chapter 5 5.pdf · 87 5 cate 5 impaired hyperemic mbf and va inducibility introduction sudden...

CHAPTER 5

Mischa T. Rijnierse

Stefan de Haan

Hendrik J. Harms

Lourens F. Robbers

LiNa Wu

Ibrahim Danad

Aernout M. Beek

Martijn W. Heymans

Albert C. van Rossum

Adriaan A. Lammertsma

Cornelis P. Allaart

Paul Knaapen

CIRCULATION: CARDIOVASCULAR IMAGING. 2014 JAN;7(1):20-30.

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ABSTRACTBackground: Risk stratification for ventricular arrhythmias (VAs) is important to refine selection criteria for primary prevention implantable cardioverter defibrillator therapy. Impaired hyperemic myocardial blood flow (MBF) is associated with increased mortality rate in ischemic and nonischemic cardiomyopathy, which may be attributed to electrical instability inducing VAs. The aim of this pilot study was to assess whether hyperemic MBF impairment may be related with VA inducibility in patients with ischemic cardiomyopathy.

Methods and Results: Thirty patients with ischemic cardiomyopathy referred for primary prevention implantable cardioverter-defibrillator implantation were prospec-tively included (26 men, 65 ± 8 years old, left ventricular ejection fraction 29 ± 6%). [15O]H2O positron emission tomography was performed to quantify resting MBF, hyperemic MBF, and coronary flow reserve (CFR). Left ventricular dimensions, function, and scar burden were assessed with cardiovascular magnetic resonance imaging. An electrophys-iological study (EPS) was performed to test VA inducibility. Positive EPS patients (n=12) showed reduced hyperemic MBF (1.25 ± 0.30 vs. 1.66 ± 0.38 mL∙min−1∙g−1, P<0.01) and CFR (1.59 ± 0.49 vs. 2.12 ± 0.48, P<0.01) compared with EPS negative patients (n=18). In EPS positive patients, the number of scar segments >75% transmurality was higher (p<0.05) although scar size and border zone did not differ. ROC curve analysis indicated that impaired hyperemic MBF (AUC 0.84, 95% CI 0.69-0.99) and CFR (AUC 0.77, 95% CI 0.57-0.96) were associated with VA inducibility.

Conclusion: In this pilot study, impaired hyperemic MBF and CFR were associated with VA inducibility in patients with ischemic cardiomyopathy. These results are hypothesis gener-ating for a potential role of quantitative PET perfusion imaging in risk stratification for VA.

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CHAPTER 5 Impaired hyperemic MBF and VA inducibility

INTRODUCTIONSudden cardiac death (SCD) attributable to ventricular arrhythmia (VA) is a feared compli-cation of ischemic cardiomyopathy.1 Large clinical trials have shown that the implantable cardioverter defibrillator (ICD) therapy significantly reduced mortality in patients with cardiomyopathy.2, 3 As a result, current guidelines recommend ICD implantation for primary prevention of SCD in patients with left ventricular ejection fraction (LVEF) below 35%.4 It has become increasingly clear, however, that selection of patients eligible for ICD therapy solely based on impaired LVEF is suboptimal since a substantial number of ICD patients never receive appropriate therapy.5 Therefore, improved risk stratification for VA is essential to identify patients most likely to benefit from ICD implantation.

Myocardial perfusion abnormalities may serve as an important substrate for VA as they result in dispersion of repolarization leading to electrical instability.6-8 Impaired hyperemic myocardial blood flow (MBF) and coronary flow reserve (CFR) as assessed with positron emission tomography (PET) are powerful independent risk factors for cardiac death not only in patients with ischemic, but also in idiopathic dilated and hypertrophic cardiomyopathy.9-13 Life-threatening arrhythmias are an important and common cause of death in these classifications of cardiomyopathy, suggesting that quantitative assessment of MBF could be utilized not merely for risk stratification but specifically to identify patients prone for SCD.14 The link between the occurrence of VA and impaired myocardial perfusion, however, remains to be elucidated.

The aim of this pilot study was to assess whether hyperemic MBF and CFR as assessed with [15O]H2O PET are associated with VA inducibility during an electrophysiological study (EPS) in patients with ischemic cardiomyopathy. Furthermore, the role of alternative risk factors such as LVEF, scar size, transmurality and border zone as assessed with cardiovascular magnetic resonance (CMR) imaging was evaluated.

METHODSStudy population

Patients with ischemic cardiomyopathy and LVEF ≤35%, who were referred for ICD implantation for primary prevention of SCD based on current guidelines4 were prospec-tively included. Exclusion criteria were a cardiac rhythm other than sinus rhythm and a prior history of sustained ventricular arrhythmia. During the study period, 581 ICD devices were implanted of which 316 were combined with resynchronization therapy or implanted for secondary prevention indication. Of the remaining 265 patients, 109 had non-ischemic cardiomyopathy and another 19 patients did not have sinus rhythm. Of the remaining 137 eligible patients, 107 patients could not be included because of refusal of participation, logistical issues, or other contra-indications for PET, CMR or

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EPS. A total of 30 patients were included in the described study. None of the patients displayed residual ischemia at non-invasive testing amendable to revascularization prior to referral for ICD implantation. All patients underwent CMR, PET, and EPS for research purposes after inclusion in this study. The study was approved by the Ethics Committee of the VU University Medical Center, in accordance with the Declaration of Helsinki and written informed consent was obtained from all patients.

