Download Table 1: Baseline and presenting characteristics

Online Appendix for the following JACC article
TITLE: Changes in Myocardial Infarction Guideline Adherence as a Function of Patient Risk: An End to Paradoxical Care?
AUTHORS: Apurva A. Motivala, MD, Christopher P. Cannon, MD, Vankeepuram S. Srinivas, MBBS, MS, David Dai, MSc, Adrian F. Hernandez, MD, MPH, Eric D. Peterson,
MD, MPH, Deepak L. Bhatt, MD, MPH, Gregg C. Fonarow, MD
___________________________________________________________
APPENDIX
Funding: The Get With The Guidelines Program (GWTG) is funded by the American Heart Association (AHA). The program is also supported in part by unrestricted educational
grants to the AHA by Pfizer, Inc., New York, NY, and the Merck-Schering Plough Partnership (North Wales, PA), who did not participate in the design, analysis, manuscript
preparation or approval.
Disclosures: Apurva A. Motivala: none; Christopher P. Cannon: Research grants/support: Accumetrics, AstraZeneca, Bristol-Myers Squibb/Sanofi Partnership,
GlaxoSmithKline, Intekrin Therapeutics, Merck, Novartis, Takeda ;Advisory Board (but funds donated to charity)-Bristol-Myers Squibb/Sanofi Partnership; Clinical Advisorequity in Automedics Medical Systems; Vankeepuram S. Srinivas: none; David S. Dai: none; Eric Peterson: Bristol Myers Squibb-Sanofi, Merck-Schering Plough; Adrian F.
Hernandez: Merck, Johnson & Johnson, AstraZeneca; Deepak L. Bhatt: Research grants from Astra Zeneca, Bristol-Myers Squibb, Eisai, Sanofi Aventis, The Medicines
Company; Gregg C. Fonarow: Bristol Myers Squibb-Sanofi, Merck-Schering Plough, Pfizer, AstraZeneca
STUDY METHODS:
Data Source: The GWTG-CAD program is a large, multicenter, observational registry started in 2000 to support and facilitate improvement of the quality of care for
patients with cardiovascular disease. The GWTG Program uses a web-based patient management tool (PMT, Outcome Sciences Inc., Cambridge, MA) to collect clinical data and
provide decision support with real-time online reporting features. The GWTG-CAD program enrolls patients hospitalized with a confirmed diagnosis of CAD (ICD-9 codes 410414 included). Trained data abstractors at participating hospitals in GWTG-CAD collected detailed information on baseline demographic and clinical characteristics, in-hospital
care processes and outcomes, and discharge treatment using a standardized set of data elements and definitions. Using an internet-based system, data quality was monitored to
assure the completeness and accuracy of the submitted data. Outcome Sciences, Inc. serves as the data collection and coordination center for GWTG. As collected data are used
primarily used for institutional quality improvement and de-identified patient information was collected anonymously through retrospective chart review, individual informed
consent was not required under the common rule. Participation in GWTG required approval of the institutional review board of each hospital. The Duke Clinical Research Institute
serves as the data analysis center and has an agreement and Institutional Review Board approval to analyze the aggregate de-identified data for research purposes.
Study patients: The study sample was drawn from 312,278 patients enrolled in the Get With The Guidelines-Coronary Artery (GWTG-CAD) database across 279
participating sites between January 2, 2000 and December 30, 2008 (after excluding sites with less than 30 admissions). Out of these, 119,948 patients with a discharge diagnosis
of MI (with and without ST-segment elevations) were analyzed. Myocardial infarction was defined as presentation with symptoms of ischemia in association with positive cardiac
enzymes. After excluding another 7100 patients who were transfer-outs, a total of 112,848 patients were analyzed (Figure 1). Of patients and hospitals included in the study past
medical history data was complete in 82.1% of patients (83.1% for low risk group, 79.2% for intermediate risk group, and 83.9% for high risk group). We evaluated treatment rates
among eligible patients only with contraindications to therapies determined and documented by their treating clinicians.
