Raimund Erbel,

M Bauer, Hagen Kälsch, Stefan Möhlenkamp

Department of Cardiology

on behalf of the Investigative Group of the HNR study

University Duisburg-Essen

www.wdhz.de

erbel@uk-essen.de

SHAPE 2011

Europrevention Geneva

14. – 16. April 2011

Detection of Subclinical Atherosclerosis with Calcium

Scoring for Improved Risk Prediction

Leszek K Borysiewicz Kerstin Dudas et. al. Circulation 123:46-52, /2011

Proportion of CHD deaths

(%) within 28 days occurring

in hospital by sex, age, and

calendar year, 1991 to 2006.

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis and Biomarkers

Proportion of in-Hospital CHD Death

Women

Men

Mortality due to CHD in the

hospital (within 28 days) and out

of the hospital per 100 000

population 35 10 84 years of

age, 1991 to 2006.

Kerstin Dudas et. al. Circulation 123:46-52, /2011

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis and Biomarkers

Most Deaths of AMI occur out of the hospital

Assmann et al. Circulation 105:310-315, 2002

JAMA 385, 2001

B. PROCAM

A. Framingham

1.Step: Score based Risk – Stratification

Graham I et al EJCPR 14 (suppl 2:S1-113), 2007

detection of signs of

subclinical

atherosclerosis

> 20% /

10 years intensive therapy /

risk factor

modification

High risk =

equivalent to

post AMI

Low risk < 10% /

10 years

Intermediate

risk 10 – 20% /

10 years

Greenland et al. Circulation 2000;101:111-116 Greenland et al. Circulation 2001;104:1863-1867

•NCEP / ATP III JAMA 2001;285:2486-97 # Erbel et al. Atherosclerosis 2007;197:662-72

+

-

2. Step based Risk – Categorization

advice for

healthy lifestyle

0

4

8

12

16

20

Low Intermediate High

p=0.003

Framingham Risk Score

Ob

serv

ed

5-y

r E

ven

t R

ate

[%

]

Events / # at Risk:

Relative Risk:

37 / 1303

2.46 (1.49-4.07)

29 / 498

5.04 (2.98-8.53) 25 / 2165

1.0

p=0.0003

5.8 %

2.8 % 1.2 %

2. Step: Risk Prediction for Coronary Events

using Framingham Risk Score in HNR study

Erbel R et. al. JACC 56:1397-406, 2010

Imaging techniques

· Non imaging techniques

· Stress ECG (M 45 - 60 J)

· Biomarker

Prevalence of risk categories in Germany

> 20% /

10 years

intensive therapy /

risk factor

modification

High Risk

for healthy lifestyle

Low Risk < 10% /

10 years

Intermediate

Risk 10 – 20% /

10 years

31% / 9%

30% / 71%

39% / 20%

Men / Women

Greenland et al. Circulation 2000;101:111-116 Greenland et al. Circulation 2001;104:1863-1867

•NCEP / ATP III JAMA 2001;285:2486-97 # Erbel et al. Atherosclerosis 2007;197:662-72

+

Data from the Heinz Nixdorf Recall Study#

(incl. ATP III risk equivalents*)

-

3. Step: subclinical signs of atherosclerosis

used for further risk stratification

modified according to Erbel R et al HERZ 32:351-55, 2007

originally ERBEL R HERZ 21: 75-77, 1996

0% 20% 45% 50% 70% 90%

Invasive Methods

EKG

ECHOCARDIOGRAPHY

PET

CT/CTA

Non invasive Methods

OCT IVUS/ICD IRS

CORONARY ANGIOGRAPHY

SCINTIGRAPHY

Remodeling

Imaging of Coronary Subclinical Atherosclerosis D

i000802

Vasomotion testing

MRT

Life time

Score

115

Score

2609

Score

49 Ao

RVOT

LM

LAD

CAC

No CAC

56 year M

50 year M

51 year M

64 year F

Detection - Distribution – Quantification

Non-Invasive Imaging of Subclinical

Coronary

Atherosclerosis using Computed

Tomography

Non-Invasive Imaging of Subclinical Coronary

Atherosclerosis using Computed Tomography

- < 20 s scan time

- 1-1.3 mSv X-ray exposure

- 100 ms acquisition time

- standardized protocols:

Agatston-Score

- 15-20 min total time

- 0.94 Kappa value for inter-

institutional variation

Imaging of coronary

artery calcification as

a specific sign of

atherosclerosis

Agatston et al. JACC 15:827-32, 1990

Hunold P et al Radiology 226:14552,2003

Schmermund et al . Z Kardiol 92:I/385,2003

0

4

8

12

16

20

0 <0-99 100-399 ≥ 400CAC Scoring

Ob

serv

ed

5-y

r E

ven

t R

ate

[%

] Events / # at Risk:

Crude Relative Risk:

FRS-adjusted* RR:

24 / 1624

1.73 (0.85-3.52)

1.46 (0.71-3.00)

23 / 659

4.08 (2.00-8.33)

3.06 (1.48-6.32)

11 / 1287

1.0

1.0

33 / 396

9.75 (4.97-19.11)

6.25 (3.01-13.00)

p=0.13

p=0.002

p=0.0007

0.9 % 1.5 %

3.5 %

8.3 %

3. Step: Improving Risk Prediction for Coronary Events

using Signs of Coronary Subclinical Atherosclerosis by CT

Erbel R et. al. JACC 56:1397-406, 2010

Greenland et al. ACCF/AHA 2007 Clinical expert consensus document JACC 115:402, 2007

