different patient cohorts, have proposed that mechanical dispersion can be used in the prediction of arrhythmias when applied to the LV, although the clinical utility of this marker has been questioned lately.10,22,23 Only one study has reported on the predictive ability of RV mechanical dispersion.16 Sarvari et al.16 showed that arrhythmogenic right ventricular cardiomyopathy (ARVC) patients have increased RV MD compared with the healthy controls and that this is associated with increased occurrence of ventricular arrhythmias. The current cohort represents a very different pathophysiology, which may be the reason we do not find an important role for RV MD in post-AMI patients. Patients with RV MD in the upper tertile did have more than a 2-fold increased risk of VA/SCD compared with other patients. However, RV MD was not independently associated with outcome. RVS vs. conventional TAPSE Most previous studies have investigated RV dysfunction by TAPSE or right ventricular fractional area change (RVFAC), but recent studies indicate that the use of RVS may have a superior prognostic value compared to these traditional measures.6,7 The current study supports the use of RVS as a robust parameter of RV dysfunction. We found RVS to be superior to TAPSE for arrhythmia risk stratification. As opposed to RVS, TAPSE was not an independent risk marker in the multivariable model and when comparing TAPSE to RVS by log-rank statistics, a significant difference in performance between the two could be demonstrated in a multivariable model. Although not statically significant, c-statistics were in support of an incremental value of RVS to TAPSE. RVS and TAPSE hold different information regarding RV function. The primary reason is likely that RVS is less angle dependent than TAPSE, which is important due to the curvature of the RV. If RVS is available, it therefore appears to provide a better option than traditional measures. Limitations The primary outcome VA/SCD is relatively rare in a post-AMI cohort. This may to some degree have compromised the statistical analysis due to potential overfitting of the multivariable models and covariates with prognostic value may have been discarded. This is, however, the largest study to date addressing this topic. The time from presentation can be important, and surely, it is unknown whether an echo performed at another time would show similar results. The approach used in the present study ensured that patient echoes were comparable and the timing just before discharge, is in our opinion, a clinically relevant time for assessment of patient prognosis. It was only possible to obtain RVS in 80.5% of cases. This was primarily due to our strict criteria regarding RV free wall assessment, which required reliable tracking in all three segments (basal, midventricular and apical) to be included. It may be that reliable tracking in two segments can be a useful approach. This was not tested in the present study. In retrospect, not all studies were focused on the RV—four chambers only but some also included the right atrium.24 However, images were always optimized at frame rates between 50 and 90 frames/min. In addition, it could be argued that RVOT flow recordings would be more correct than using aortic flow recordings to define end systole, and this may have caused minor variations in RVS measurements. For evaluation of RV function, RVFAC is often reported.12,13 This measure was not included in the current study, because it was not part of the predefined echo analysis protocol. Instead, RVS was compared with TAPSE, which has been reported to be a strong clinical parameter of equal value for prediction of clinical outcome. We find that the lack of RVFAC is of limited importance for the overall conclusions, but surely no conclusions involving this parameter should be drawn based on the current study. Conclusion Assessment of RV function by RVS may improve the