changing views toward clinical cvd risk prediction -...

24/09/2014

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September 24, 2014 1

Presented by: Michael J. Blaha

Changing Views Toward Clinical CVD Risk Prediction

Michael J. Blaha MD MPH

Rationale, History, and the Modern Problem of Overestimation

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Talk Outline

• Rationale for clinical CVD risk prediction

• History of CVD risk scores

• Temporal trends in CVD incidence

• Modern problem of overestimation

• Fallacies of the risk factor model

• How do we improve modern risk

prediction?


RATIONALE FOR CLINICAL CVD RISK PREDICTION

Section 1


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Level of Risk FactorLevel of Risk FactorLevel of Risk FactorLevel of Risk Factor

Proportion of Population




OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline ElevatedElevatedElevatedElevated Very Very Very Very

ElevatedElevatedElevatedElevated

Risk Factor DistributionRisk Factor DistributionRisk Factor DistributionRisk Factor Distribution


Relative Risk of CVD




ElevatedElevatedElevatedElevated Very Very Very Very


Risk Factors and CVD RiskRisk Factors and CVD RiskRisk Factors and CVD RiskRisk Factors and CVD Risk

High relative High relative High relative High relative

riskriskriskrisk

OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline

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Total CVD Deaths

Total CVD Deaths

Total CVD Deaths

Total CVD Deaths



Risk Factors and Total Number of DeathsRisk Factors and Total Number of DeathsRisk Factors and Total Number of DeathsRisk Factors and Total Number of Deaths

Total Number of DeathsTotal Number of DeathsTotal Number of DeathsTotal Number of Deaths









PopulationPopulationPopulationPopulation----Based ApproachBased ApproachBased ApproachBased Approach


Prevention Paradox = Prevention Paradox = Prevention Paradox = Prevention Paradox =

Small Small Small Small changes at population changes at population changes at population changes at population

produce large changes in produce large changes in produce large changes in produce large changes in

overall healthoverall healthoverall healthoverall health

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IndividualIndividualIndividualIndividual----Based ApproachBased ApproachBased ApproachBased Approach

“True” “True” “True” “True” increased risk: increased risk: increased risk: increased risk:

Identify via screening, Identify via screening, Identify via screening, Identify via screening,

then treat aggressivelythen treat aggressivelythen treat aggressivelythen treat aggressively


Individual “High-Risk” Prevention – What is the Goal of a Risk Score?

• Relative Risk vs. Absolute Risk?

• Relative Risk ~ “Discrimination”

• Absolute Risk ~ “Calibration”

– Number Needed to Treat

– Number Needed to Harm

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HISTORY OF CHD/CVD RISK SCORES

Section II


The Framingham Heart Study

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“Risk Factor” - 1961

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The Framingham Risk Score - 1998

The Framingham Risk Score - 1998

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National Cholesterol Education Program – Adult Treatment Panel III

17

NCEP ATPIII

18

Assessing CHD Risk in Men

Age

Years Pts

20-34 -9

35-39 -4

40-44 0

45-49 3

50-54 6

55-59 8

60-64 10

65-69 11

70-74 12

75-79 13

CHD Risk

Pts 10-YrCHD Risk

< 0 < 1%0 1%1 1%2 1%3 1%4 1%5 2%6 2%7 3%8 4%9 5%

10 6%11 8%12 10%13 12%14 16%15 20%16 25%

> 17 > 30%

Systolic Blood Pressure

Untreated Treated

<120 0 0

120-129 0 1

130-139 1 2

140-159 1 2

> 160 2 3

Total Cholesterol

(mg/dL) 20-39 40-49 50-59 60-69 70-79

<160 0 0 0 0 0

160-199 4 3 2 1 0

200-239 7 5 3 1 0

240-279 9 6 4 2 1

280 11 8 5 3 1

Cigarette Smoking

Nonsmoker 0 0 0 0 0

Smoker 8 5 3 1 1

HDL-C

(mg/dL) Pts

> 60 -1

50-59 0

40-49 1

< 40 2

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AGE? (CHRONOLOGIC AGE)

