atherosclerosis, stenosis and ischemia

Atherosclerosis, Stenosis and Ischemia:

One Primary, One Secondary, One Tertiary

James K. Min, MD Professor of Radiology and Medicine

Weill Cornell Medicine

Director, Dalio Institute of Cardiovascular Imaging

NewYork-Presbyterian Hospital

Editor-in-Chief

Journal of Cardiovascular Computed Tomography

• Research Support: National Institutes of Health, Dalio Foundation,

General Electric, Michael Wolk Foundation

• Medical Advisory Board: General Electric, Arineta

• Ownership Interest: Cleerly

Disclosures

• Quote

• Premise

• Patient

• Prediction

Outline

If I had an hour to solve a problem and my life depended on it,

I would use the first 55 minutes determining the proper questions to ask.

- Albert Einstein

Quote

• Atherosclerosis evaluation should be the primary objective to define (a)

clinical risk, (b) identify ischemia, and (c) guide therapeutic decision

making to improve patient-centered outcomes.

• Atherosclerosis = primary disease process

• Stenosis = secondary anatomic consequence of atherosclerosis

• Ischemia = tertiary physiologic consequence of atherosclerosis plus stenosis

Premise

We are asking the right questions . . . in the wrong order

Left anterior descending artery1st Diagonal Branch

Left circumflex artery Right coronary artery

• “Obstructive”

• “Obstructive”

• “Non-obstructive”

• Normal

63 y/o man with chest pain on exertion

What is the question that we are trying answer?

Diagnostic Performance of CT versus Cath for High-Grade Stenosis

N Prevalence Sensitivity Specificity PPV NPV

ACCURACY1 230 25% 94 83 48 99

Stable Chest Pain; No known CAD; No exclusion for calcium score, heart rate or body mass

index

Europe2 360 68% 99 64 85 97

Acute and Stable Chest Pain; No known CAD

CorE643 291 56% 85 90 91 83

Stable Chest Pain; No known / Known CAD; Exclusion for coronary calcium score >600

Source: 1Budoff et al. J Am Cardiol 2008; 2Meijboom et al. J Am Coll Cardiol 2009; 3Miller et al. N Engl J Med 2008;

• Prospective multicenter studies comparing CT to invasive coronary angiography for

identification and exclusion of >50% stenosis

Diagnostic Performance of CT is Comparable to Other Methods

Test* Sensitivity Specificity

Exercise ECG treadmill1 68% 77%

Exercise Echo treadmill2 86% 81%

Dobutamine Echo2 ~85% ~85%

Exercise nuclear treadmill3 87% 73%

Pharmacologic nuclear3 89% 75%

Coronary CTA4 95% 83%

Source: ACC/AHA 2002 Guideline Update for Exercise Testing; ACC/AHA/ASE 2003 Guideline Update for the Application of Echocardiography; ACC/AHA/ASNC Guidelines for the Clinical Use of Cardiac Radionuclide Imaging;

ACCURACY study

*Compared to invasive angiography >50% stenosis

There is no better non-invasive diagnostic test for the

detection and exclusion of high-grade coronary stenosis.

Source: Min JK et al. JACC 2011; Leipsic J et al. Radiology 2013

Stenosis Severity: Prognostic Utility of CT Findings

0

2

4

6

8

10

12

14

16

1VD 2VD 3VD

HR 1.93 HR 2.74 HR 6.09

>6-fold higher mortality for patients with 3V “mild” CAD

n=2,583, all with <50% stenosis

Followed for 3.1 years for ACM

“Mild” Stenoses Prognosticate Events in Dose-Dependent Fashion.

