Impact of Plaque Composition on Long-Term Clinical Outcomes in Patients with Coronary Artery Occlusive Disease

Original Article http://dx.doi.org/10.4070/kcj.2013.43.6.377 Print ISSN 1738-5520 • On-line ISSN 1738-5555 Korean Circulation Journal Impact of Plaq...

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Original Article http://dx.doi.org/10.4070/kcj.2013.43.6.377 Print ISSN 1738-5520 • On-line ISSN 1738-5555

Korean Circulation Journal

Impact of Plaque Composition on Long-Term Clinical Outcomes in Patients with Coronary Artery Occlusive Disease Ki Hong Kim, MD1, Wan Ho Kim, MD2, Hyun Woong Park, MD1, In Girl Song, MD1, Dong Ju Yang, MD1, Young Hoon Seo, MD1, Hyung Bin Yuk, MD1, Yo Han Park, MD1, Taek Geun Kwon, MD1, Charanjit S Rihal, MD3, Amir Lerman, MD3, Moo-Sik Lee, MD4, and Jang-Ho Bae, MD1 1

Division of Cardiology, Konyang University Hospital, Daejeon, Cardiology, Andong Sungso Hospital, Andong, Korea Cardiology, Mayo Clinic, Rochester, MN, USA 4 Division of Epidemiology, Konyang University, Daejeon, Korea 2 3

Background and Objectives: It is unclear which plaque component is related with long-term clinical outcomes in patients with coronary artery occlusive disease (CAOD). We assessed the relationship between plaque compositions and long-term clinical outcomes in those patients. Subjects and Methods: The study subjects consisted of 339 consecutive patients (mean 61.7±12.2 years old, 239 males) who underwent coronary angiogram and a virtual histology-intravascular ultrasound examination. Major adverse cardiac and cerebrovascular events (MACCE), including all-cause death, non-fatal myocardial infarction, cerebrovascular events, and target vessel revascularization were evaluated during a mean 28-month follow-up period. Results: Patients with high fibrofatty volume (FFV, >8.90 mm3, n=169) had a higher incidence of MACCE (25.4% vs. 14.7%, p=0.015), male sex (75.7% vs. 65.3%, p=0.043), acute coronary syndrome (53.3% vs. 35.9%, p=0.002), multivessel disease (62.7% vs. 41.8%, p<0.001) and post-stent slow flow (10.7% vs. 2.4%, p=0.002) than those with low FFV (FFV≤8.90 mm3, n=170). Other plaque composition factors such as fibrous area/volume, dense calcified area/volume, and necrotic core area/volume did not show any impact on MACCE. Cardiogenic shock {hazard ratio (HR)=8.44; 95% confidence interval (CI)=3.00-23.79; p<0.001} and FFV (HR=1.85; 95% CI=1.12-3.07; p=0.016) were the independent predictors of MACCE by Cox regression analysis. Thin-cap fibroatheroma, necrotic core area, and necrotic core volume were not associated with MACCE. Conclusion: FFV of a culprit lesion was associated with unfavorable long-term clinical outcomes in patients with CAOD. (Korean Circ J 2013;43:377-383) KEY WORDS: Intravascular ultrasonography; Plaque, atherosclerotic; Coronary artery disease.

Introduction Atherosclerotic plaque responsible for coronary heart disease is Received: April 15, 2013 Revision Received: May 20, 2013 Accepted: May 31, 2013 Correspondence: Jang-Ho Bae, MD, Division of Cardiology, Konyang University Hospital, 158 Gwanjeodong-ro, Seo-gu, Daejeon 302-718, Korea Tel: 82-42-600-9400, Fax: 82-42-600-9420 E-mail: [email protected] • The authors have no financial conflicts of interest. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2013 The Korean Society of Cardiology