Development/validation of a risk stratification model: After further excluding patients with missing gender or death information (3224 patients), a developmental
sample consisting of 60% randomly selected MI patients from the study sample (109,624 patients) was used to create a risk score to divide the population into risk groups. After
selecting a relevant list of risk factors (age, sex, race, body mass index, ST segment elevation MI, history of smoking, diabetes, hypertension, hyperlipidemia, renal insufficiency,
hemodialysis, heart failure, stroke, peripheral vascular disease, prior myocardial infarction (MI), COPD, chronic depression, atrial fibrillation/flutter) based on prior models and
clinical insight, we used univariate as well as multivariate, logistic regression analysis to identify factors independently associated with in-hospital mortality and a risk model was
devised (Appendix 3). Model discrimination was assessed using C statistics. This risk stratification model was validated in the remaining 40% of MI patients in our cohort and had
a discrimination C-statistic of 0.75.
Statistical Analysis: Using this validated risk score, patients in our study sample were stratified into tertiles: low (0-3%), intermediate (3-6.5%), and high (>6.5%) inhospital mortality rates. Their baseline and presenting characteristics, in-hospital and discharge therapies were compared. Categorical variables are expressed as frequencies and
percentages and were compared between groups using Pearson’s Chi-square test. Continuous variables are expressed as means and were compared between groups using KruskalWallis test. In examining the association between the risk and measure outcomes, a multivariable logistic regression was used to estimate the marginal effects of risk. The
Generalized Estimating Equation (GEE) method with exchangeable working correlation structure was used to account for within-hospital clustering, because patients at the same
hospital are more likely to have similar responses relative to patients in other hospitals (i.e. within-center correlation for response). The method produces estimates similar to those
from ordinary logistic regression, but the estimated variances of the estimates are adjusted for the correlation of outcomes within each hospital. The GEE method was also used to
examine the trend effect among each risk group. Furthermore, we evaluated if any trends observed were due to better documentation of contra-indications of therapies (as
determined by the treating clinicians) over time. A p-value of <0.05 was considered statistically significant for all tests. All analyses were performed using SAS software version
9.1 (SAS Institute, Cary, NC).
STUDY LIMITATIONS
Although our sample consists of a large number of medical centers all over the US, participation in GWTG is purely voluntary and thus these findings may not be reflective
of centers that are not included in the quality initiative. Data were collected by medical chart review and depend upon the accuracy and completeness of documentation. As such, a
proportion of patients reported to be eligible for treatment who did not receive recommended therapies may have had contraindications or intolerance to specific interventions that
were present but not documented. Also, the prevalence in the use of objective risk scores was not documented. Counseling regarding lifestyle interventions may have been
provided but not recorded in the medical record. The improvements in performance and quality measures over time may have been influenced by factors other than GWTG-CAD
participation such as secular trends. As GWTG does not collect data on post-discharge treatment or outcomes, the full implications of these improvements in process measure
treatment rates for patients at low, intermediate and higher risk over time could not be directly explored.