Source: Lin FY et al. J Am Coll Cardiol 2008

Source: Ostrom et al. J Am Coll Cardiol 2008; Andreini et al. JACC Imaging 2012; Hadamitzky et al. Eur Heart J 2013; CONFIRM

The Absence of Stenosis Confirms the Absence of Risk“Warranty Period” of Normal CT >10 Years

Extent/Severity of CAD Findings

Ris

k*

*Adjusted or unadjusted • Elderly

• Men and Women

• No Hx CAD

• CAC=0

• No modifiable risk

factors

• Known CAD

• Post-CABG

• Post-PTCA

• Ethnicity

• Diabetes

• Young

• Family history

• LV dysfunction

• Known CAD

• Chronic total

occlusion

• CKD

Stenosis Severity: Prognostic Utility of CT Findings

Source: Min et al. J Am Coll Cardiol 2011; Chow et al. Circ CV Imaging 2011; Villines et al. J Am Coll Cardiol 2011; Cheng et al. Circulation 2011; Cho et al. Circulation 2012; Chow et al. J Am Coll Cardiol 2011;

Villines et al. Am J Cardiol 2012; Shaw et al. J Am Coll Cardiol 2012; Min et al. Eur Heart J 2012; Nakazato et al. Atherosclerosis 2012; Rana et al. Diabetes Care 2012; Otaki et al. J Nucl Cardiol 2012; Hadamitzky M

et al. J Am Coll Cardiol 2013; Al-Mallah MH et al. Eur Heart J Cardiovasc Imaging 2014; Shah S et al. J Nucl Cardiol 2014; Nakazato R et al. Eur Heart J Cardiovasc Imaging 2014; Arsanjani R et al. Radiology 2014;

Leipsic J et al. Radiology 2014; Cho I et al. Eur Heart J 2015; Schulman-Marcus J et al. Atherosclerosis 2015; Otaki Y et al. Eur Heart J Cardiovasc Imaging 2015; Chow BJ et al. Arterioscler Thromb Vasc Biol 2015;

Ahmadi A et al. PLoS One 2015; Gebhard C et al. Eur Heart J Cardiovasc Imaging 2015; Cheruvu C et al. J Cardiovasc Comput Tomogr 2016; Schulman-Marcus J et al. JACC Cardiovasc Imaging 2016; Gebhard C et

al. Eur Heart J Cardiovasc Imaging 2017; Blanke P et al. JACC Cardiovasc Imaging 2016; Andreini D et al. Int J Cardiol 2017; Naoum C et al. Circ Cardiovasc Imaging 2017; Schulman-Marcus J et al. Eur Heart J

Cardiovasc Imaging 2017; Deseive S et al. Eur Heart J Cardiovasc Imaging 2017; Nakanishi R et al. Hypertension 2017; Xie JX et al. Circ Cardiovasc Imaging 2017

Source: Schulman-Marcus J et al. Atherosclerosis 2015; Min JK et al. Eur Heart J 2012

Aspirin StatinsBeta

BlockerAce

Inhibitor

% Difference -26 -43 5 38

-26-43

5

38

-100

-50

0

50

100

% D

iffe

ren

ce

in

MA

CE

1° Prevention

2° Prevention

• N=1,637 patients with >50% stenosis

• Outcome: MACE @ 3 years

Medical Therapy

*

*p<0.05

• N=15,223 (high risk vs. non-high risk dx)

• Outcome: Mortality @ 2.1 years

Revascularization

Effective Stenosis-Guided Therapy Improves Survival

• Hypothesis: Use of a CT-based strategy will improve the diagnosis of angina

pectoris secondary to CHD at 6 weeks

RANDOMIZE

Standard of CareStandard of Care

plus CCTA

Suspected angina from CHD

(n-4146)

Additional Endpoints

• Diagnosis of any CHD

• Changes in planned

investigations

• Changes in therapy

Source: SCOT-HEART Investigators, Lancet 2015


SCOT-HEART Randomized Controlled Trial


27

1518

9

1 14

10

5

10

15

20

25

30

Dx of CHD Planned testing Preventive Med Tx Anti-anginal

CT Stress Testing

% R

ecla

ssific

ation C

hange

38% Reduction in Fatal / Non-Fatal MI at 1.7 years (p=0.06)





SCOT-HEART 3-Year Follow-Up

• CCTA performed

• Results reviewed

• Management changes

• Preventive therapy initiated

• Coronary revascularization

Implementation Delay

HR 0.50

p=0.020

Impact of Alterations

in Therapy

• Equal revasc

• More preventive Tx

(HR 4.03, p<0.01)

0

-40

-33

-50 -61

-100

-80

-60

-40

-20

0

Hosp for AMI Death or MI Death or MI Death

Reduction in A

dvers

e E

vents

(n=282,830),

CMS

(n=10,003),

PROMISE

(n=4146),

SCOT-Heart

(n=4244),

Observational

Time 180 d 12 months 20 months 80 months

Source: Hlatky MA et al. JAMA 2011; Douglas P et al NEJM 2015; Newby D et al. Lancet 2015, Budoff MJ et al. Atherosclerosis 2014

SCOT-HEART is consistent with all other studies to date.