heterogeneous in their composition, containing a variable amount of lipid, scar tissue, calcium, neovessels, inflammatory cells, and thrombotic material.1) Various imaging techniques are currently under investigation for the tissue characterization of coronary plaque. Virtual histology-intravascular ultrasound (VH-IVUS) is one of the most promising techniques available. Indeed, VH-IVUS has been shown to have a 93-97% ex vivo and 87-92% in vivo accuracy rate for the characterization of four different types of atherosclerotic plaque (fibrous, fibrofatty, dense calcium, and necrotic core).2-8) Until now, there have been few reports demonstrating an association between plaque composition and long-term clinical outcomes. Recently, there was a report that thin-cap fibroatheroma (TCFA), high plaque burden (PB), and small luminal area were responsible for longterm major adverse cardiac events (MACE) in non-culprit lesions 377

378 Coronary Plaque Composition and Long-Term Clinical Outcomes

with acute coronary syndrome (ACS).9) Other reports have shown only necrotic core and calcium were significantly greater in the nonculprit lesions of patients with a future MACE.10) However, these reports evaluated the plaque composition of nonculprit lesions in ACS. Therefore, we sought to evaluate the impact of in vivo plaque composition of culprit lesions using VH-IVUS on long-term clinical outcomes in patients who had coronary artery occlusive disease (CAOD) and had undergone percutaneous coronary intervention (PCI).

Subjects and Methods Study population All consecutive patients were prospectively enrolled in the Konyang University Hospital VH-IVUS registry (n=339). They had undergone successful PCI and VH-IVUS study between July 2006 and July 2008. They were followed up for a mean 28 months for major adverse cardiac and cerebrovascular events (MACCE). Exclusion criteria for VH-IVUS were severe vessel tortuousness or severe luminal narrowing with calcification precluding the insertion of an IVUS catheter. Stent selection and the use of a glycoprotein IIb/IIIa inhibitor were all up to the operator and physicians’ discretion. Patient demographics and laboratory data were obtained before the PCI & VH-IVUS study. The study was approved by the hospital ethics committee of the Konyang University Hospital. Intravascular ultrasound examination and analysis The VH-IVUS study, using a dedicated VH-IVUS console (Volcano Therapeutics, Rancho Cordova, CA, USA), was performed on native de novo target lesions (significance of the stenosis defined by an angiographic diameter stenosis >70%) in patients undergoing clinically indicated PCI after intracoronary administration of 100 to 200 µg nitroglycerin. A 20-MHz, 2.9 F monorail, electronic Eagle Eye Gold IVUS catheter (Volcano Therapeutics, Rancho Cordova, CA, USA) was advanced into the target lesion after wiring (n=242, 71.4%) or small sized (1.5 mm diameter) ballooning (n=97, 28.6%) in order to minimize the effect of ballooning on plaque morphology. Automatic pullback at 0.5 mm/s was conducted onto an aorto-ostial junction. The VH-IVUS image was recorded on a DVD-ROM for offline analysis at a later stage. The VH-IVUS uses spectral analysis of IVUS radiofrequency data to construct a tissue map. Qualitative and quantitative analyses of gray scale IVUS images were performed according to the criteria of the American College of Cardiology’s Clinical Expert Consensus Document on IVUS.3) Minimal luminal area (MLA) was identified as if there were several slices with equal lumen size, and that with the largest external elastic membrane and plaque cross sectional area was selected. A small MLA http://dx.doi.org/10.4070/kcj.2013.43.6.377