Table 1: Baseline and presenting characteristics
LOW RISK
INTERMEDIATE
HIGH RISK
RISK
P-value
(<3%)
(3%-6.5%)
(>6.5%)
N=36,541
N=36,542
N=36,541
52±9 yrs
65±10 yrs
79±9 yrs
<0.0001
Female gender
8747 (24%)
12476 (34%)
18894 (52%)
<0.0001
Caucasians
27692 (76%)
28212 (77%)
28815 (79%)
<0.0001
Blacks
3426 (9%)
2705 (7%)
2159 (6%)
<0.0001
Hispanics
2188 (6%)
2176 (6%)
1869 (5%)
n/a
Asians
805 (2%)
995 (3%)
1424 (4%)
n/a
28±3
27±3
26±4
<0.0001
21730 (60%)
11525 (32%)
3820 (11%)
<0.0001
Diabetes Mellitus-insulin treated
1523 (5%)
2129 (7%)
2532 (8%)
<0.0001
Diabetes Mellitus-non-insulin
3793 (13%)
3819 (13%)
3327 (11%)
<0.0001
Hypertension
19200 (63%)
18843 (65%)
21119 (69%)
<0.0001
Hyperlipidemia
18917 (62%)
13830 (48%)
9361 (31%)
<0.0001
Chronic renal insufficiency
333 (1%)
1272 (4%)
5511 (18%)
<0.0001
ESRD on hemodialysis
37 (0.1%)
268 (1%)
1274 (4%)
<0.0001
Age (mean±SD)
BMI (mean±SD)
Smoking
Prior MI
5241 (17%)
5200 (18%)
6687 (22%)
<0.0001
CVA/TIA
544 (2%)
1670 (6%)
5282 (17%)
<0.0001
Peripheral Vascular Disease
835 (3%)
2030 (7%)
4309 (14%)
<0.0001
COPD/Asthma
1891 (6%)
3590 (12%)
6517 (21%)
<0.0001
Heart Failure
846 (3%)
2419 (8%)
8232 (27%)
<0.0001
Anemia
358 (1%)
776 (3%)
1909 (6%)
<0.0001
Prior PCI
834 (3%)
705 (2%)
490 (2%)
<0.0001
Prior Bypass Surgery (CABG)
402 (1%)
571 (2%)
544 (2%)
<0.0001
STEMI
7315 (20%)
8990 (25%)
8116 (22%)
n/a
NSTEMI
20335 (56%)
16358 (45%)
14966 (41%)
n/a
HR (mean±SD;bpm)
80±19
82±22
86±23
<0.0001
SBP (mean±SD;mmHg)
138±28
137±29
135±31
<0.0001
LDL (mean±SD;mg/dL)
113±42
103±39
94±35
<0.0001
HDL (mean±SD;mg/dL)
36±11
39±12
41±14
<0.0001
183±147
147±108
120±82
<0.0001
HbA1C
8±2
8±2
7±1.5
<0.0001
Ejection Fraction (mean)
50%
47%
44%
<0.0001
TG (mean±SD;mg/dL)
All results in expressed as total (percentage) unless stated otherwise
SD: Standard Deviation
Table 2: Management and Outcomes
LOW RISK
INTERMEDIATE HIGH RISK
RISK
P-value
(>6.5%)
(<3%)
(3%-6.5%)
N=36,541
N=36,542
Per-cutaneous intervention (in-hospital)
22983 (71%)
19988 (64%)
13926 (47%)
<0.0001
Coronary Artery Bypass Graft (in-hospital)
3385 (10%)
3582 (11%)
2431 (8%)
<0.0001
3
4
5
<0.0001
34734 (95%)
31746 (87%)
23339 (64%)
<0.0001
Discharged to skilled nursing
686 (2%)
2344 (6%)
7064 (19%)
<0.0001
Death
500 (1%)
1473 (4%)
4132 (11%)
<0.0001
Length of stay (median days)
Discharged home
All results in expressed as total (percentage) unless stated otherwise
SD: Standard Deviation
N=36,541
Table 3: Performance Measures (among eligible patients only)
LOW RISK
INTERMEDIATE
HIGH RISK
RISK
(<3%)
(3%-6.5%)
P-value
(>6.5%)
MI/angina patients receiving
aspirin within 24 hrs of presentation
Patients discharged on aspirin
Patients with LDL>100 who receive lipid
lowering agents
Patients discharged on β-blockers
N=36,541
N=36,542
N=36,541
21852/22440
21996/22800
22830/24277
(98%)
(97%)
(94%)
33424/34193
31584/32444
26849/28094
(98%)
(97%)
(96%)
11797/12432