CMR image acquisition

CMR studies were performed on a clinical 1.5-T MRI scanner (Magnetom Avanto; Siemens, Erlangen, Germany), using a dedicated phased-array body coil. After survey scans, cine imaging was performed using a retrospectively ECG-gated, steady-state free precession sequence during breath holds of ~10 seconds in mild expiration (typical image parameters: slice thickness 5 mm, slice gap 5 mm, temporal resolution <50 ms, repetition time (TR) 3.2 ms, echo time (TE) 1.54 ms, flip angle 60°, typical image resolution 1.3×1.6× 5.0 mm). Standard four-, three- and two-chamber orientations were obtained and subsequently, a stack of 10 to 12 consecutive short-axis slices was acquired, fully covering the left ventricle from the mitral valve annulus to the apex. After administra-tion of 0.2 mmol.kg−1 gadolinium-DOTA (Dotarem, Guerbet, Villepinte, France) and a delay of 10 to 15 minutes, late gadolinium enhancement (LGE) images were acquired in similar orientations as the cine images, using a two-dimensional segmented inversion-recovery prepared gradient echo sequence (TE 4.4 ms, TR 9.8 ms, inversion time 250 to 300 ms, typical voxel size 1.3×1.6×5.0 mm). In case of difficulties with breath holding during LGE imaging, a single-shot sequence was used instead of a segmented sequence.

CMR image analysis

Images were analysed using the software package MASS (Mass v.5.1 2010-EXP beta, Medis, Leiden, the Netherlands). Endocardial and epicardial borders of short-axis cine images were outlined manually in both end-diastolic and endsystolic phases. Papillary muscles were included in the left ventricular cavity. Left ventricular volumes, LVEF, and mass were computed using these analyses. Subsequently, endocardial and epicardial contours of the short-axis LGE images were traced manually. Myocardial scar size was quantified using the full-width at half-maximum method, which defines scar as myocar-dial tissue with a signal intensity ≥50 % of the maximum signal intensity in hyperenhanced area.15 Scar border zone was defined as myocardium with signal intensity ≥35 % but <50 % of the maximum signal intensity, as previously described.16 Scar and scar border zone were automatically quantified, and manual adjustments of the automated tracings were made when necessary. Areas of enhancement were quantified by computer-assisted planimetry on each of the short-axis images based on the 17-segment American Heart Association (AHA) model17, excluding the apex. Total scar size and border zone were expressed as grams and percentages of the total left ventricular mass. Furthermore, the

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transmural extent of LGE was quantified by dividing scar transmurality into quartiles (1 to 25%, 25 to 50%, 50 to 75%, and 75% to 100%).18 Mean infarct transmural extent was calcu-lated as the mean transmurality of all myocardial scar segments. According to segmental enhancement results, scar segments and normal remote segments were selected.

PET image acquisition

All PET studies were performed using a PET/computed tomography (CT) device (Philips Gemini TF 64, Philips Healthcare, Best, The Netherlands). Patients were instructed to refrain from intake of products containing caffeine or xanthine 24 hours prior to the scan. The scanning protocol consisted of a 6-min emission scan which was started simul-taneously with the injection of a 5 mL (0.8 mL∙s-1) bolus of [15O]H2O (370 MBq) followed by a 35 mL saline flush (2 mL∙s-1). This dynamic scan sequence was followed immediately by a respiration-averaged low dose CT scan to correct for attenuation (55 mAs; rotation time, 1.5 s; pitch, 0.825; collimation, 64 x 0.625; acquiring 20 cm in 11 s) during normal breathing.19 After an interval of 10 min to allow for decay of radioactivity, an identical PET sequence was performed 2 minutes after the start of intravenous adenosine infusion (140 µg∙kg-1∙min-1). Adenosine infusion was terminated after the low dose CT. Dynamic images were reconstructed into 22 frames (1x10, 8x5, 4x10, 2x15, 3x20, 2x30, and 2x60 seconds) using the 3-dimensional row-action maximum likelihood algorithm and applying all appropriate corrections.

PET image analysis

PET image analysis was performed using in-house developed software.20 Image derived input functions of the ascending aorta and right ventricular cavity were obtained by manually defining volumes of interest (VOI) during the first pass phase of the injected tracer bolus. Both VOIs were then transferred to the full dynamic images to obtain arterial whole blood and right ventricular time-activity curves. Parametric images of MBF, perfusable tissue fraction (PTF), and arterial and venous blood volume fractions were generated as previously described.20 Parametric images of arterial and venous blood volume frac-tions were subtracted from normalized CT transmission images, resulting in parametric anatomic tissue fraction images (ATF).21 Finally, VOIs were defined manually on short axis parametric PTF images, according to the 17-segment AHA model17 after which this template was projected onto the parametric MBF images. MBF was expressed in millilitres per minute per gram of perfusable tissue. CFR was defined as the ratio of hyperemic and resting MBF. Based on scar and remote segments selected in the LGE-CMR image analysis, mean resting MBF, hyperemic MBF, and CFR in scar and remote areas were assessed. Heterogeneity of MBF and CFR was expressed as the coefficient of variation (COV), which was calculated as the ratio of the standard deviation (SD) and the mean of myocardial segments (excluding the apex) multiplied by 100 for global myocardium, scar, and remote areas.