RANDOMIZE

1600 Stable Patients with Suspected Coronary Heart Disease

Referred to Cath Based Upon ACC / AHA Class II Guideline

Direct Cath StrategyInvasive

Coronary Angiography

Physician Discretion

Selective Cath StrategyNon-invasive

Coronary CT Angiography

Physician Discretion

Source: Chang HJ et al. Late-Breaking Clinical Trials, European Society of Cardiology, August 2016

CONSERVE RCTCan CT serve as a safe, non-invasive, economically efficient “gatekeeper”?


-78

-45-50

-100

-80

-60

-40

-20

0

%


Cath Rates Revasc Cardiovascular Costs

Selective Cath Strategy vs. Direct Cath Strategy

CT alone results in ~80% reduction in invasive coronary angiography.


Direct ICA Selective ICA

MACE Rates4.6%

(33/719)

4.6%

(36/784)

Hazards Ratio for MACE

(p=0.99)

31

1 2

8

02

33

2 2

9

02

0

10

20

30

40

50

CV Hosp CV Death MI UA Urgentrevasc

Stroke

No

. o

f E

ven

ts

No differences in any individual MACE

p=1.00 p=1.00 p=1.00 p=1.00 p=1.00 p=1.00




• Ischemic?

• Ischemic?

• Non-ischemic?

• Normal



Anatomic – Physiologic Discordance by CT

CCTA correlates unreliably to hemodynamic significance of lesions

>50% diameter stenosis >50% diameter stenosis

Only one of these patients has

hemodynamically significant CAD.

Source: Meijboom et al. J Am Coll Cardiol 2008

Maximal MBF through a diseased artery

MBF in the hypothetical case the artery is normal

Lesion-specific ischemia: FFR <0.80

• Only method to pinpoint ischemia-causing lesions

• Only method to guide revascularization to improve event-free survival

Source: Pijls NH et al. J Am Coll Cardiol. 2007; Pijls NH et al. J. Am. Coll. Cardiol. 2010

FAME RCT, N=1,005

“Gold” standard for vessel-based ischemia

Invasive fractional flow reserve (FFR)

• From typically acquired CCTA

• No additional radiation

• No modification to imaging protocols

• No administration of medications

Source: Min JK et al. J Cardiovasc Comput Tomogr 2011, Min JK et al. Am J Card 2012; Min JK et al. J Cardiovasc Comput Tomogr. 2012; Grunau GL et al. Curr Cardiol Report; Min JK et al. JAMA 2012; Koo et al. J Am Coll Cardiol 2012

FFR from CT

FFRCT: Three (3) Prospective Multicenter Trials

DISCOVER-FLOW DeFACTO NXT

Principal Investigator Min (JACC) Min (JAMA) Norgaard (JACC)

Primary end point Per pt. diag accuracy Per pt. diag accuracy; lower

limit 95% CI 0.7

Per pt. AUC

Study sites/ countries 4 / 3 17 / 5 10 / 8

Site expertise qualification FFR CT or FFR CT plus FFR

CT training of site Yes No Yes

FFR training of site No No Yes

CT quality check No No Yes

CT results reading Core lab Core lab Site

FFR results report Site Site Site with core lab overview

Vessel size for inclusion ≥ 2.0 mm ≥ 1.5 mm ≥ 2.0 mm

Software version* V 1.0 manual V 1.2 partial automation ~6

hrs

V 1.4 automation; <4 hours

Source: Koo et al. JACC 2011; Min JK et al. JAMA 2012; Norgaard BL et al. JACC 2014

