changing views toward clinical cvd risk prediction -...
TRANSCRIPT
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?
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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
Proportion of Population
Proportion of Population
Proportion of Population
OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline ElevatedElevatedElevatedElevated Very Very Very Very
ElevatedElevatedElevatedElevated
Risk Factor DistributionRisk Factor DistributionRisk Factor DistributionRisk Factor Distribution
Level of Risk FactorLevel of Risk FactorLevel of Risk FactorLevel of Risk Factor
Relative Risk of CVD
Relative Risk of CVD
Relative Risk of CVD
Relative Risk of CVD
ElevatedElevatedElevatedElevated Very Very Very Very
ElevatedElevatedElevatedElevated
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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Level of Risk FactorLevel of Risk FactorLevel of Risk FactorLevel of Risk Factor
Total CVD Deaths
Total CVD Deaths
Total CVD Deaths
Total CVD Deaths
ElevatedElevatedElevatedElevated Very Very Very Very
ElevatedElevatedElevatedElevated
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
OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline
Level of Risk FactorLevel of Risk FactorLevel of Risk FactorLevel of Risk Factor
Proportion of Population
Proportion of Population
Proportion of Population
Proportion of Population
ElevatedElevatedElevatedElevated Very Very Very Very
ElevatedElevatedElevatedElevated
PopulationPopulationPopulationPopulation----Based ApproachBased ApproachBased ApproachBased Approach
OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline
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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Level of Risk FactorLevel of Risk FactorLevel of Risk FactorLevel of Risk Factor
Proportion of Population
Proportion of Population
Proportion of Population
Proportion of Population
ElevatedElevatedElevatedElevated Very Very Very Very
ElevatedElevatedElevatedElevated
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
OptimalOptimalOptimalOptimal BorderlineBorderlineBorderlineBorderline
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
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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
Age, Total Cholesterol, HDL-C,
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
Age, Total Cholesterol, HDL-C,
BP, Diabetes status, Smoking,
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,
BP, Diabetes status, Smoking,
Gender, White of African American
Ethnicity, AntihypertensionMedication use
THE IMPORTANCE OF TEMPORAL TRENDS IN CVD INCIDENCE
Section III
September 24, 2014 36
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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
September 24, 2014 41
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
September 24, 2014 48
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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
September 24, 2014 52
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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