Transcript Nicorandil and Microvascular Integrity

The Value of Observational Research
A Case Study Approach
Hal V. Barron, MD
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Examine associations and attempt to speculate
on causality when RCTs are not feasible
• Studies have demonstrated the importance of establishing
and maintaining a patent infarct related artery in the
setting of acute myocardial infarction (AMI) complicated
by cardiogenic shock.
• The purpose of the present study was to determine
whether the use of Intra-aortic baloon pumping (IABP) is
associated with a survival advantage in patients with AMI
complicated by cardiogenic shock.
• Why not do a RCT???
National Registry of Myocardial Infarction
(NRMI) :
IABP Use and Outcome
• Using data from the National Registry of Myocardial
Infarction 2 (NRMI 2), we evaluated 23,180 patients
who presented with or developed cardiogenic shock
during the hospitalization.
NRMI : IABP Use and Outcome
IABP -
IABP +
69.2
70
49.5
Mortality (%)
60
44.8
50
40
30
20
10
0
TT
PPTCA
Type of Reperfusion Therapy
47.5
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Data from the TIMI 2 Study
BB+
BB-
6
5.2
5
4
3
2.2
2
0.8
1
0
0
100mg tPA
150 mg tPA
Examine associations and attempt to speculate
on causality when RCTs are not feasible
• Do beta-blockers reduce intra-cranial hemorrhage ICH
rates when given immediately following tPA for AMI
–
–
–
–
Does this meet the FINER criteria?
What is the rate of ICH following tPA?
Is a 30% reduction meaningful?
What size trial would need to be conducted?
NRMI: BB Use and ICH
Unadjusted ICH Rate (%)
Immediate BB
No Immediate BB
2.5
2
1.5
1
0.5
0
<65
65-74
AGE
75
NRMI: BB Use and ICH
Immediate BB
No Immediate BB
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Male
Female
NRMI and Beta-blocker Use
Multivariate Analysis: Effect of Drug Therapy Administered Within
24 Hours on Intracranial Hemorrhage Rate
Medication
Adjusted OR (95% Cl)
 blocker
0.69 (0.57-0.84)*
ACE inhibitor
0.75 (0.55-1.03)
Calcium channel antagonist
1.27 (0.98-1.64)
Lidocaine
0.93 (0.76-1.13)
Intravenous magnesium
1.05 (0.76-1.45)
Intravenous nitroglycerin
0.86 (0.69-1.09)
*p<0.001.
Cl = confidence intervals; OR = odds ratio; other abbreviation as in Table 1.
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– unethical studies
– sample size is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening oin the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
The Association Between White Blood Cell Count,
Epicardial Blood Flow, Myocardial Perfusion, and Clinical
Outcomes in the Setting of Acute Myocardial Infarction
Hal V. Barron, M.D.; Christopher P. Cannon, M.D.; Sabina A. Murphy, M.P.H.; Susan J.
Marble, M.S., R.N.; Eugene Braunwald, M.D.; and C. Michael Gibson, M.S., M.D.; for
the TIMI 10 Study Group
•
Background: Patients with elevated white blood cell (WBC) counts during
acute myocardial infarction (AMI) have a higher risk of adverse outcomes.
• Objectives: The goal of this study was to determine the relationship between the
WBC count and angiographic characteristics to gain insight into this
pathophysiology of this clinical observation.
• Methods: Angiographic and clinical data from 936 patients in the TIMI 10A and
TIMI 10B trials was used to evaluate these relationships
•
Results : The development of new congestive heart failure was associated with
significantly higher WBC counts (13.3  8.9, n=64 vs 10.8  3.5, p<0.0001), an
observation which remained significant in a multivariable model adjusting for all
potential confounding variables (O.R. 1.2 per 1 unit increase in WBC count,
p<0.001).
Death or CHF
Death
10.4
10
20.9
20
8
15
6
4.9
10
3.8
4
7.8
5-10
10-15
5
2
0
7.9
0
0-5
5-10
10-15
>15
0
0
0-5
>15
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– unethical studies
– sample size is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening oin the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
ICH Risk following t-PA:NRMI 2
12
Adjusted OR
10
8
6
4
2
0
M<65
F<65
M 65-74
.
Gurwitz et al. 1998 Annals Int Med. 129; 597-604
F 65-74
M>75
F>75
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– unethical studies
– sample size is prohibitive
• Examine associations for hypothesis generation
• Examine associations to identify treatment modifiers
• Describe what is happening oin the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
All Patients in NRMI 2
Background
• Initial reperfusion therapy (IRT) is beneficial for patients with
acute myocardial infarction (AMI)
• A minority of patients with AMI receive IRT.
• Underutilization could be related to:
– the absence of clear indications,
– perceived contraindications and
– physicians’ reluctance to prescribe IRT.
Hypothesis
• To determine what percent of patients identified as having clear
indications for initial reperfusion therapy (IRT) do not receive this
life-saving therapy and
• To identify patient subgroups who are at greatest risk for not
receiving IRT.