FFR from CT: Severe LAD Stenosis

Source: Min JK et al. JAMA 2012

CT: Severe Stenosis FFRCT: Lesion-specific Ischemia FFR: Ischemia

FFR from CT: Severe RCA Stenosis

Source: Min JK et al. JAMA 2012

CT: Severe Stenosis FFRCT: No Ischemia FFR: No Ischemia

FFR from CT

• 5 studies, n=539 (908 vessels)

• Only 13% with intermediate stenosis

Source: Cook CM et al. JAMA Cardiology 2017

• Accurate at the extremes

• Between 0.7-0.9 values – ~50%

Source: Min JK et al. J Cardiovasc Comput Tomogr 2011, Min JK et al. Am J Card 2012; Min JK et al. J Cardiovasc Comput Tomogr. 2012; Grunau GL et al. Curr Cardiol Report; Min JK et al. JAMA 2012; Koo et al. J Am Coll Cardiol 2012

FFRCT considers only the lumen.

• Stenosis alone

Source: Cook CM et al. JAMA Cardiology 2017

• Atherosclerosis

Sparse feature set Rich feature set

FFRCT considers only the lumen.

• Atherosclerotic plaque characteristics

1. Stenosis (%DS, %AS, MLD, MLA)

2. Non-obstructive stenoses

3. Plaque burden (volume, area, thickness)

4. Plaque composition (non-calcified, calcified)

5. “Lipid dense” necrotic core (low attenuation plaque)

6. Arterial remodeling (positive, negative, intermediate)

7. Napkin ring sign and “spotty” calcifications (<3 mm length)

8. Absolute material density (dual energy CT)

9. Others (location, length, diffuseness, bifurcations, etc.)

>90% specificity

for necrotic core

Dual Energy Coronary CT Angiography

Source: Thomsen C and Abdulla J, Eur Heart J Cardiovasc Imaging 2016; Rodriguez-Granillo GA et al. Eur Heart J Cardiovasc Imaging 2016; Danad I et al. JACC Cardiovasc Imaging 2015

Atherosclerotic Plaque Characterization by CT

CT Atherosclerosis Characterization: Quantitative and Qualitative

Whole-heart “3-vessel” atherosclerosis

quantification and characterization

Source: Nakazato R et al. J Am Coll Cardiol 2012

Quantifying Plaque Burden Identifies Ischemia: Aggregate Plaque Volume

Source: Nakazato R et al. J Am Coll Cardiol 2012

• DS: 55.6%

• AS 59.5%

• APV 60.2%

• DS 63.3%

• AS 72.2%

• APV 27.8%

Ischemic Lesion Non-Ischemic Lesion

Quantifying Plaque Burden Identifies Ischemia: Aggregate Plaque Volume

Atherosclerosis Defines IschemiaPlaques look different because they act different.

A B C

C D E

The type of atherosclerosis is more important than the presence of atherosclerosis.

Source: Park HB et al. JACC Imaging 2015; Nakazato R et al. Eurointervention 2015

252 patients undergoing CT, ICA and FFR

Plaque Composition Identifies Ischemia

Agnostic to Stenosis Severity

Prospective multicenter international study

(N=251 undergoing CT and invasive FFR)


Lik

eli

ho

od

of

Isc

he

mia





• High-grade Stenosis

• High plaque burden (volume)

• Diffuse

• Calcified + Non-calcified

• Low attenuation plaque

• Negative remodeling


• Mild-moderate plaque burden (volume)

• Focal

• Spotty calcification

• Positive remodeling

• Moderate Stenosis

• Mild plaque burden (volume)

• Diffuse





Source: Pooled data from 4 studies: Ambrose et al, 1988; Little et al, 1988; Nobuyoshi et al, 1991; and Giroud et al, 1992. (Adapted from Falk et al.)

Most Myocardial Infarctions Are Caused By Low Grade Stenoses

Atherosclerosis is the primary driver of risk.