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Electrophysiological study

After ICD implantation, an EPS was performed non-invasively using the ICD and its programmer. The EPS protocol consisted of programmed stimulation delivered from the right ventricular lead with 8-beat drive trains (two cycle lengths of 600 ms and 400 ms) immediately followed by up to 3 extrastimuli. Extrastimuli were delivered at progres-sively shorter coupling intervals in 20-ms steps until the effective refractory period was reached. No coupling interval shorter than 180 ms was used. If no sustained VA was induced, the protocol was repeated with a basic cycle length of 400 ms under infusion of isoproterenol (aimed baseline heart rate 100-110/min).22, 23 The EPS was performed and interpreted by an experienced electrophysiologist, blinded to the imaging data. In case a monomorphic VA was induced, the origin was determined based on the 12-lead electrocardiogram by two independent electrophysiologists, blinded to the imaging data. A positive EPS was defined as ventricular fibrillation or ventricular tachycardia lasting >30 seconds or requiring termination in case of hemodynamic instability.

Statistical analysis

Continuous variables are presented as mean ± SD and categorical data are summarized as frequencies and percentages. The Shapiro-Wilk test and histograms were used to evaluate if continuous data were normally distributed. Groups with positive and negative EPS were compared using the unpaired two-sided Student’s t test for continuous variables and two-tailed Fisher’s exact test for categorical data. Levene’s test for equality of variances was used to verify if the equal variances assumption of the unpaired Student’s t test was appropriate. Comparison of normally distributed paired data was performed with the paired t test. If not stated otherwise, nonparametric tests confirmed the parametric test results and were not needed. Receiver-operating characteristic (ROC) curve analysis was used for all imaging variables and areas under the curve (AUCs) were calculated. Patients with missing values for resting MBF (n=1) and scar size (n=1) were excluded from the ROC curve analysis to allow adequate comparisons of AUCs. Furthermore, univariate logistic regression analysis was performed for clinical and global imaging variables. Odds ratios (OR) were determined per 0.1 U decrease for MBF and CFR, and per 1 U increase for COV. A P-value below 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software package (SPSS 20.0, IBM Corporation, Armonk, NY, USA) and MedCalc (MedCalc 12.7.0, MedCalc Software, Oostende, Belgium).

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A B CRe

stingMBF(m

L·min

-1·g-1)

Hyperem

icM

BF(m

L·min

-1·g-1)

E F G

RestingMBF(m

L·min

-1·g-1)

Hyperem

icM

BF(m

L·min

-1·g-1)

H

D

Figure 1. PET and CMR scan in two patients with a history of an anterior wall myocardial infarction. In patient 1 (A-D), EPS was negative whereas in patient 2 (E-H) a monomorphic ventricular tachycardia was induced. Both patients displayed significant scar size (C, D, G and H) and perfusion defect (A, B, E and F) in the anteroseptal wall. Quantitative MBF results showed significantly impaired hyperemic MBF in patient 2 as compared with patient 1. Note the difference in scaling on the PET images (A, B, E, and F) between both patients. CMR, cardiovascular magnetic resonance; EPS, electrophysiological study; MBF, myocardial blood flow; PET, positron emission tomography.

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RESULTSStudy population

Thirty patients were included in this study. Clinical baseline characteristics are presented in table 1. None of the patients were considered candidates for revascularization prior to inclusion in this study. In 27 of 30 patients, coronary angiography was performed within a median time of 8 (IQR 2-21) months. In 21 patients, complete revasculariza-tion was achieved whereas in 6 patients residual lesions persisted, not amendable to revascularization as non-invasive conventional perfusion imaging revealed no signs of significant ischemia. In all three patients without coronary angiography, no ischemia was detected at non-invasive testing. All patients underwent CMR and [15O]H2O PET after inclusion in this study. In one patient LGE was not assessed due to severe motion artefacts, whereas in another individual resting MBF could not be obtained due to tech-nical problems. Twelve patients (40%) displayed VA inducibility at EPS (monomorphic VA n=11, polymorphic VA n=1). The origin of the induced monomorphic VAs was deter-mined in 10 patients (anteroseptal wall n=3, lateral wall n=3, inferior wall n=4) whereas in one patient, the origin was indeterminate due to poor quality of the electrocardio-gram. There were no significant differences in clinical baseline characteristics between patients with a positive and negative EPS. However, a trend was observed towards a higher prevalence of diabetes in the EPS positive group. In addition, patients with residual coronary lesions (but without clinical significant ischemia) seemed more likely to have a positive EPS (P=0.06). Figure 1 provides examples of PET and CMR images of patients with positive and negative EPS.