HOW GOOD IS NCEP III AT PREDICTING MI? HOW GOOD IS NCEP III AT PREDICTING MI? HOW GOOD IS NCEP III AT PREDICTING MI? HOW GOOD IS NCEP III AT PREDICTING MI? AKOSAHAKOSAHAKOSAHAKOSAH ET AL JACC 2003 ET AL JACC 2003 ET AL JACC 2003 ET AL JACC 2003

222 patients with 1222 patients with 1222 patients with 1222 patients with 1stststst acute MI, no prior CADacute MI, no prior CADacute MI, no prior CADacute MI, no prior CAD

men <55 y/o (75%), women <65 (25%), no DMmen <55 y/o (75%), women <65 (25%), no DMmen <55 y/o (75%), women <65 (25%), no DMmen <55 y/o (75%), women <65 (25%), no DM

75% did not qualify 75% did not qualify 75% did not qualify 75% did not qualify

for pharmacotherapyfor pharmacotherapyfor pharmacotherapyfor pharmacotherapy

High Risk Intermediate Risk Low Risk

18%18%18%18%

12%12%12%12%

70%70%70%70%

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Case Example: WJC at 56 y/o

Total Score =

FRS = 11%

Wilson et al. Circulation 1998; 97: 1837-1847

4

0

1

0

1

0

6

56 y/o male

BP 128/80

TC 210

HDL-C 40

LDL-C 160

Non-smoker

No DM

? FH CHD

GENDER?

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Risk Stratification by Age and Gender

0%

20%

40%

60%

80%

100%

30-39 40-49 50-59 60-69Age (years)Age (years)Age (years)Age (years)

<10%<10%<10%<10% 10-20% 10-20% 10-20% 10-20% >20%>20%>20%>20%

Ford ES et al, JACC 2004

80%

100%

30-39 40-49 50-59 60-69Age (years)Age (years)Age (years)Age (years)

<10%<10%<10%<10% 10-20% 10-20% 10-20% 10-20% >20%>20%>20%>20%

MenMenMenMen

WomenWomenWomenWomen

60% of men

aged 50-59 &

92% aged 60-69

are at least

intermediate

risk

Just 1% women

aged 50-59 &

9% aged 60-69

are at least

intermediate

risk

Case Example – AH, a 40 y/o female smoker with SBP 160, TC 260 LDL 190, HDL 40

_______________________________________________________________

Framingham Points (ATP III 2001)

Age Age Age

40 50 60

___________________

Age 0 6 10

Smoker (yes) 7 4 2

Systolic Blood Pressure 160 mm Hg 3 3 3

Total cholesterol 260 mg/dL 8 5 3

HDL cholesterol 40 mg/dL 1 1 1

____________________

Framingham Risk Score (points) 19 19 19

10-year Framingham Risk 8 % 8 % 8 %

________________________________________________________________

Ridker PM, Cook N. Circulation 2005;111:657-8

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RACE?

D’Agostino et al. JAMA.

2001;286(2):180-7.

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CHD, NOT ALL CVD?

CVD Types

� Coronary Heart Disease

� Stroke

� TIA

� Peripheral Vascular Disease

� Abdominal Aortic Aneurysm

� Heart Failure

� Revascularization??◦ Hard vs. soft endpoints

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10-YEAR RISK, NOT LIFETIME RISK?

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

50 60 70 80 90

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

50 60 70 80 90

Attained Age

Ad

jus

ted

Cu

mu

lati

ve

In

cid

en

ce

5%

36%

50%

69%

8%

27%

50%

Men Women

46%

39%

Lloyd-Jones, Circulation 2006

≥≥≥≥2 Major RFs1 Major RF≥≥≥≥1 Elevated RF≥≥≥≥1 Not Optimal RFOptimal RFs

LIFETIME RISK ESTIMATION

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Alternative Risk Scores

� 2007 – Reynolds Risk Score

◦ More modern cohort, adds family history and C-reactive protein

� 2008 –10-year Framingham CVD Risk Score

◦ Angina, MI, CHD death, stroke, TIA, peripheral vascular disease, heart failure

� 2009 – 30-year Framingham CVD Risk Score

2013 Prevention Guidelines

ASCVD Risk Estimator

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ASCVD Risk Calculator: Pooled