CONFIRM Risk Score: n=13,686 followed for 3.1 years for MACE occurrence

Framingham Stenosis Plaque

Calcified or mixed plaque in

left main 1 point

proximal LAD 1 point

proximal LCx 1 point

first obtuse marginal 1 point

proximal RCA 1 point

mid RCA 1 point

Score calculation based on

Age

Sex

Cholesterol levels

Blood pressure

Diabetes

Nicotine abuse

family history for CAD

score x 1 sum x 6 sum x 9+ +

Confirm-Score

Stenosis >50% in

left main 1 point

proximal LAD 1 point

proximal LCx 1 point

first obtuse marginal 1 point

proximal RCA 1 point

mid RCA 1 point

x 0.3

mid LAD 1 pointmid LAD 1 point

<6: low risk 6-10: intermediate risk >10: high risk

Clinical Risk Factors

CONFIRM Risk Score Low Risk

Annualized event rate 0.3%

(95% CI 0.2-0.5%)

High Risk Annualized event rate 2.1%

(95% CI 1.8-2.5%)

Severe Stenosis Atherosclerosis But No Severe Stenosis

Source: Hadamitzky M et al. JACC 2013

Risk Increases With Atherosclerosis In the Absence of Stenosis.

Source: Cho I et al. Atherosclerosis 2017 (online before print)

N=6,656 followed for 5.1 years

All-cause mortality (n=456)

1.4

2.5

2.2

2

2.9

0

1

2

3

CAC 1-99 CAC >100 <50%Stenosis

Plaque, 0%Stenosis

>50%Stenosis

Ha

za

rds R

atio

High-risk Plaques: Positive Remodeling & Low Attenuation Plaque Qualitative atherosclerosis characterization portends ACS risk

Source: Motoyama S et al. J Am Coll Cardiol 2009

No APCs

PR, LAP, SC

ICONIC: 25,251 patients undergoing CT, 3.4 years

Propensity Score

Age and Gender

Site

CAD Risk Factors

Angiographic CAD extent &

severity^

Patient who experienced

ACS after CCTA

Case (n=234)

Patient who did not experience

ACS after CCTA

Control (n=234)

Source: Chang HJ et al. J Am Coll Cardiol 2018 (in press)

When angiographic CAD extent and severity is the same, do atherosclerotic plaque characteristics matter?

Does quantitative atherosclerosis characterization enhance ACS risk stratification?

ICONIC Results: Maximal % stenosis at time of CT

<50% stenosis

65.4%21.8%

12.8%

Patient

(n=234)

50-70% stenosis >70% stenosis


75.2%20.1%

4.7%

Culprit Lesion

(n=129)

ICONIC Evaluation: High Risk Plaque (%)

52.1

43.2

87.6

30.833.3

27.4

79.9

20.1

0

10

20

30

40

50

60

70

80

90

100

HRP (> or = 2) Low Attenuation Positive Remodeling Spotty Calcification

p=0.003

58% higher

p<0.001

58% higher

p=0.026

9.6% higher

p=0.013

53.2% higher

ACS No ACS


ICONIC Evaluation: Plaque Volume (mm3)

Total

127

112

0

20

40

60

80

100

120

140

Fibrous

p=NSp=NS

ACS No ACS

98

109

0

20

40

60

80

100

120

Calcified

p=NS

290

267

0

50

100

150

200

250

300

350

Total

p=NS


ICONIC Evaluation: Plaque Volume (mm3)

290

267

255

260

265

270

275

280

285

290

295

Total

98

109

92

94

96

98

100

102

104

106

108

110

Calcified

p=NS

127

112

50

60

70

80

90

100

110

120

130

140

Fibrous

p=NS

6.5

4.2

3

3.5

4

4.5

5

5.5

6

6.5

7

Necrotic Core

p=0.026

55% higherp=NS

ACS No ACS

59

41

30

35

40

45

50

55

60

65

Fibrofatty

p=0.009

43% higher


Purely calcified plaques never cause ACS.

The Vulnerable Patient: Characterizing Atherosclerosis

Distance from ostium (mm)

Area (m

m2)

Remodeling index: 1.6

Left Anterior Descending Artery

Spotty Calcifications / Positive Remodeling


975 days later

What is the Most Important Feature of Atherosclerosis?