CMR

Results of CMR imaging are presented in table 2 and figure 2. Mean LVEF was 28.9 ± 6.1%. There were no significant differences in left ventricular volumes or LVEF between EPS positive and negative patients. Mean scar size was 15.8 ± 9.1% with a mean transmural extent of 34.6 ± 16.5 %. There were no differences observed in scar size and border zone between both patient groups. Patients with a positive EPS, however, displayed a higher number of segments with >75% scar transmurality, whereas the number of segments with 1-25% transmurality was higher in patients with negative EPS. In total, 8 of 10 induced monomorphic VAs originated from scar areas, which were transmurally infarcted in 4 patients.

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Table 1. Baseline patient characteristics

Characteristics (N (%), mean ± SD)

Total(n=30)

Positive EPS(n=12)

Negative EPS(n=18)

P – value (positive EPS vs.

negative EPS)

Age (years) 67.1 ± 9.0 64.7 ± 8.2 68.7 ± 9.4 0.24

Male gender 26 (87%) 10 (83%) 16 (89%) 1.00

Medication:

ACE/ARB 24 (80%) 8 (67%) 16 (89%) 0.18

Beta-blockers 29 (97%) 12 (100%) 17 (94%) 1.00

Ca channel blocker 3 (10%) 2 (17%) 1 (6%) 0.55

Diuretics 18 (40%) 9 (75%) 9 (50%) 0.26

Statin 27 (90%) 11 (92%) 16 (89%) 1.00

Amiodarone 1 (3%) 1 (8%) 0 0.40

Diabetes 7 (23%) 5 (42%) 2 (11%) 0.08

Hypertension 12 (40%) 4 (33%) 8 (44%) 0.71

Hyperlipidaemia 12 (40%) 6 (50%) 6 (33%) 0.46

Time since last MI (years) 6.1 ± 7.6 4.5 ± 5.0 7.2 ± 8.9 0.51

Coronary angiography 27 (90%) 12 (100%) 15 (83%) 0.26

Extent of native CAD:

1-vessel 12 (40%) 6 (50%) 6 (40%) 0.87

2-vessel 4 (13%) 2 (17%) 2 (13%) 0.87

3-vessel 11 (37%) 4 (33%) 7 (47%) 0.87

PCI/CABG 25 (83%) 10 (83%) 15 (83%) 1.00

Time since last PCI/CABG (median months (IQR))

18.0 (7.0-66.0) 17.5 (7.0-93.3) 18 (7.0-65.0) 0.68

Complete revascularization

21 (78%) 7 (58%) 14 (93%) 0.06

Heart failure:

NYHA I 10 (33%) 3 (25%) 7 (39%) 0.42

NYHA I I 19 (63%) 8 (67%) 11 (61%) 0.42

NYHA I I I 1 (3%) 1 (8%) 0 0.42

NT-proBNP (ng/L) 1306.7 ± 1979.4 996.8 ± 836.7 1513.2 ± 2473.4 0.49

Creatinine (umol/L) 95.6 ± 42.8 102.0 ± 56.3 91.3 ± 32.1 0.51

ACE, angiotensin converting enzyme; ARB, angiotensin-II-receptor blockers; Ca, calcium; CAD, coronary artery disease; CABG, coronary arterial bypass graft surgery; EPS, electrophysiological study; NT-proBNP, N-terminal prohormone of brain natriuretic peptide; NYHA, New York Heart Association; PCI, percutaneous coronary intervention.

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Table 2. CMR imaging results

Characteristics(mean ± SD)

Total(n=30)

Positive EPS(n=12)

Negative EPS(n=18)

P – value (positive EPS vs.

negative EPS)

LVEDV (mL) 273.2 ± 69.2 266.1 ± 81.0 278.0 ± 62.2 0.65

LVESV (mL) 197.6 ± 60.9 190.0 ± 70.4 202.7 ± 55.2 0.59

LVEF (%) 28.9 ± 6.1 30.5 ± 6.1 27.8 ± 6.1 0.24

LV mass (g) 136.6 ± 42.4 127.6 ± 34.3 142.7 ± 46.9 0.35

Scar size (g) 20.2 ± 13.0 25.2 ± 17.4 17.2 ± 8.6 0.11

Scar size (%) 15.8 ± 9.1 19.1 ± 10.3 13.8 ± 7.9 0.13

Mean infarct transmurality (%)

34.6 ± 16.5 39.1 ± 13.6 31.8 ± 17.8 0.25

Scar segments by transmurality (N):

1-25% 5.1 ± 2.2 4.0 ± 1.7 5.8 ± 2.2 0.03

25-50% 2.9 ± 1.1 3.3 ± 0.8 2.7 ± 1.3 0.17

50-75% 1.9 ± 1.6 1.9 ± 1.3 1.8 ± 1.7 0.90

75-100% 1.4 ± 1.9 2.0 ± 1.8 1.1 ± 2.0 <0.05*

Border zone (g) 9.9 ± 4.6 11.1 ± 5.8 9.1 ± 3.6 0.27

Border zone (%) 7.6 ± 3.4 8.4 ± 3.6 7.1 ± 3.3 0.33

EPS, electrophysiological study; LV, left ventricle; LVEDV, left ventricular end diastolic volume; LVEF, left ventricular ejection fraction; LVESV, left ventricular end systolic volume. Mean scar size based on total n=29 with positive EPS n=11. * Tested with Mann Whitney U-test.