was defined as an area less than 4 mm2. Proximal and distal reference was defined by the site with the largest lumen proximal and distal to a stenosis, but usually within 10 mm of the stenosis with no major intervening branches, respectively. Spectral analysis of intravascular ultrasound radiofrequency data These analyses were done on the culprit lesion with customized software (IVUS Lab; Volcano Therapeutics, Rancho Cordova, CA, USA) by examiners (Bae JH and Kwon TG) who were unaware of the clinical characteristics of the patients. For both the lumen and the media-adventitia interface, automatic border detection was performed at the predefined lesion segment. Then, the border detection was manually corrected again in the lesion after automatic border detection. After confirming the border detection, the software automatically calculates and shows the results. For each frame, virtual histological findings were expressed in colors, as previously described (green for fibrous, green-yellow for fibrofatty, white for dense calcified, and red for necrotic core area). In addition, the area (mm2) and percentage of each tissue component of plaque was expressed as well as conducting a volumetric analysis (mm3). The predictive accuracy of this method with tissue mapping has been validated.4) Outcomes Clinical follow-up data were obtained from outpatient records or telephone interviews. A MACCE was defined as the composite of all-cause death, nonfatal myocardial infarction (MI), cerebrovascular events, or target vessel revascularization. Non-fatal MI was defined as the presence of clinical symptoms, electrocardiographic change, or abnormal imaging findings of MI, combined with an increase in creatine kinaseMB fraction or troponin T/I >99th percentile of the upper normal limit that was not related to an interventional procedure.11) Cerebrovascular events were defined as a stroke, transient ischemic attack, and reversible ischemic neurological deficit, as determined by a neurologist and confirmed on imaging. Target vessel revascularization was defined as the PCI of any segment of the epicardial coronary artery, including the target lesion. Low left ventricular ejection fraction (LVEF) was defined as LVEF less than 40%. Statistical analysis All analyses were performed with Statistical Package for the Social Sciences (SPSS, version 18.0; SPSS Inc., Chicago, IL, USA). Values are expressed as mean±SD. We used the Student t-tests, chi-square test, and Fisher’s exact test to compare the mean or frequency of variables by the groups. Cumulative event rates were estimated using www.e-kcj.org

Ki Hong Kim, et al. 379

the Kaplan-Meier method. Relevant baseline clinical and VH-IVUS characteristics showing a significant difference (p<0.05) were included in the multivariate analysis. Multivariate logistic regression analysis was performed to assess the predictors of long-term MACCE. Cox regression analysis with a forward stepwise method was performed to evaluate the independent factors of long-term clinical outcomes in study patients.

Results Patient demographics and predictors for major adverse cardiac and cerebrovascular events Between July 2006 and July 2008, a total of 339 patients with CAOD were enrolled after they had undergone a successful PCI (Table 1). The median age was 63.0 years, 70.5% were men, 29.2% had diabetes mellitus, 52.2% had hypertension, 34.8% had dyslipidemia, Table 1. Demographic, clinical, and procedural characteristics of the patients

and 36.9% were smokers. The mean follow-up was 28 months. Each of the plaque compositions in MLA {fibrous area (FA), fibrofatty area (FFA), dense calcium area (DCA) and necrotic core area (NCA)} and the entire culprit lesion {fibrous volume (FV), fibrofatty Table 2. Univariate analysis for MACCE Odds ratio

Variable

95% confidence interval

p

Age >65 years

1.80

1.05-3.08

0.041

Male sex

0.92

0.52-1.64

0.768

Diabetes mellitus

1.42

0.81-2.49

0.235

Hypertension

0.71

0.42-1.21

0.224

Hyperlipidemia

0.99

0.56-1.74

1.000

Smoking

0.60

0.34-1.08

0.093

Previous MI

1.65

0.61-4.41

0.396

Clinical presentation-ACS

1.14

0.67-1.94

0.683

Multi-vessel disease

1.39

0.81-2.38

0.277

eGFR <30 mL/min

0.94

0.10-8.60

1.000

Variable

Value

LVEF <40%

0.78

0.45-1.34

0.407

Age-years

61.7±12.2

Post-stent slow flow

2.45

0.98-6.10

0.057

155 (45.7)

Cardiogenic shock

Male sex (%)

Age >65 years (%)

239 (70.5)

TCFA

Hypertension (%) Diabetes mellitus (%)

16.81

1.85-152.98

0.006

0.81

0.47-1.42

0.488

177 (52.2)

2

MLA <4.0 mm

1.35

0.76-2.39

0.326

99 (29.2)