Methods - Study Population
SymptomsHosp <6 hrs
ST Segment or LBBB
Contraindications to thrombolytic Rx
No IRT
N=20,319
IRT
N=64,344
Reperfusion Therapy
Less Likely
More Likely
LBBB
No CP
Age > 75
Prior CHF
Prior MI
Prior Stroke
Killip 3#
Killip 2#
Prior Angina
Diabetes
Female
Prior Revasc.
Anterior MI*
Prior HTN
Caucasian
Current Smoker
Prehospital ECG
Sx < 3 hrs.
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Underutilizing of IRT In High Risk Patients
OR: In-Hospital
Death
OR:
Receiving IRT
Female Gender
1.47
0.88
History of CHF
1.23
0.35
History of Stroke
1.76
0.66
History of Diabetes
1.12
0.78
Current Smoker
0.68
1.34
Anterior Wall MI
1.76
1.59
Rales On Initial PE
2.06
0.69
Pulmonary Edema on
Initial PE
2.19
0.34
Variable
Conclusions
• At least 31% of patients presenting with AMI are appropriate for IRT
• 1 in 4 patients appropriate for IRT do not receive this life-saving therapy.
• The underutilization is particularly evident in the elderly, women and other
patients at increased risk for in-hospital mortality.
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Sex Differences in Early Mortality After
Myocardial Infarction
Vaccarino et al. N Engl J Med 1999;341:217-25
Background
• Literature is conflicting about whether short-term
mortality after MI is higher in women than in men
after adjusting for age and other prognostic
factors
• Traditional approach: compare all the men and all
the women, adjusting for age and other factors
Specific Aims
• To test the following hypotheses:
1. the mortality of women relative to men is not
constant across ages
2. the younger the age of the patients, the higher
the risk of death in women relative to men
• To identify factors that may account for the higher
mortality rates of women compared with men
Data Source
• Second National Registry of Myocardial Infarction
(NRMI-2)
• 1,658 participating U.S. hospitals
• N=691,995 MI patients enrolled up to 1/31/98
Study Sample
EXCLUSIONS:
•
•
•
•
Age <30 and > 90
Patients transferred from other hospitals
Patients transferred to other hospitals
N for analysis: 384,878
Methods of Analysis
Multiple logistic regression with hospital death as
outcome
1. Traditional analysis approach: main effect of female
sex after adjusting for age
2. Test for sex-age interaction
3. Sequential adjustment for other covariables
RESULTS
Selected Patient Characteristics by Sex
Mean age
History of MI (%)
History of CHF (%)
History of HTN (%)
History of diabetes (%)
Chest pain (%)
ST elevation (%)
CHF or cardiog. shock (%)
Hospital mortality (%)
Women
72
Men
66
24
21
59
33
63
38
27
17
28
13
47
25
72
42
19
11
Factors Disproportionately more Common in Women at Younger Ages
• Demographic factors
– Non-White race
– Medicaid insurance
• Admission data
–
–
–
–
Delay to presentation >6 hrs
No ST elevation
CHF, pulmonary edema
Hypotension or cardiogenic shock
• Medical history
– Hx of CHF
– Hx of diabetes
– Hx of stroke
• Treatments
– No coronary reperfusion therapy
– No use of IV beta-blockers
History of Diabetes
40
35
30
25
Men
Women
% 20
15
10
5
0
30-59
60-69
70-79
Age
80-89
Presentation After 6 hrs from Symptom Onset
45
40
35
30
25
%
20
15
10
5
0
Men
Women
30-59
60-69
70-79
Age
80-89
Hypotension on Admission
6
5
4
Men
Women
%3
2
1
0
30-59
60-69
70-79
Age
80-89
Overall Effect of Female Sex on Mortality (traditional
approach)
Unadjusted
Age adjusted
OR of Mortality
Women Vs. Men
(95% CI)
1.54
1.14
(1.51-1.57)
(1.12-1.17)
Hospital Mortality Rates by Sex and Age (Unadjusted)
Sex-Age Interaction: P<0.001
Hospital Mortality (%)
25
20
15
Men
Women
10
5
0
<50 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89
Age
Effect of Female Sex on Mortality by Age
(Unadjusted)
OR (Women Vs. Men)
3.0
2.5
2.0
Predicted
Actual
1.5
1.0
0.5
30
35
40
45
50
55
60
Age
65
70
75
80
85
90
Impact of Overall Adjustment
3.0
OR (Women
Vs. Men)
2.5
Unadjusted
2.0
1.5
Adjusted
1.0
0.5
30
35
40
45
50
55
60
Age
65
70
75
80
85
90
Summary / Conclusions
• A higher risk of death in women relative to men is seen
in the younger age groups only
• There is a linear increase of risk for women relative to
men going from older to younger age
• The younger the patients’ age, the higher the risk of death
of women relative to men
• Adjustment for covariables explains only 1/3 of the
higher mortality risk for women at younger ages
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Examine associations to identify treatment modifiers
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Trials Comparing Primary PTCA With
Fibrinolytic Therapy: PAMI Cohort
12.0
12
10
8
6.5
6
5.1
4
2.6
2
0
In-hospital death
P=0.06
Grines CL, et al. N Engl J Med. 1993;328:673-679.