12/1/09

37% area stenosis

08/08/2013

67% area stenosis

How A Plaques Progresses Over Time

High-Risk Atherosclerotic Plaque Plus Plaque Progression

N=449, 4 years

Source: Motoyama S et al. J Am Coll Cardiol 2015; Ito H et al. Heart Vessels 2014

8.2

1.6 2.1

0

5

10

HRP >70% Stenosis Prior ACS

1.30.11

13.1

17.2

0

5

10

15

20

None >70%Stenosis

HRP Both

8.3

33.4

0

10

20

30

40

Prior ACS Progression

Timepoint 1 Timepoint 2

PARADIGM: Evolution of Atherosclerosis

CT#1MACE

3.8 years

CT#2

• Prospective observational cohort study

• N=1,255, 13 sites, 7 countries

• Quantitative CT evaluation for APCs

• Lab testing, including lipid panels

9 years

Source: Lee SE et al. JACC CV Imaging 2018 (in press)

Source: Chang HJ et al. ESC Hot Line Topics 2017

35% reduction in development in high-risk plaques (HRP) with statins

PARADIGM Does medical therapy slow plaque progression, cause plaque regression, or induce plaque transformation?

1079 lesions

in 474 statin-naïve patients

2,496 lesions

in 781 statin-taking patientsversus

Annualized Change in Plaque Compositions According to Statin Use




• High plaque burden (volume)

• Diffuse

• Calcified + Non-calcified




• Mild-moderate plaque burden (volume)

• Focal

• Spotty calcification

• Positive remodeling

• Moderate Stenosis

• Mild plaque burden (volume)

• Diffuse




Atherosclerosis quantifies risk, identifies ischemia, and improves

therapeutic decision making.

How can we incorporate atherosclerosis findings in daily practice?

512One cardiac CT image has

512 x 512 pixels.

300One patient’s CT imaging

dataset has 300 images.

20One CT imaging dataset has

20 phases.

20One radiologist reads 20

patient’s studies per day.

x

x

xPixels

Images

Phases

Studies

3.15 billion pixels for

1 cardiologist per day.

• “By 2025, 80% of the functions doctors do will be done much better and

much more cheaply by machines and machine learned algorithms.”

--Vinod Khosla

--Founder, Sun Microsystems

The Very Near-term Future

Machine Learning: Superhuman In Every Field To Date

Browsing

Shopping

“Liking”

(Personal

Preferences)

Meteorology

and

Farming

Credit Card

Fraud

SEARCH

“BIG DATA”

Drones

Self-driving

Cars

Voice

Recognition

Gunshot

Detection

HEARING

Oil

Refineries

AUTOMATION

Go

Jeopardy

Game Show

THINKING

Empathy

and

Ethics

SEEING

Facial

Recognition

Medical Imaging?

FEELING

Machines are particularly good at imaging.

• Classification

• Clustering

• Regression

• Association rules

• Ranking

• Grammar induction

• Feature learning

Machines are particularly good at imaging.

• Classification

• Clustering

• Regression

• Association rules

• Ranking

• Grammar induction

• Feature learning

Which stenosis is >70%?

Which plaques cause heart attacks?

Which plaques have necrotic cores?

Which plaque features together increase risk of heart attack?

Which plaques are most dangerous?

How should these findings be reported?

What features are most important?

Machines process images better and faster than we can.

Machines process images better and faster than we can.

Source: https://www.youtube.com/watch?v=VG68SKoG7vE

Machine can foresee outcomes better than we can.

Number of possible moves in Go = 10120

Number of atoms in universe = 1080

Source: nature.com (January 27 2016)

Machine Learning for Integrating Atherosclerosis: Ischemia

Source: Dey D et al. Eur Radiol 2017

• 254 patients (484 vessels) analyzed for stenosis, atherosclerosis and CT scan findings

Ischemia

(n=80)

No ischemia

(n=174)

P

value

Diameter

stenosis (%)

67.6 47.4 <0.0001

Plaque volumes (mm3)