8921/9602 (93%)
5256/6153
(95%)
30823/31839
26705/28117
(97%)
(97%)
(95%)
Patients with documented LV systolic
4752/5202
5840/6546 (89%)
6303/7656
dysfunction discharged on ACEi/ARBs
(91%)
Current smokers who receive smoking
20253/21099
10139/10929
2858/3322
(96%)
(93%)
(86%)
32537/36019
31450/35453
28458/33958
(90%)
(89%)
(84%)
Composite Performance measure for 100%
compliance
<0.0001
<0.0001
(85%)
32198/33166
cessation advice
<0.0001
<0.0001
<0.0001
(82%)
<0.0001
<0.0001
All results in expressed as total (percentage) unless stated otherwise
LDL: Low density lipoprotein, LV: Left ventricle, ACEi: Angiotensin Converting Enzyme inhibitors
ARBs: Angiotensin Receptor Blockers
Table 4: Quality Measures (among eligible patients only)
LOW RISK
INTERMEDIATE
HIGH RISK
RISK
P-value
(<3%)
(3%-6.5%)
(>6.5%)
N=36,541
N=36,542
N=36,541
MI pts who receive
18796/20280
18198/20268
18174/20977
-blockers≤24 hrs
(93%)
(90%)
(87%)
85/138 mins
84/140 mins
92/156 mins
<0.0001
37/54 mins
39/55 mins
44/60 mins
0.004
Door-PCI time for STEMI/LBBB
<0.0001
(N=17,787) (median/mean)
Door-tPA time for STEMI/LBBB
(N=2143) (median/mean)
Patients with a recorded LDL
Patients with last recorded BP <140/90 mm Hg
Patients who receive statins or lipid lowering agents
Patients discharged on ACEi/ARBs
Patients that received cardiac rehabilitation referral or
physical activity recommendations
Overweight patients that receive wt. management and
physical activity recommendations
Diabetic treatment amongst DM patients
Diabetic teaching amongst DM patients
26694/35594
25095/34657
20149/31439
(75%)
(72%)
(64%)
23593/27667
21321/25896
18917/24222
(85%)
(82%)
(78%)
32323/34842
30354/33760
24022/30247
(93%)
(90%)
(79%)
25735/33258
24694/31715
20396/27677
(77%)
(78%)
(74%)
28441/35594
26136/34657
22574/31439
(80%)
(75%)
(72%)
21022/24033
16978/20008
11217/13891
(88%)
(85%)
(81%)
4697/5191
4844/5561
4147/5034
(91%)
(87%)
(82%)
495/5191
528/5561
367/5034
(10%)
(10%)
(7%)
All results in expressed as total (percentage) unless stated otherwise
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
LBBB: Left bundle branch block, LDL: Low density lipoprotein, ACEi: Angiotensin Converting Enzyme inhibitors
ARBs: Angiotensin Receptor Blockers
Table 5: Temporal trends for improvement among each category
Unadjusted
Adjusted
Outcome
Category
O.R.
Lower CI
Upper CI
AMI patients without contraindications that receive ASA<24 hrs
(High Risk per Year Increase)
1.14
1.07
1.21
p Value
<.0001
O.R.
1.22
Lower CI
1.11
Upper CI
1.33
P Value
<.0001
Unadjusted
Outcome
Defect-free measure
Current smokers that receive smoking cessation advice
Patients discharged on Beta Blockers
Patients discharged on aspirin
Patients with LDL>100 who receive lipid lowering drugs
Patients with documented LVSD discharged on ACE Inhibitors or ARB
Opportunity Composite Measure
Adjusted
Category
O.R.
Lower CI
Upper CI
p Value
O.R.