Plaque rupture

0.95

0.34-2.60

1.000

Hyperlipidemia (%)

118 (36.9)

Plaque burden ≥70%

1.65

0.91-2.98

0.118

Smoking (%)

125 (36.9)

Plaque length (mm)

1.00

0.95-1.05

0.945

Total plaque area at MLA (mm2)

1.00

0.95-1.06

0.983

Previous MI (%)

21 (6.2)

Diagnosis (%)

3

Total plaque volume (mm )

1.00

0.99-1.00

0.465

188 (55.5)

Large FA

1.01

0.59-1.71

1.000

Unstable angina

33 (9.7)

Large FFA

1.72

0.99-2.98

0.058

NSTEMI

24 (7.1)

Large DCA

0.64

0.38-1.10

0.131

STEMI

94 (27.7)

Large NCA

0.88

0.52-1.50

0.685

High PV

1.24

0.73-2.11

0.498

0.68-1.96

0.684

Stable angina

Cholesterol (mg/dL) Total

188.3±52.5

High FV

1.15

LDL-C

122.0±32.7

High FFV

1.98

1.15-3.42

0.015

HDL-C

43.9±10.6

High DCV

1.02

0.60-1.74

1.000

165.3±128.2

Triglycerides (mg/dL)

High NCV

1.46

0.85-2.49

0.177

LVEF

62.7±10.9

High corrected PV

1.12

0.68-2.01

0.586

Estimated creatinine clearance (mL/min)

64.5±22.2

High corrected FV

1.19

0.69-2.04

0.584

5 (1.5)

High corrected FFV

1.74

1.01-3.02

0.055

Cardiogenic shock (%) Post-stent slow flow (%)

22 (6.5)

No. of diseased epicardial coronary arteries (%) One

161 (47.5)

Two

111 (32.7)

Three

67 (19.8)

MI: myocardial infarction, (N)STEMI: (Non) ST-segment elevation myocardial infarction, LDL-C: low density lipoprotein-cholesterol, HDL-C: high density lipoprotein-cholesterol, LVEF: left ventricle ejection fraction www.e-kcj.org

High corrected DCV

0.93

0.55-1.60

0.891

High corrected NCV

1.42

0.83-2.45

0.218

MACCE: major adverse cardiac and cerebrovascular events, MI: myocardial infarction, ACS: acute coronary syndrome, eGFR: estimated glomerular filtration rate, LVEF: left ventricle ejection fraction, TCFA: thin-cap fibroatheroma, MLA: minimal lumen area, FA: fibrous area, FFA: fibrofatty area, DCA: dense calcium area, NCA: necrotic core area, PV: plaque volume, FV: fibrous volume, FFV: fibrofatty volume, DCV: dense calcium volume, NCV: necrotic core volume http://dx.doi.org/10.4070/kcj.2013.43.6.377

380 Coronary Plaque Composition and Long-Term Clinical Outcomes

volume (FFV), dense calcium volume (DCV), and necrotic core volume (NCV)} were divided into two groups according to median value to evaluate the impact of each plaque composition on long-term clinical outcomes. We also evaluated MACCE according to the median value of corrected plaque compositions, adjusted with different lesion length (volume/lesion length). The univariate logistic analysis showed that age (≥65 year old), cardiogenic shock, and FFV were significant predictors for MACCE (Table 2). TCFA, high PB, and small MLA were not significant predictors for MACCE. Another plaque composition, such as FA, FV, FFA, DCA, DCV, NCA, NCV, and each corrected plaque composition did not have any impact on MACCE. Multivariate logistic analysis was evaluated with reliable variables including patient age, cardiogenic shock, high FFV, ACS, low LVEF, post-stent no reflow, and multivessel disease. The independent predictors of long-term MACCE were cardiogenic shock, FFV, and patient age (Table 3). A Cox regression analysis also showed that cardiogenic shock {hazard ratio (HR)=8.44; 95% confidence interval (CI) I=3.00-23.79; p<0.001) and high FFV (HR=1.85; 95% CI=1.12-3.07; p=0.016} were the only significant predictors for MACCE (Table 4). Therefore, the investigators divided the study subjects according to FFV to evaluate the impact of plaque compositions on longterm clinical outcomes.