Necrotic core 46.7 13.8 <0.0001

Non-calcified 273.3 96.8 <0.0001

Calcified 10.6 6.5 0.01

Length (mm) 42.9 19.6 <0.0001

Contrast

difference

33.1 14.3 <0.0001

• 254 patients (484 vessels) analyzed for stenosis, atherosclerosis and scan findings

• Highest predictive value for contrast density differences and low attenuation plaque

Source: Dey D et al. Eur Radiol 2017

Machine Learning for Integrating Atherosclerosis: Ischemia

Machine Learning for Atherosclerosis Risk Prediction

LAD Diag 1

Diag 2

RCALCX

Source: Motwani M et al. Eur H Journal 2017

10,030 patients followed for 5 years

44 CT + 25 clinical parameters

Conclusion

1. Atherosclerosis is primary; Stenosis is secondary; and Ischemia is tertiary

2. Atherosclerosis is the primary driver of future adverse events, and its treatment

improves event-free survival

3. Atherosclerosis-Defined Ischemia is a robust method for determining the ischemia-

causing nature of coronary artery lesions

4. Machine learning will transform the way we evaluate coronary artery disease.

Thank you.

Treating CT Findings Improves Outcomes

RANDOMIZE

Standard of CareStandard of Care

plus CT

Suspected angina from Coronary Heart Disease

(n-4146)

Source: Newby D and the SCOT-HEART Investigators, Lancet 2015

• Conclusion: Improved diagnostic certainty by use of a CT strategy

• Hypothesis: Use of a CT-based strategy will improve the diagnosis of angina pectoris

secondary to coronary heart disease at 6 weeks


(2) Identify and Reduce Risk of Cardiac Events

• Identify coronary artery lesions that may cause future

cardiac events

• Institute medical therapy and to reduce risk

Source: Pooled data from 4 studies: Ambrose et al, 1988; Little et al, 1988; Nobuyoshi et al, 1991; and Giroud et al, 1992. (Adapted from Falk et al.)

(1) Identify and Relieve Myocardial ischemia

• Determine hemodynamic significance of CAD

• Identify high-grade stenosis (that may benefit from

revascularization)

Two (2) Major Goals for For Patients With Coronary Artery Disease

• Atherosclerosis = primary disease process

• Stenosis = secondary anatomic consequence of atherosclerosis

• Ischemia = tertiary physiologic consequence of atherosclerosis plus stenosis

Atherosclerosis Defines Ischemia

Plaque Volume (r=0.95, p<0.0001)

(105.0 vs. 109.4 mm3, p=NS)

1 mm cross-sections

• No Differences (Interobserver correlation, r=0.94, p<0.001)

- Plaque Burden: Volume, MLA, MLD, %AS, %DS

- Positive Remodeling

- Lipid-rich (Low Attenuation Plaque)

- Spotty Calcification

Source: Nakazato R et al. Eur Radiol 2013

CT Atherosclerosis Characterization: Comparison to IVUS

The Effect of Atherosclerosis on Perivascular Adipose Tissue Defines Risk.

• 453 patients undergoing cardiac surgery: related ex vivo images with CT scan

• FAI (Fat attenuation index) that describes adipocyte lipid content and size

• Identifies CAD

• A/w 18F-FDG PET inflammation

Source: Antoniades C et al. Science Transl Med 2017

ATHEROSCLEROSIS

Improved methods for atherosclerosis evaluation

>50% stenosis High-risk plaque Important

findings?

Machine Learning: Atherosclerosis-Defined IschemiaWhich of these lesions is ischemic?

A B C

C D E

Source: Mancini GB et al. JACC CV Interventions 2014

Atherosclerosis explains the relationship of ischemia to events.

Perc

en

t w

ith D

eath

, M

I or

NS

TE

-AC

S

Ischemic Myocardium

Angiographic

CAD Burden

(% stenosis)

COURAGE Trial

Ischemia does not improve risk stratification over anatomy.


Right coronary artery Left anterior descending

No ischemia(FFR 0.94)

Ischemia(FFR 0.79)

17% of CT lesions <50% stenosis exhibit ischemia by invasive FFR.

Plaque Composition Identifies Ischemia:Identification of Ischemia Agnostic to Stenosis Severity

atherosclerosis, stenosis and ischemia

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