In-hospital death or nonfatal
reinfarction
P=0.02
PTCA
t-PA
Composite Outcome (%)
Trials Comparing Primary PTCA With
Fibrinolytic Therapy: GUSTO-IIb Cohort
18
16
14
12
10
8
6
4
2
0
16.1
13.7
14.1
PTCA
t-PA
9.6
30 days
6 months
P=0.033
P=NS
GUSTO-IIb Angioplasty Substudy Investigators. N Engl J Med. 1997;336:1621-1628.
Meta-analysis of Mortality Benefit With Primary
PTCA Versus Fibrinolytic Therapy
Rate %
Study
Group
PTCA
Lytic
Therapy
Odds Ratio
(95% CI)
Absolute Risk
Reduction, %
(95% CI)
4.0
5.9
0.66 (0.29 to1.50)
1.9 (-2.7 to 4.1)
3- to 4-hour t-PA 3.5
5.7
0.60 (0.24 to1.41)
2.2 (-2.2 to 4.3)
Accelerated
t-PA
5.0
7.2
0.68 (0.42 to 1.08) 2.2 (-0.5 to 4.0)
Total
4.4
6.5
0.66 (0.46 to 0.94)
Streptokinase
Weaver WD, et al. JAMA. 1997;678:2093-2098.
2.1 (0.4 to 3.4)
Trials Comparing Primary PTCA With
Fibrinolytic Therapy: MITI Cohort
100
Primary Angioplasty
Fibrinolytic Therapy
Survival (%)
95
90
85
80
75
P=NS
70
0
0.5
1
1.5
2
2.5
3
Time After Discharge (years)
Every NR, et al. N Engl J Med. 1996;335:1253-1260.
3.5
4
PPTCA versus tPA :NRMI 2
• 4,939 nontransfer pts underwent PPTCA within 12 hrs from Sx
onset
• 24,705 pts received tPA
• Lytic ineligable and shock pts were excluded
Randomized Trial Results Versus Community-Setting
Results: NRMI-2 Cohort
n=2958, lytic eligible, no shock at presentation
Percent
5.2
PTCA
5.4
t-PA
5.6
6.2
8
6
4
2
0
In-hospital mortality
P=NS
Tiefenbrunn AJ, et al. J Am Coll Cardiol. 1998;31:1240-1245.
In-hospital mortality or
nonfatal stroke
P=NS
Mortality (%)
rt-PA
PTCA
Overall
STE or LBBB 1st ECG
5.4
5.3
5.2
5.5
3.4
16.5
3.5
14.4
Male
Female
4.5
9.6
5.2
8.9
Inferior MI
Anterior MI
3.9
7.6
3.9
7.1
Low Risk
Not Low Risk
2.9
7.5
2.8
7.4
Age < 75 yr.
Age > 75 yr.
Odds Ratio and 95% CI
0.5
rt-PA better
1.0
1.5
PTCA better
PPTCA versus tPA
(Death and Nonfatal Stroke)
t-PA
20
PPTCA
18.4
18
16
14.6
14
12
10
8
6
6.2
5.6
6.1
5.9
4.1
4
3.9
2
0
All
STE or LBBB
Age >75
Age <75
Efficacy vs Effectiveness
• Why might they differ?
Importance of Door-to-Balloon Time:
30-Day Mortality in the GUSTO-IIb Cohort
25
14.1
P=0.001
Mortality (%)
20
15
6.4
10
5
0
3.7
4
1
< 60
61-75
76-90
>91
Door-to-Balloon Time (minutes)
Berger PB, et al. Circulation. 1999;100:14-20.
PTCA not
performed
Treatment effect modifiers
Death during Hospitalization (%)
Thrombolytic Therapy
8
7
6
5
4
3
2
1
0
5.85
6.21
Primary Angioplasty
5.87
5.37
4.46
3.37
Low Volume
Medium Volume
High Volume
Hospital-specific primary angioplasty volume category
Rates of Death during Hospitalization for Myocardial Infarction among patients treated with thrombolytic therapy
versus primary angioplasty. The interaction between reperfusion strategy and primary angioplasty volume was
significant (p<.01).
Overview
Review what we can learn from observational data
• Examine associations and attempt to speculate on causality
when RCTs are not feasible
– when RCTs are unethical (Does smoking really cause cancer?)
– when the sample size needed for a RCT is prohibitive
• Examine associations for hypothesis generation
• Describe what is happening in the “real world”
– Safety surveillance : Identification of rare events or subgroup
analysis
– Drug utilization patterns
– Natural history of disease
– Efficacy vs Effectiveness
Conclusions
• Observational research studies can be very valuable
– They provide information not obtainable from RCTs
– They provide important information when RCTs are not
feasible
• Observational research studies can be very
misleading as well
– They can never really clarify causality (only
associations)
– Measured and especially unmeasured confounders can
be a VERY BIG problem!-more to come on this