because ACS is one of the main risk factors of MACCE. Univariate analysis showed that previous MI, high PB (≥70%), and high FFV were significant predictors of MACCE in patients with stable angina, however, patient age, male sex, diabetes, smoking, small MLA, and larger NCA were significant predictors in patients with ACS. Multivariate logistic analysis showed that small MLA and larger NCA were Table 5. Characteristics of patients by FFV Variable

Low FFV

High FFV

p

Age-years

61.0±11.9

62.4±12.5

0.270

Age >65 years (%)

71 (41.8)

84 (49.7)

0.157

111 (65.3)

128 (75.7)

0.043

Hypertension (%)

89 (52.7)

88 (52.1)

1.000

Diabetes mellitus (%)

47 (27.8)

52 (30.8)

0.633

Hyperlipidemia (%)

67 (40.9)

51 (30.5)

0.052

Smoking (%)

62 (36.7)

63 (37.3)

1.000

8 (4.7)

13 (7.7)

0.368

109 (64.1)

79 (46.7)

Male sex (%)

Previous MI (%) Diagnosis (%) Stable angina

0.006

Unstable angina

12 (7.1)

21 (12.4)

NSTEMI

13 (7.6)

11 (6.5)

STEMI

36 (21.2)

58 (34.3)

Cholesterol (mg/dL)

Subgroup analysis for stable angina and acute coronary syndrome Subgroup analysis was performed according to clinical diagnosis

Variable

Odds ratio

95% confidence interval

p

Cardiogenic shock

16.36

1.73-154.42

0.015

High FFV

1.91

1.04-3.52

0.038

Age >65 years

1.81

1.01-3.26

0.048

Acute coronary syndrome

0.73

0.39-1.34

0.301

Low LVEF

0.82

0.46-1.48

0.516

Post-stent no reflow

1.44

0.45-4.61

0.540

Multivessel disease

1.15

0.63-2.09

0.660

MACCE: major adverse cardiac and cerebrovascular events, FFV: fibrofatty volume, LVEF: left ventricular ejection fraction Table 4. Cox proportional hazards regression analysis of predictors of MACCE Hazard ratio

95% confidence interval

p

Cardiogenic shock

8.44

3.00-23.79

<0.001

High FFV

1.85

1.12-3.07

0.016

Age >65 years

1.61

0.98-2.65

0.062

MACCE: major adverse cardiac and cerebrovascular events, FFV: fibrofatty volume

http://dx.doi.org/10.4070/kcj.2013.43.6.377

193.2±60.50

183.6±42.9

0.097

LDL-C

123.5±33.3

120.6±32.3

0.423

HDL-C

44.5±10.9

43.2±10.2

0.267

Triglycerides (mg/dL)

Table 3. Multivariate analysis of predictors of MACCE

Variable

Total

171.9±149.4

158.9±103.6

0.362

LVEF (%)

64.7±10.2

60.8±11.3

0.001

Estimated creatinine clearance (mL/min)

64.2±23.8

64.8±20.5

0.810

Cardiogenic shock (%)

2 (1.2)

3 (1.8)

0.249

Post-stent slow flow (%)

4 (2.4)

18 (10.7)

0.002

No. of diseased epicardial coronary arteries (%)

<0.001

One

99 (58.2)

62 (36.7)

Two

43 (25.3)

68 (40.2)

Three

28 (16.5)

39 (23.1)

Stent type (%) Sirolimus-eluting stent

0.479 76

70

Paclitaxel-eluting stent

25

35

Zotarolimus-eluting stent

25

32

Others Stent diameter (mm) Stent length (mm)