Cohort Equations

Risk Factor Units Value

Acceptable

range of

values

Optimal

values

Sex M or F M or F

Age years 20-79

Race AA or WH AA or WH

Total Cholesterol mg/dL 130-320 170

HDL-Cholesterol mg/dL 20-100 50

Systolic BP mm Hg 90-200 110

Treatment for High BP Y or N Y or N N

Diabetes Y or N Y or N N

Smoker Y or N Y or N N

ASCVD Risk Calculator: 55 Year Old

African-American and White Women

African AmericanWomen

WhiteWomen

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Summary – 25 Years of Risk Scores

Risk Score Target Age Group

Target Cardiovascular Events Variables Included

FraminghamCHD

30 – 74 years Angina, MI, CHD death, coronary insufficiency

Age, Total Cholesterol, HDL-C,

BP, Diabetes status, Smoking, Gender

FraminghamCVD

30 – 74 years Angina, MI, CHD death, stroke, TIA,

peripheral vascular disease, heart failure


BP, Diabetes status, Smoking,

Gender, Antihypertension Medication use

Framingham

CHDATP3 Version

>20 years MI, CHD death Age, Total Cholesterol, HDL-C,

BP, Smoking, Gender, Antihypertension Medication use

Reynolds Risk Score

Women 45 –80 years

Men 50 – 80 years

MI, CHD death, stroke, coronary revascularization



Gender, hs-CRP, Family History,

HbA1c (Female Diabetic Subjects only)

ACC/ACHD

ASCVD(2013)

40 – 79 years MI, CHD death, stroke Age, Total Cholesterol, HDL-C,


Gender, White of African American

Ethnicity, AntihypertensionMedication use

THE IMPORTANCE OF TEMPORAL TRENDS IN CVD INCIDENCE

Section III


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Changing CHD Mortality Over Time

259.4

129.2

85.4

43.5

253.3

126.1

84

42.1

240.8

117.7

82.4

39.6

236.6

113.6

82.7

39.1

0

50

100

150

200

250

300

Total CVD CHD Other CVD Stroke

De

ath

s p

er

10

0,0

00

2007 2008 2009 2010

(ICD-10 I00-I99; Q20-Q28) (ICD-10 I60-I69)(ICD-10 I20-I25) (ICD-10 I00 –I15, I26 –I51, I70

–I78, I80 –I89, I95–I99)

Source: CDC, National Vital Statistics Reports.

U.S. death rates from CVD

2007-2010

38

Recent Progress – Reducing Deaths

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Declining CVD in Medicare Population

39

Krumholz HM. Circulation. 2014 Aug 18.

As CVD Decreases, Less Remaining Risk in Attributable to Risk Factors

40

Cheng. Circulation.

2014; 130.

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THE MODERN PROBLEM OF OVERESTIMATION

Section IV


Is the New Risk Calculator Flawed?

September 24, 201442

“Dr. Blaha said the problem might have

stemmed from the fact that the calculator uses

as reference points data collected more than a

decade ago, when more people smoked and

had strokes and heart attacks earlier in life.

For example, the guideline makers used data

from studies in the 1990s to determine how

various risk factors like cholesterol levels and

blood pressure led to actual heart attacks and

strokes over a decade of observation.

But people have changed in the past few

decades, Dr. Blaha said. Among other things,

there is no longer such a big gap between

women’s risks and those of men at a given age.

And people get heart attacks and strokes at

older ages.

“The cohorts were from a different era,” Dr.

Blaha said.”

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Overestimation

43Ridker and Cook. Lancet. 2013;382:1762-5.

Kavousi. JAMA. 2014;311:1416-23.