Erbel et al JACC 56:1397-406, 2010

categories

Meta-analysis

HNR study

Meta-analysis

HNR study

Meta-analysis

HNR study

Meta-analysis

HNR study

Improvement of Risk Prediction for Coronary Events

using Signs of Coronary Subclinical Atherosclerosis by CT

Elias-Smale SE et al JACC 56:1407-14, 2010

Rotterdam Study

Improvement of Risk Prediction for Coronary Events

using Signs of Coronary Subclinical Atherosclerosis by CT

low intermediate high

NRI: 20.8% (p=0.0004)

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis by CT

demonstrated by the Net Reclassification Improvement NRI

Erbel R et. al. JACC 56:1397-406, 2010

Classification

according to FRS

Reclassification accounting

for CAC scores

Low Intermed. High Total

Coronary events

<10%

10-20%

>20%

Total Number

25

12

0

37

0

9

0

9

0

16

29

45

25

37

29

91

No coronary events

<10%

10-20%

>20%

Total

2140

805

0

2945

0

293

0

293

0

168

469

637

2140

1266

469

3875

NRI: 20.8% (p=0.0004)

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis by CT

demonstrated by the Net Reclassification Improvement NRI

Erbel R et. al. JACC 56:1397-406, 2010

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis by CT

demonstrated by the Net Reclassification Improvement NRI

Erbel R et. al. JACC 56:1397-406, 2010

Classification

according to

FRS 10-year event

rate

Reclassification accounting

for CAC scores

low intermed. high Total

with events

- low

- intermediate

- high

Total

7

27

0

34

0

12

0

12

0

18

29

47

7

57

29

93

without events

- low

- intermediate

- high

Total

933

1870

0

2803

0

479

0

479

0

246

508

754

933

2595

508

4036

NRI = 30.6% (p<0.0001)

Comparison to the FRS 6-20% instead of 10-20%

Risk Marker / Factor: NRI p-value Reference

Multiple Biomarker Score 26.7% p=0.005 (Zethelius, NEJM 2008)* (Troponin I, NT-proBNP, Cystatin C, CRP)

Multiple Biomarker Score 14.6% p=NS (Melander, JAMA 2009)* (MR-proADM, NT-proBNP)

HDL-Cholesterol (Framingham) 12.1% p<0.001 (Pencina, Stat Med 2008)

HDL-Cholesterol (SCORE-Data) 2.2% p=0.006 (Cooney, EJCPR 2009)

hsCRP (women) 5.7% p<0.0001 (Cook, Ann Int Med 2006)

hsCRP (men and women) 11.8% p<0.009 (Wilson Cirulation 2008)

hsCRP (men) 14.1% p<0.001 (Ridker, Circulation 2008)*

HbA1c (men) 3.4% p=0.06 (Simmons, Arch Int Med 2008)

HbA1c (women) - 2.2% p=0.27 (Simmons, Arch Int Med 2008)

CAC

HNR(ATP III, FRS 10-20%, 6-10%) 18.8, 21.7%, 30.6% p=0.0002 (Erbel, JACC 2010)*

Rotterdam FRS 10 – 20 % 14% p<0.01 also hard events,older

MESA FRS 6 – 20% 30% p<0.001 also soft endpoints

modified from Cooney et al. JACC 54 :1209-1227, 2009

Erbel R et al JACC 56 :1397- 406, 2010

Improvement of Risk Prediction for Coronary Events

using Signs of Subclinical Atherosclerosis and Biomarkers

I dedicate my lecture to Philip Poole-Wilson

and Helmut Drexler

„... we are still living in a world where almost 1/3 of

the patients who die ... die suddenly before we were

even aware that these people were ill or that their

lives were in jeopardy. So it seems to me that the

most important problem we face is to find a way

of recognizing these people before they drop

dead and tell us that they were sick“

In: Coronary Heart Disease, 3rd Int. Symposium

Frankfurt, Kaltenbach M, Lichtlen P, Balcon R,

Bussmann WD (eds) Thieme, Stuttgart 1978; 83

Mason Sones in

Frankfurt 1978

Conclusion

Detection of Subclinical Atherosclerosis with Calcium Scoring

for Improved Risk Prediction

In comparison to other signs of subclincial atherosclerosis

CAC seems to be the method of choice for improvement

of risk prediction.

And cardiology has to turn its attention to prevention,

because here the biggest target for risk improvement.

The majority of patient (60 to 80 %), who die from AMI,

die outside the hospital and do not reach the hospital.

University Clinic Essen, University Duisburg-Essen

• Department of Cardiology R Erbel, S Möhlenkamp, M Bauer, H Kälsch

• IMIBE KH Jöckel, S Moebus,

B Hoffmann, N Lehmann, U Roggenbuck

• Department of Endocrinology K Mann

• Division of Laboratory Research K Mann, M Bröcker-Preuß

• Institute of Health Economics J Wasem

University Düsseldorf

• Institute of Medical Sociology J Siegrist, N Dragano

Cardioangiological Center Bethanien, Frankfurt A Schmermund

Martin-Luther-University of Halle-Wittenberg

• Institute of Clinical Epidemiology A Stang

Thanks To…