30

29

3.4±3.0

3.3±0.4

0.808

24.4±5.5

<0.001

21.4±5

FFV: fibofatty volume, MI: myocardial infarction, (N)STEMI: (Non) ST-segment elevation myocardial infarction, LDL-C: low density lipoprotein-cholesterol, HDL-C: high density lipoprotein-cholesterol, LVEF: left ventricle ejection fraction

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Ki Hong Kim, et al.

significant predictors for MACCE only in patients with ACS. Patient characteristics by fibrofatty volume High FFV (FFV>8.90 mm3, n=169) and low FFV (FFV≤8.90 mm3, n= 170) were classified according to median value (8.90 mm3). The patients with high FFV had a higher incidence of male gender (75.7% vs. 65.3%, p=0.043), ACS (53.3% vs. 35.9%, p=0.002), and multivessel disease (62.7% vs. 41.8%, p<0.001) than patients with low Table 6. Gray-scale and virtual histology-intravascular ultrasound findings by FFV Variable

Low FFV

High FFV

p

12.2±3.9

18.0±5.4

<0.001

3.8±1.2

4.1±1.2

0.014

Site of MLA EEM CSA (mm2) Lumen CSA (mm2) 2

8.4±3.4

13.9±4.9

<0.001

Plaque burden (%)

Plaque and media CSA (mm )

67.5±9.7

76.0±6.7

<0.001

Remodeling index

0.97±0.19

0.99±0.18

0.189

47 (27.6)

57 (33.7)

0.341

120 (70.6)

96 (56.8)

0.009

Fibrous CSA (mm )

3.14±1.75

6.29±2.95

<0.001

Fibrofatty CSA (mm2)

0.43±0.51

2.05±1.71

<0.001

Positive remodeling (>1.05), n (%) MLA <4.0 mm2, n (%) 2

2

Dense calcium CSA (mm )

0.58±0.58

0.56±0.53

0.700

Necrotic core CSA (mm2)

1.41±1.14

1.54±1.19

0.333

Volumetric analysis EEM volume (mm3)

197.2±87.2 3

382.2±174.0 <0.001

Lumen volume (mm )

87.0±38.1

141.7±69.5

Plaque and media volume (mm3)

<0.001

110.9±54.0

240.5±113.6 <0.001

Lesion length (mm)

16.3±6.5

21.4±8.3

<0.001

Fibrous volume (mm3)

34.4±19.9

97.6±52.7

<0.001

4.1±2.4

28.2±20.6

<0.001

3

Fibrofatty volume (mm ) 3

Dense calcium volume (mm )

7.6±8.1

11.8±10.9

<0.001

Necrotic core volume (mm3)

15.5±13.7

26.3±21.3

<0.001

Corrected plaque and media volume (mm3/cm)

69.8±24.8

114.8±35.7

<0.001

Corrected fibrous volume (mm3/cm)

22.2±11.9

46.9±19.4

<0.001

Volumetric analysis adjusted by lesion length

Corrected fibrofatty volume (mm3/cm)

2.9±2.3

14.3±10.2

<0.001

Corrected dense calcium volume (mm3/cm)

4.5±4.2

5.4±4.4

0.088

Corrected necrotic core volume (mm3/cm)

9.5±7.5

12.2±8.7

0.002

EEM: external elastic membrane, CSA: cross-sectional area, MLA: minimal lumen area