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Figure 2. Scatter plot comparison of LVEF (A) and scar size (B) between patients with positive and negative EPS results. EPS, electrophysiological study; LVEF, left ventricular ejection fraction.

PET

Table 3 summarizes MBF results. Global resting MBF was 0.80 ± 0.19 and increased to 1.50 ± 0.40 mL∙min−1∙g−1 (P<0.001) during hyperemic conditions resulting in a global CFR of 1.92 ± 0.55. Resting MBF, hyperemic MBF, and CFR were significantly lower in scar areas compared with remote areas. Patients without complete revascularization displayed a significantly impaired CFR compared with complete reperfused patients (1.32 ± 0.29 vs. 2.10 ± 0.50, P=0.001, respectively), but this difference was not observed for hyperemic MBF (1.25 ± 0.20 vs. 1.55 ± 0.42 mL∙min−1∙g−1, P=0.11, respectively). These results for hyperemic MBF and CFR were comparable within scar and remote areas. As demonstrated in figure 3, there was no significant difference in global resting MBF between patients with positive and negative EPS (P=0.85). However, patients with posi-tive EPS were characterized by a significantly impaired global hyperemic MBF (P=0.004) resulting in a significant reduced global CFR compared with EPS negative patients (P=0.009). Comparable results were obtained for both hyperemic MBF and CFR in scar and remote areas compared between both groups (table 3). When excluding patients with diabetes, a similar difference in global hyperemic MBF and CFR was observed (1.11 ± 0.20 vs. 1.68 ± 0.36 mL∙min−1∙g−1, P=0.001 and 1.66 ± 0.29 vs. 2.16 ± 0.45, P=0.02, respectively). Again, these results were comparable for scar areas as well as remote areas. Results for the COV of MBF data are presented in table 4 and figure 4. Scar areas were characterized by a higher COV of resting and hyperemic MBF in comparison with

EPS negativ

e (n=18)

EPS positive (n=12)

0

10

20

30

40

50 p = 0.24A

LVEF

(%)

EPS negativ

e (n=18)

EPS positive (n=11)

0

10

20

30

40

50 p = 0.13B

Scar

size

(%)

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remote areas. Patients with VA inducibility during EPS displayed significantly higher COVs of resting MBF, hyperemic MBF, and CFR in global myocardium. These differences were predominantly driven by higher COVs in the infarct area. The origin of induced monomorphic VAs displayed a mean hyperemic MBF and CFR of 0.94 ± 0.61 mL∙min−1∙g−1 and 1.35 ± 0.73, respectively. In 6 patients with a positive EPS, the origin of VA displayed hyperemic MBF below 1.00 mL∙min−1∙g−1, whereas the CFR was below 2.00 in 8 patients.

Table 3. [15O]H2O PET MBF results


Total(n=30)

Positive EPS(n=12)

Negative EPS(n=18)

P – value (positive EPS vs. negative

EPS)

Resting MBF:

Global 0.80 ± 0.19 0.81 ± 0.27 0.80 ± 0.12 0.85

Remote area 0.90 ± 0.23 0.94 ± 0.28 0.88 ± 0.19 0.52

Scar area 0.72 ± 0.19 0.70 ± 0.27 0.73 ± 0.12 0.72

P-value (remote vs. scar)

<0.001 0.001 0.003

Hyperemic MBF:

Global 1.50 ± 0.40 1.25 ± 0.30 1.66 ± 0.38 0.004

Remote area 1.76 ± 0.50 1.51 ± 0.32 1.92 ± 0.54 0.02

Scar area 1.23 ± 0.38 1.00 ± 0.34 1.39 ± 0.33 0.005


<0.001 <0.001 <0.001

CFR:

Global 1.92 ± 0.55 1.59 ± 0.49 2.12 ± 0.48 0.009

Remote area 2.02 ± 0.53 1.70 ± 0.56 2.21 ± 0.41 0.008

Scar area 1.81 ± 0.64 1.51 ± 0.63 1.99 ± 0.59 <0.05


0.02 0.21 0.04

CFR, coronary flow reserve; EPS, electrophysiological study; MBF, myocardial blood flow (mL∙min−1∙g−1). Mean resting MBF and CFR based on total n=29 with positive EPS n=11.

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Figure 3. Scatter plot comparison of global resting MBF, hyperemic MBF, and CFR between patients with positive and negative EPS results (Mean resting MBF and CFR based on total n=29 with EPS positive n=11). CFR, coronary flow reserve; EPS, electrophysiological study; MBF, myocardial blood flow.

Figure 4. Scatter plot comparison of the COV for global resting MBF, hyperemic MBF and CFR between patients with positive and negative EPS results (mean COV of resting MBF and CFR based on total n=29 with EPS positive n=11). CFR, coronary flow reserve; COV, coefficient of variation; EPS, electrophysiological study; MBF, myocardial blood flow.