prediction of SCD/VA in patients with acute MI, but RV mechanical dispersion is not independently associated with outcome. The use of 2DSE for the assessment of RV function was found to have an incremental value in comparison with the conventional measure TAPSE for the prediction of VA or SCD. Conflict of interest: N.R. has delivered lectures at symposia sponsored by GE Healthcare. M.E. has delivered lectures at symposia sponsored by GE Healthcare. P.S. is a speaker for GE Healthcare and coordinating investigator for Biotronik. L.K. has delivered lectures at symposia sponsored by Servier. All other authors have reported that they have no relationships relevant to the contents of this article to disclose. Funding The authors thank the following for providing financial assistance with echocardiographic equipment and analytical software: Foundation Juchum, Switzerland; Beckett Fonden, Denmark; Toyota Fonden, Denmark and Aase og Ejnar Danielsens Fond, Denmark. References 1 Moss AJ, Zareba W, Hall WJ, Klein H, Wilber DJ, Cannom DS et al.   Prophylactic implantation of a defibrillator in patients with myocardial infarction and reduced ejection fraction. N Engl J Med  2002; 346: 877– 83. Google Scholar CrossRef Search ADS PubMed  2 Epstein AE, DiMarco JP, Ellenbogen KA, Estes NAIII, Freedman RA, Gettes LS et al.   2012 ACCF/AHA/HRS focused update incorporated into the ACCF/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol  2013; 61: e6– 75. Google Scholar CrossRef Search ADS PubMed  3 Buxton AE, Lee KL, Hafley GE, Pires LA, Fisher JD, Gold MR et al.   Limitations of ejection fraction for prediction of sudden death risk in patients with coronary artery disease: lessons from the MUSTT study. J Am Coll Cardiol  2007; 50: 1150– 7. Google Scholar CrossRef Search ADS PubMed  4 Ghio S, Temporelli PL, Klersy C, Simioniuc A, Girardi B, Scelsi L et al.   Prognostic relevance of a non-invasive evaluation of right ventricular function and pulmonary artery pressure in patients with chronic heart failure. Eur J Heart Fail  2013; 15: 408– 14. Google Scholar CrossRef Search ADS PubMed  5 Gulati A, Ismail TF, Jabbour A, Alpendurada F, Guha K, Ismail NA et al.   The prevalence and prognostic significance of right ventricular systolic dysfunction in nonischemic dilated cardiomyopathy. Circulation  2013; 128: 1623– 33. Google Scholar CrossRef Search ADS PubMed  6 Mehta SR, Eikelboom JW, Natarajan MK, Diaz R, Yi C, Gibbons RJ et al.   Impact of right ventricular involvement on mortality and morbidity in patients with inferior myocardial infarction. J Am Coll Cardiol  2001; 37: 37– 43. Google Scholar CrossRef Search ADS PubMed  7 Doyle CL, Huang DT, Moss AJ, Solomon SD, Campbell P, McNitt S et al.   Response of right ventricular size to treatment with cardiac resynchronization therapy and the risk of ventricular tachyarrhythmias in MADIT-CRT. Heart Rhythm  2013; 10: 1471– 7. Google Scholar CrossRef Search ADS PubMed  8 Rajagopal S, Forsha DE, Risum N, Hornik CP, Poms AD, Fortin TA et al.   Comprehensive assessment of right ventricular function in patients with pulmonary hypertension with global longitudinal peak systolic strain derived from multiple right ventricular views. J Am Soc Echocardiogr  2014; 27: 657– 65. Google Scholar CrossRef Search ADS PubMed  9 Forsha D, Risum N, Kropf PA, Rajagopal S, Smith PB, Kanter RJ et al.   Right ventricular mechanics using a novel comprehensive three-view echocardiographic strain analysis in a normal population. J Am Soc Echocardiogr  2014; 27: 413– 22. Google Scholar CrossRef Search ADS PubMed  10 Ersboll M, Valeur N, Andersen MJ, Mogensen UM, Vinther M, Svendsen JH et al.   Early echocardiographic deformation analysis for the prediction of sudden cardiac death and life-threatening arrhythmias after myocardial infarction. JACC Cardiovasc Imaging  2013; 6: 851– 60. Google Scholar CrossRef Search ADS PubMed  11 Haugaa KH, Grenne BL, Eek CH, Ersboll M, Valeur N, Svendsen JH et al.   