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381

FFV. The high FFV group had a higher rate of post-stent slow flow (10.7% vs. 2.4%, p=0.002), lower LVEF (60.8±11.3% vs. 64.7±10.2%, p=0.001), and longer stent length (24.4±5.5 mm vs. 21.4±5.0 mm, p<0.001) than the low FFV group. There were no significant differences in age, risk factors for coronary artery disease, lipid profile, stent type, and stent diameter (Table 5). Virtual histology-intravascular ultrasound findings by fibrofatty volume At the MLA, the external elastic membrane (EEM) area, lumen area, plaque and media (P&M) area, PB, FA and FFA were significantly larger and higher in the high FFV group than in the low FFV group. Remodeling index and positive remodeling were not significantly different between the two groups (Table 6). For the entire lesion length (21.4±8.3 mm vs. 16.3±6.5 mm, p< 0.001), EEM volume (382.2±174.0 mm3 vs. 197.2±87.2 mm3, p< 0.001), lumen volume (141.7±69.5 mm3 vs. 87.0±38.1 mm3, p< 0.001), P&M volume (240.5±113.6 mm3 vs. 110.9±54.0 mm3, p< 0.001), FV (97.6±52.7 mm3 vs. 34.4±19.9 mm3, p<0.001), FFV (28.2±20.6 mm3 vs. 4.1±2.4 mm3, p<0.001), DCV (11.8±10.9 mm3 vs. 7.6±8.1 mm3, p<0.001), and NCV (26.3±21.3 mm3 vs. 15.5±13.7 mm3, p<0.001) were significantly higher in the high FFV group than the low FFV group. The high FFV group also had significant corrected higher total plaque and media volume (114.8±35.7 mm3/cm vs. 69.8±24.8 mm3/cm, p<0.001), corrected FV (46.9±19.4 mm3/cm vs. 22.2±11.9 mm3/cm, p<0.001), corrected FFV (14.3±10.2 mm3/ cm vs. 2.9±2.3 mm3/cm, p<0.001), and corrected NCV (12.2±8.7 mm3/cm vs. 9.5±7.5 mm3/cm, p=0.002) per fixed segment length, except corrected DCV. Clinical outcomes by fibrofatty volume A composite rate of MACCE was 25.4% in the high FFV group and 14.7% in the low FFV group (p=0.015), but all-cause death (11.2% vs. 5.3%, p=0.051), nonfatal MI (0.6% vs. 0.6%, p=1.000), cerebrovascular events (1.8% vs. 0.0%, p=0.123) and TVR (11.8% vs. 8.8%, p=0.378) were not significantly different between the two groups Table 7. Long-term clinical outcomes by FFV Variable

Low FFV

High FFV

p

MACCE (%)

25 (14.7)

43 (25.4)

0.015

9 (5.3)

19 (11.2)

0.051

All-cause death (%) Sudden cardiac death

3 (1.8)

7 (4.1)

0.218

Non-cardiac death

6 (3.5)

12 (7.1)

0.154

1 (0.6)

1 (0.6)

1.000

0 (0.0)

3 (1.8)

0.123

15 (8.8)

20 (11.8)

0.378

Non-fatal MI (%) Cerebrovascular events (%) TVR (%)

FFV: fibrofatty volume, MACCE: major adverse cardiac and cerebrovascular events, MI: myocardial infarction, TVR: target vessel revascularization

http://dx.doi.org/10.4070/kcj.2013.43.6.377

382 Coronary Plaque Composition and Long-Term Clinical Outcomes

Cumulative MACCE free survival rate

1.0

0.8

Low FFV (n=170) High FFV (n=169)

0.6

Mean follow up duration =28.0±11.6 months Log rank p=0.012

0.4 0

10

20

30

40

Follow up duration (months)

Fig. 1. Comparison of Kaplan-Meier curves for major adverse cardiac and cerebrovascular events (MACCE) according to fibrofatty volume (FFV). A log-rank showed a significant difference (p=0.012) between the low FFV (FFV≤8.90 mm3) and high FFV groups (FFV>8.90 mm3).

(Table 7). The Kaplan-Meier curve showed a significantly higher MACCE rate in the high FFV group (p=0.012) (Fig. 1).