0.0

1.0

2.0

3.0

4.0

0.0

1.0

2.0

3.0

4.0

p = 0.004EPS negative (n=18)EPS positive (n=12)

p = 0.85

Resting MBF Hyperemic MBF CFR

p = 0.009M

yoca

rdia

lBlo

odFl

ow(m

Lm

in-1 g

-1)

CoronaryFlow

Reserve

0

20

40

60

80

100p = 0.04p = 0.02

Resting MBF Hyperemic MBF CFR

p = 0.008EPS negative (n=18)EPS positive (n=12)

Coef

ficie

ntof

varia

tion

(%)

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Table 4. Coefficient of variation of resting MBF, hyperemic MBF, and CFR


Total(n=30)

Positive EPS(n=12)

Negative EPS

(n=18)

P – value (positive EPS vs. negative

EPS)

COV resting MBF:

Global 22.5 ± 12.2 29.1 ± 15.3 18.5 ± 7.8 0.02

Remote area 12.2 ± 6.1 14.1 ± 8.1 11.0 ± 4.3 0.18

Scar area 19.7 ± 13.0 25.9 ± 14.2 16.0 ± 11.0 <0.05

P-value (remote vs. Scar)

0.005 0.03 0.08

COV hyperemic MBF:

Global 27.9 ± 14.9 34.5 ± 18.9 23.4 ± 9.7 0.04

Remote area 15.1 ± 10.0 19.2 ± 12.9 12.4 ± 6.8 0.07

Scar area 23.9 ± 14.9 32.0 ± 17.2 18.5 ± 10.4 0.01


0.003 0.04 0.03

COV CFR:

Global 25.2 ± 9.5 30.6 ± 9.0 21.8 ± 8.3 0.01

Remote area 17.9 ± 9.2 22.7 ± 10.7 14.9 ± 6.9 0.02

Scar area 22.8 ± 11.9 31.2 ± 12.7 17.7 ± 8.1 0.002


0.05 0.11 0.30

CFR, coronary flow reserve; COV, coefficient of variation; EPS, electrophysiological study; MBF, myocardial blood flow (mL∙min−1∙g−1). COV of resting MBF and CFR based on total n=29 with positive EPS n=11.

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Risk factors for VA inducibility

Table 5 summarizes ROC AUC statistical analysis for risk assessment of VA inducibility. Both impaired global hyperemic MBF and CFR were significant risk factors for a posi-tive EPS (AUC 0.84, P<0.01 and 0.77, P=0.02, respectively) (figure 5), whereas scar size, infarct transmurality and border zone were not. For global hyperemic MBF, an optimal derived cut-off value of 1.66 mL∙min−1∙g−1 yielded a sensitivity of 100 and specificity of 56%. Optimal cut-off value for global CFR was 1.80, with a sensitivity of 80 and specific-ity of 72%. Comparable results were observed for the AUCs of hyperemic MBF and CFR in scar and remote areas (table 5). AUCs for COV of MBF and CFR in different areas ranged from 0.58 for resting MBF in remote area to 0.83 for CFR in scar area.

Univariate analysis revealed that global hyperemic MBF was associated with a positive EPS (OR 1.44, 95% CI 1.08-1.92, P=0.01). Other significant risk factors were CFR (OR 1.24, 95% CI 1.04-1.49, P=0.02) and COV of CFR (OR 1.12, 95% CI 1.01-1.25, P=0.03).

Figure 5. Receiving operator curve analysis with corresponding AUCs displaying the association with VA inducibility of hyperemic MBF, CFR, and scar size. AUC, area under the curve; CFR, coronary flow reserve; MBF, myocardial blood flow.

0.0 0.2 0.4 0.6 0.8 1.00.0

0.2

0.4

0.6

0.8

1.0

CFR (AUC: 0.77)Hyperemic MBF (AUC: 0.84)Scar size (AUC 0.61)

1 - Specificity

Sens

itivi

ty

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Table 5. ROC curve analysis for VA inducibility risk of CMR and PET variables

VariableROC-curve analysis

AUC (95% CI) P-value

Scar:Scar size (%) 0.61 (0.39-0.84) 0.34

Scar size (g) 0.61 (0.39-0.83) 0.34

Mean infarct transmurality (%) 0.62 (0.41-0.83) 0.31

Transmural scar segments (>75%) 0.70 (0.50-0.91) 0.08

Border zone (g) 0.60 (0.37-0.83) 0.39

Border zone (%) 0.59 (0.37-0.81) 0.44

MBF Global:Resting MBF 0.54 (0.27-0.81) 0.74

Hyperemic MBF 0.84 (0.69-0.99) 0.003

CFR 0.77 (0.57-0.96) 0.02

COV resting MBF 0.76 (0.57-0.94) 0.03

COV hyperemic MBF 0.72 (0.52-0.92) 0.06

COV CFR 0.79 (0.63-0.96) 0.01

MBF Scar area:Resting MBF 0.57 (0.29-0.84) 0.57

Hyperemic MBF 0.83 (0.67-0.98) 0.005

CFR 0.73 (0.52-0.94) <0.05

COV resting MBF 0.73 (0.54-0.92) <0.05


COV CFR 0.83 (0.67-1.00) 0.004

MBF Remote area:Resting MBF 0.51 (0.27-0.76) 0.92

Hyperemic MBF 0.79 (0.61-0.98) 0.01

CFR 0.76 (0.54-0.97) 0.03

COV resting MBF 0.58 (0.36-0.80) 0.47


COV CFR 0.76 (0.57-0.96) 0.02

AUC, area under the curve; CFR, coronary flow reserve; COV, coefficient of variation; EPS, electrophysiological study; MBF, myocardial blood flow (mL∙min−1∙g−1); PET, positron emission tomography.