Overestimation

MESA

0.0

5.1

.15

.2.2

5.3

.35

.4O

bserv

ed (pro

portio

n)

0 .05 .1 .15 .2 .25 .3 .35 .4Predicted (probability)

AHA-ACC-ASCVD

Predicted (probability)

0.0

5.1

.15

.2.2

5

Observ

ed (pro

portio

n)

0 .05 .1 .15 .2 .25Predicted (probability)

AHA-ACC-ASCVD

Men (n=1,961) Women (n=2,266)

• 10.2 year follow-up • Adjudicated Events

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Linear Regression:

Factors Predicting Overestimation

Dependent variable is the discordance between the observed and expected probabilities

Standard errors in parenthesis. *p<0.05. **p<0.01. ***p<0.001. n=4,227

Model 1: multivariable model of aspirin, anti-hypertensive and lipid lowering medication and revascularization

Model 2: consistent use of aspirin, anti-hypertensive medication or lipid lowering medication at any time during the study, age, revascularization, gender (M), ethnicity, systolic blood pressure, total cholesterol, HDL cholesterol, smoking.

AHA-ACC-ASCVD

Model 1 Model 2

Risk factors

SBP/10 0.68***

(0.03)

Total cholesterol/10

0.08***

(0.01)

HDL/10 -0.32***

(0.03)

Smoker 2.16***

(0.14)

AHA-ACC-ASCVD

Model 1 Model 2

Intercept 2.51*** -23.22***

(0.12) (0.56)

R-squared 0.089 0.526

ATP3-FRS-CHD

Model 1 Model 2

Demographics

Age (per year) 0.19***

(0.01)

Male 4.68***

(0.09)

Chinese -0.09

(0.13)

Black -0.23*

(0.10)

Hispanic -0.02

(0.11)

Linear Regression:

Therapies Predicting Overestimation

n=4,227 AHA-ACC-ASCVD

Model 1 Model 2

Treatment

Aspirin 0.34* 0.04

(0.14) (0.10)

Anti-hypertensive 2.43*** 0.56***

(0.14) (0.11)

Lipid-lowering 0.23 -0.00

(0.14) (0.10)

Revascularization 1.31** 0.28

(0.41) (0.29)

Dependent variable is discordance (continuous)

Standard errors in parenthesis. *p<0.05. **p<0.01. ***p<0.001. n=4,227

Model 1: aspirin, anti-hypertensive and lipid lowering medication and revascularization

Model 2: consistent use of aspirin, anti-hypertensive medication or lipid lowering medication at any time during the study, age, revascularization, gender (M), ethnicity, systolic blood pressure, total cholesterol, HDL cholesterol, smoking.

Risk scoreExpected # Events (%)

Observed # Events (%)

PercentageDiscordance

Men (n=392)

AHA-ACC-ASCVD 33 (8.38) 11 (2.81) 200%

Women (n=398)

AHA-ACC-ASCVD 14 (3.56) 3 (0.75) 366%

Subset –No CVD Therapy

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Calibration is Not Better

Discrimination – Any Better?

Risk score Harrell's C

Men (n=1,961)

FRS-CHD 0.68

FRS-CVD 0.71

ATP3-FRS-CHD 0.72

RRS 0.69

AHA-ACC-ASCVD 0.70

Women (n=2,266)

FRS-CHD 0.59

FRS-CVD 0.70

ATP3-FRS-CHD 0.67

RRS 0.72

AHA-ACC-ASCVD 0.69

FALLACIES OF THE RISK FACTOR MODEL

Section V


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0 10 20 30 40 50 60 70 80 90

AGE �

RIS

K �

Traditional 10-Year Risk Model

ATHEROSCLEROSIS

Risk Factor

Exposure??Outcomes??

0 10 20 30 40 50 60 70 80 90

AGE �

RIS

K �

“Lifetime” Risk Model

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0.1

.2.3

.4.5

Pre

dic

ted

10

-Ye

ar

Ris

k

40 50 60 70 80AGE

Framingham Risk Score ACC/AHA CVD Risk Score

The Ethical Problem of Chronologic Age in Clinical Practice

HOW TO IMPROVE MODERN RISK PREDICTION?

Section IV


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Subclinical

Atherosclerosis

CHD EventDeath

MI

ACS

Revasc.

Genetics

Overt CAD

Ischemia

Environment

Risk Factors

Biomarkers

Coronary Calcification

Continuum of Atherosclerosis Propagation Prior to a CHD Event

hsCRP

Inflammation

Obesity

Hypertension

Dyslipidemia

Diabetes

changing views toward clinical cvd risk prediction -...

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