Discussion To the best of our knowledge, this is the first study showing the impact of VH-IVUS findings at a culprit lesion on long-term clinical outcomes in patients who had CAOD and underwent PCI. The main finding of this study is that high FFV in a culprit lesion was an important predictor of long-term clinical outcomes. Previous studies showed that the FFV of culprit lesions in patients who had undergone primary PCI was an important factor in long-term clinical outcomes.12) In the present study, subjects with stable angina as well as ACS were included and FFV was also an important predictor of long-term clinical outcomes, but not other plaque compositions including NCV and DCV. Corrected high FFV adjusted by lesion length also had a similar tendency to increase the incidence of MACCE, although it was not statistically significant (p=0.055). One study showed that NCA and DCA were greater in the nonculprit lesions of patients who had diabetes and metabolic syndrome with MACE than without MACE.10) However, this study showed no significant differences in plaque compositional area, including FA, FFA, DCA, and NCA for long-term MACCE. The reason could possible be because the study included only ACS patients, and the result was from non-culprit lesions. The PROSPECT study showed TCFA, high PB, and small MLA were related with high MACE in non-culprit lesions in ACS.9) Our study showed that TCFA was correlated with ACS, but was not related with MACCE. In addition, high PB and small MLA did not affect MACCE. http://dx.doi.org/10.4070/kcj.2013.43.6.377

The high FFV group had larger MLA and had a worse MACCE rate than the low FFV group. This difference could result from the problems of culprit or non-culprit lesions and whether they included stable angina or not. Previous studies have demonstrated that plaque composition has several clinical significances, such as positive remodeling, plaque vulnerability, slow flow phenomenon, risk factor for sudden coronary death and cardiovascular risk factors.13-17) Several studies have revealed that positive remodeling lesions have a large lipid burden,7)18)19) but also less necrotic core percent area at the MLA site, compared with intermediate/negative remodeling lesions.15) It has been shown that a large amount of calcium deposition is related to negative remodeling,19)20) whereas other studies identified calcium deposition as an indicator of positive remodeling.18)21) The association between coronary artery remodeling and plaque composition remains controversial. In this study, coronary artery remodeling was not found to be correlated with MACCE. Plaque composition is associated with plaque disruption and thrombosis, which leads to acute coronary events.22-25) Lesions with a large lipid core have a higher risk for disruption than sclerotic plaques.25-27) However, there were differences in clinical outcomes according to the coronary plaque composition. Bae et al.13) reported that patients with slow flow were associated with more FV and FFV, but not NCV, at the time of the primary PCI for acute MI. Nakamura et al.14) also reported a trend towards a larger percentage of FFV in the no-reflow group than in the normal-reflow group in a VH-IVUS study. Our study also showed that there was a significant correlation between high FFV and post-stent slow flow. To clarify this result, further investigation is required. Our study has some limitations. We arbitrarily divided the study population into two groups according to plaque composition, because the present study focused only on the clinical outcomes according to coronary plaque composition. Furthermore, until now, there is no cut-off value or appropriate value for the absolute plaque composition. There may be the possibility of selection bias. However, multivariate analysis showed that FFV was a significant independent predictor of MACCE. Subgroup analysis depending on clinical manifestations such as ACS showed that FFV was not a significant predictor of MACCE. These conflicts should be resolved in future studies. Second, the current VH-IVUS tree is not able to differentiate intraluminal thrombus from other plaque components.28) Thrombus may be misclassified as fibrous plaque (fibrofatty dependent on age), proportionally increasing this plaque component at the expense of others.29) Therefore, we analyzed the lesions, excluding the plaque cavity, in order to avoid any possible thrombus, potentially leading to an underestimation of NCA. Finally, we did not analyze the impact of lesion type of the culprit lesion on MACCE, www.e-kcj.org

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because there are still some ambiguous issues with the lesion type, such as lumen containing necrotic core angle, which is a main important characteristic of TCFA. Similarly, the amount of calcium can be misinterpreted due to some artifact usually expressed as a quadrangle. In conclusion, the present study suggests that VH-IVUS analysis for plaque composition in a culprit lesion may be useful to predict the risk of long-term clinical outcomes in patients who have undergone PCI.

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