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DISCUSSIONThe aim of current pilot study was to evaluate whether impaired hyperemic MBF and CFR were associated with VA inducibility in patients with ischemic cardiomyopathy and to compare the role of alternative imaging variables such as scar size, transmurality and border zone. Global hyperemic MBF and CFR were found to be significantly lower in patients with a positive EPS. Of interest, this pattern was observed both in the scar area and the remote myocardium. In contrast to quantitative myocardial perfusion imaging, scar size, border zone, and LVEF did not differ between patients with positive and negative EPS. However, patients with positive EPS displayed a higher number of scar segments with >75% transmurality.

Refinement of eligibility criteria for primary prevention ICD therapy is needed as LVEF has limited sensitivity for predicting the risk of SCD.24 Currently, much effort is put into the search for enhanced risk stratification markers which assess the pathophysiologi-cal substrate related to electrical instability.25 Among parameters such as scar burden16,

26-32 and sympathetic innervation defects33, ischemia might play an important role. Myocardial perfusion abnormalities resulting in ischemia are well known to modulate automaticity, excitability, and refractoriness. These changes lead to heterogeneity of repolarization, thus creating a substrate for VA.34, 35. Paganelli et al.36 demonstrated that residual ischemia in patients following a myocardial infarction, assessed with single photon emission computed tomography (SPECT), was associated with inducibility of VA, whereas LVEF and fixed perfusion defects were not. In particular, the extent of residual ischemia in the peri-infarction area was found to be of importance. To deter-mine whether SPECT imaging might improve risk stratification for SCD, Piccini et al.37 conducted a study in >6000 patients with coronary artery disease with a median follow up time of 2.7 years. The summed stress perfusion defect score was an independent risk factor for SCD and provided incremental prognostic value over LVEF. Interestingly, both summed difference perfusion defect score (reflecting ischemia) and resting perfusion defect score (reflecting scar) were not associated with SCD in a multivariate analysis, suggesting that the combination of scar and ischemia has incremental prognostic value compared with both single imaging variables. Furthermore, in patients with coronary artery disease and LVEF>35%, similar results were obtained.38 Hyperemic perfusion abnormalities, reflecting scarred, hibernating and ischemic myocardium might there-fore assist in risk stratification for VA in patients with impaired and preserved LVEF.

As ischemia serves as an important trigger or substrate for VA, current guidelines recom-mend optimal revascularization in case of residual ischemia prior to ICD therapy.4 Yet, in a not inconsequential number of patients, (subtle) perfusion abnormalities persist which are not amendable to revascularization, as demonstrated in the current study. In comparison with SPECT imaging, PET myocardial perfusion imaging has a higher resolu-tion and allows for absolute quantification of perfusion.39, 40 Consequently, more subtle

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perfusion abnormalities can be detected in patients with ischemic and non-ischemic cardiomyopathy. Assessment of quantitative hyperemic MBF and CFR might, therefore, enhance risk stratification. Indeed, several large studies have demonstrated the incre-mental prognostic value of hyperemic MBF and CFR as assessed with PET in patients with ischemic cardiomyopathy, idiopathic dilated cardiomyopathy, and hypertrophic cardiomyopathy.9-13 Tio et al.9 investigated the prognostic value of CFR assessed with PET in comparison with LVEF In patients with ischemic cardiomyopathy. Global CFR was an independent strong risk factor for total cardiac mortality. Consistent with the results of Piccini et al.37, these findings were superior to the prognostic value of LVEF and independent of the extent of qualitatively assessed ischemia or scar size. The question arises, however, whether the value of impaired quantitatively assessed hyperemic MBF and CFR in risk stratification holds true for ventricular arrhythmias specifically. In the current study, VA inducibility was related with impaired hyperemic MBF and CFR which is hypothesis generating that presence of residual perfusion abnormalities assessed with quantitative PET might be associated with VA events.

It is demonstrated that assessment of MBF with [15O]H2O PET includes flow in viable myocardium that is capable of exchanging water rapidly only. Consequently, the contri-bution of MBF within scar tissue is assumed to be negligible in the MBF calculation.41,

42 Scar size therefore does not influence global MBF values from a theoretical point of view. In patients with positive EPS results, a significantly impaired hyperemic MBF was observed, not only in the global myocardium but also in remote areas without scar. Since hyperemic MBF mainly depends on the vasodilatory function in the microvascula-ture, these results could indicate that vascular dysfunction extending beyond epicardial stenotic coronary arteries is related with VA inducibility. Second, more severe diffuse residual epicardial coronary artery disease may account for the observed difference. In the infarcted area [15O]H2O PET assessed MBF reflects flow per millilitre of perfusable tissue (viable myocardium in regions surrounding scar). As monomorphic VAs typically arise in regions with a mixture of viable and non-viable tissue in areas adjacent to scar43,

44, MBF specifically within the infarcted area was assessed. We found that both resting and hyperemic MBF were significantly lower in scar segments compared with remote areas reflecting lower metabolic demand and coronary microvascular dysfunction in the peri-infarct myocardium.