Strain echocardiography improves risk prediction of ventricular arrhythmias after myocardial infarction. JACC Cardiovasc Imaging  2013; 6: 841– 50. Google Scholar CrossRef Search ADS PubMed  12 Antoni ML, Scherptong RW, Atary JZ, Boersma E, Holman ER, van der Wall EE et al.   Prognostic value of right ventricular function in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. Circ Cardiovasc Imaging  2010; 3: 264– 71. Google Scholar CrossRef Search ADS PubMed  13 Park SJ, Park JH, Lee HS, Kim MS, Park YK, Park Y et al.   Impaired RV STEMI. JACC Cardiovasc Imaging  2015; 8: 161– 9. Google Scholar CrossRef Search ADS PubMed  14 Ersboll M, Valeur N, Mogensen UM, Andersen MJ, Moller JE, Velazquez EJ et al.   Prediction of all-cause mortality and heart failure admissions from global left ventricular longitudinal strain in patients with acute myocardial infarction and preserved left ventricular ejection fraction. J Am Coll Cardiol  2013; 61: 2365– 73. Google Scholar CrossRef Search ADS PubMed  15 Risum N, Ali S, Olsen NT, Jons C, Khouri MG, Lauridsen TK et al.   Variability of global left ventricular deformation analysis using vendor dependent and independent two-dimensional speckle-tracking software in adults. J Am Soc Echocardiogr  2012; 25: 1195– 203. Google Scholar CrossRef Search ADS PubMed  16 Sarvari SI, Haugaa KH, Anfinsen OG, Leren TP, Smiseth OA, Kongsgaard E et al.   Right ventricular mechanical dispersion is related to malignant arrhythmias: a study of patients with arrhythmogenic right ventricular cardiomyopathy and subclinical right ventricular dysfunction. Eur Heart J  2011; 32: 1089– 96. Google Scholar CrossRef Search ADS PubMed  17 Zornoff LA, Skali H, Pfeffer MA, St John SM, Rouleau JL, Lamas GA et al.   Right ventricular dysfunction and risk of heart failure and mortality after myocardial infarction. J Am Coll Cardiol  2002; 39: 1450– 5. Google Scholar CrossRef Search ADS PubMed  18 La GA, Roberts TJ. Straining the RV to predict the future. JACC Cardiovasc Imaging  2015; 8: 170– 1. Google Scholar CrossRef Search ADS PubMed  19 La Gerche A, Claessen G, Dymarkowski S, Voigt JU, De BF, Vanhees L et al.   Exercise-induced right ventricular dysfunction is associated with ventricular arrhythmias in endurance athletes. Eur Heart J  2015; 36: 1998– 2010. Google Scholar CrossRef Search ADS PubMed  20 Aktas MK, Kim DD, McNitt S, Huang DT, Rosero SZ, Hall BW et al.   Right ventricular dysfunction and the incidence of implantable cardioverter-defibrillator therapies. Pacing Clin Electrophysiol  2009; 32: 1501– 8. Google Scholar CrossRef Search ADS PubMed  21 Antoni ML, Boden H, Delgado V, Boersma E, Fox K, Schalij MJ et al.   Relationship between discharge heart rate and mortality in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. Eur Heart J  2012; 33: 96– 102. Google Scholar CrossRef Search ADS PubMed  22 Biering-Sorensen T, Olsen FJ, Storm K, Fritz-Hansen T, Olsen NT, Jons C et al.   Prognostic value of tissue Doppler imaging for predicting ventricular arrhythmias and cardiovascular mortality in ischaemic cardiomyopathy. Eur Heart J Cardiovasc Imaging  2016; 17: 722– 31. Google Scholar CrossRef Search ADS PubMed  23 Kutyifa V, Pouleur AC, Knappe D, Al-Ahmad A, Gibinski M, Wang PJ et al.   Dyssynchrony and the risk of ventricular arrhythmias. JACC Cardiovasc Imaging  2013; 6: 432– 44. Google Scholar CrossRef Search ADS PubMed  24 Muraru D, Onciul S, Peluso D, Soriani N, Cucchini U, Aruta P, Romeo G, Cavalli G, Iliceto S, Badano LP. Sex- and method-specific reference values for right ventricular strain by 2-dimensional speckle-tracking echocardiography. Circ Cardiovasc Imaging  2016;doi: 10.1161/CIRCIMAGING.115.003866. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Journal

European Heart Journal – Cardiovascular ImagingOxford University Press

Published: Jul 31, 2017

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off