Global MBF in patients with ischemic cardiomyopathy consists of a combination of perfusion in scar areas, residual ischemic areas, and remote areas. Consequently, heterogeneity of perfusion and CFR is present which may contribute to the heteroge-neity of repolarization and enhanced vulnerability for VA. Therefore, the COV of MBF and CFR in global myocardium, scar areas, and remote areas were calculated. Indeed, the COV of resting MBF, hyperemic MBF, and CFR were related with VA inducibility, in particular within scar regions. An alternative explanation might be from a methodologi-cal point of view. Dense scar segments often suffer from poor count statistics and spill

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over artefacts, resulting in non-physiological MBF readings. These technical factors may also explain the COV findings. However, patients with a positive EPS displayed a higher COV in remote areas as well, in particular for CFR. Nonetheless, the specific assess-ment of MBF impairment and heterogeneity in scar areas yielded no incremental value compared with global hyperemic MBF.

Interestingly, total scar size and infarct border zone did not identify patients prone to VA inducibility in the current study. These results are inconsistent with previously published larger studies demonstrating the value of CMR assessed infarct size45 and border zone46 in predicting the risk of VA inducibility. More importantly, the role of scar burden in risk stratification for spontaneous VA events was demonstrated in ischemic and non-ischemic cardiomyopathy.26-30, 32 In the present study the difference in scar size between EPS negative and positive patients did not reach statistical significance. This might be due to the smaller sample size included in this study as compared with the abovementioned larger studies. Patients with a positive EPS, however, displayed a higher number of transmural scar segments with >75% transmurality. This resembles the findings in the study performed by Scott et al.32, which demonstrated that the number of myocardial segments with full-thickness (>75% transmurality) scar was the most important risk factor for appropriate ICD therapy. However, in the current study, scar transmurality was not associated with VA inducibility in univariate analysis. Next to scar burden, CMR assessment of infarct border zone specifically may improve risk stratification for VA events.16, 31, 46 The value of infarct border zone, however, is less well established as several studies use different definitions for the quantification of infarct tissue heterogeneity which are not interchangeable.27 In the current study, the defini-tion described by Roes et al.16 was used for border zone quantification as this method minimizes the susceptibility to border zone overestimation and excludes the potential influence of suboptimal T1 nulling and artefacts in the remote myocardium affecting the signal intensity. Patients with a positive EPS did not display a larger infarct border zone. This may be due to the fact that we assessed the relation with VA inducibility whereas Roes et al.16 demonstrated the value in risk stratification for spontaneous VA events.

Limitations

There are several limitations in this study. First, we evaluated the myocardial electrical instability with EPS. Although previous studies have demonstrated that a positive EPS in the chronic phase after myocardial infarction is the single best risk factor for the occurrence of spontaneous VA47, 48, the diagnostic accuracy for clinical events (of SCD) remains only moderate. It is important to note that the value of VA inducibility in risk stratification for SCD depends on the type of stimulation protocol used and the type of initiated VA during EPS.49 Induction of a monomorphic VA has been demonstrated to be more relevant than polymorphic VA or ventricular fibrillation.50, 51 In 11 of 12 patients with a positive EPS result in our study, a monomorphic VA was induced. Second, this

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study population comprised a small number of patients, therefore potentially obscuring some associated imaging variables which did not reach statistical significance. Further-more, multivariable analysis including potential confounders such as the somewhat skewed distribution of patients with DM between the EPS positive and negative groups in the current study could not be performed due to the small sample size. Indeed, it has been demonstrated that patients with coronary risk factors such as diabetes are more likely to have impaired hyperemic MBF.52 However, no differences were observed in hyperemic MBF between diabetic and non-diabetic patients in the current study. Moreover, comparable differences in hyperemic MBF and CFR between EPS positive and negative patients were obtained when excluding patients with diabetes. Finally, the ROC analysis used in this study is not a very robust method for the evaluation of risk predictors for VA inducibility.53 Therefore, the results of this study should be interpreted in a descriptive manner. Studies in larger number of patients with appropriately timed follow-up collecting more relevant clinical endpoints are warranted.

Conclusion

In this pilot study, impaired global hyperemic MBF and CFR were associated with inducibil-ity of VA in patients with ischemic cardiomyopathy, whereas the relation with CMR assessed LVEF, scar burden and border zone seemed of less significance. These results suggest that impaired hyperemic MBF might contribute to electrical instability and is hypothesis generating for the potential role of quantitative PET myocardial perfusion imaging in risk stratification for ventricular arrhythmias in ischemic cardiomyopathy patients.

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