Lower CI
Upper CI
P Value
(Intermediate Risk per Year Increase)
1.13
1.05
1.22
0.0009
1.21
1.11
1.31
<.0001
(Low Risk per Year Increase)
1.20
1.07
1.35
0.002
1.30
1.14
1.47
<.0001
(High Risk per Year Increase)
1.28
1.22
1.34
<.0001
1.32
1.25
1.40
<.0001
(Intermediate Risk per Year Increase)
1.29
1.21
1.37
<.0001
1.33
1.25
1.42
<.0001
(Low Risk per Year Increase)
1.31
1.21
1.42
<.0001
1.37
1.26
1.49
<.0001
(High Risk per Year Increase)
1.54
1.40
1.69
<.0001
1.62
1.45
1.81
<.0001
(Intermediate Risk per Year Increase)
1.61
1.44
1.82
<.0001
1.70
1.49
1.94
<.0001
(Low Risk per Year Increase)
1.64
1.44
1.87
<.0001
1.76
1.53
2.03
<.0001
(High Risk per Year Increase)
1.17
1.08
1.27
0.0003
1.33
1.19
1.48
<.0001
(Intermediate Risk per Year Increase)
1.14
1.03
1.26
0.01
1.30
1.15
1.47
<.0001
(Low Risk per Year Increase)
1.07
0.93
1.22
0.36
1.23
1.04
1.45
0.018
(High Risk per Year Increase)
1.10
1.00
1.22
0.05
1.34
1.21
1.48
<.0001
(Intermediate Risk per Year Increase)
1.06
0.92
1.21
0.43
1.28
1.10
1.49
0.0012
(Low Risk per Year Increase)
1.02
0.84
1.25
0.82
1.26
1.00
1.59
0.05
(High Risk per Year Increase)
1.12
1.07
1.16
<.0001
1.22
1.15
1.30
<.0001
(Intermediate Risk per Year Increase)
1.08
1.04
1.13
0.0003
1.18
1.12
1.25
<.0001
(Low Risk per Year Increase)
1.09
1.05
1.14
<.0001
1.25
1.18
1.33
<.0001
(High Risk per Year Increase)
1.35
1.28
1.41
<.0001
1.29
1.21
1.37
<.0001
(Intermediate Risk per Year Increase)
1.33
1.26
1.41
<.0001
1.28
1.19
1.37
<.0001
(Low Risk per Year Increase)
1.33
1.25
1.43
<.0001
1.28
1.18
1.39
<.0001
(High Risk per Year Increase)
1.24
1.18
1.30
<.0001
1.30
1.23
1.37
<.0001
(Intermediate Risk per Year Increase)
1.24
1.16
1.32
<.0001
1.30
1.21
1.39
<.0001
Unadjusted
Outcome
Adjusted
Category
O.R.
Lower CI
Upper CI
(Low Risk per Year Increase)
1.25
1.15
1.37
p Value
<.0001
O.R.
1.33
Lower CI
1.22
Upper CI
1.45
P Value
<.0001
In examining the association between the risk groups and the outcomes, a multivariable logistic regression was used to estimate the marginal effects of the risk groups. The Generalized Estimating Equation (GEE) method with exchangeable working correlation
structure was used to account for within-hospital clustering, because patients at the same hospital are more likely to have similar responses relative to patients in other hospitals (i.e. within-center correlation for response). The method produces estimates similar to those
from ordinary logistic regression, but the estimated variances of the estimates are adjusted for the correlation of outcomes within each hospital.
As the patient characteristics such as age, gender, race, BMI, and medical histories have been used to classify the different risk groups, we do not include them as the adjusted variables here. The adjusted variables are the hospital characteristics (bed size, region, heart
transplants, academic, interventional, residents, CT surgery on site, primary PTCA performed for AMI).
Opportunity Composite Measure: Calculated as the total number of successes across all patients, divided by the number of opportunities for all performance measures for which they are eligible.
Table 6: Temporal trends in documentation of contra-indications to therapies
Low risk group:
Admit year ACEi/ARB Aspirin Beta blocker Lipid Lowering
agents
2002
0%
2.04%
3.88%
0%
2003
0%
2.24%
5.34%
0.41%
2004
0.26%
1.93%
6.09%
0.37%
2005
2.47%
3.3%
5.16%
1.22%
2006
7.85%
4.19%
6.42%
2.16%
2007
9.77%
4.25%
7.03%
2.2%
2008
10.89%
4.61%
8.27%
2.72%
Intermediate risk group:
Admit year ACEi/ARB Aspirin Beta blocker Lipid Lowering
agents
2002
0%
2.08%
3.99%
0.98%
2003
0%
3.39%
6.44%
0.58%
2004
0%
3.06%
7.57%
0.60%
2005
5.71%
3.81%
6.28%
1.06%
2006
11.22%
6.88%
7.83%
2.78%
2007
14.80%
7.41%
8.81%
3.69%
2008
19.06%
8.59%
8.85%
4.38%
High risk group:
Admit year ACEi/ARB Aspirin Beta blocker Lipid Lowering
agents
2002
0%
5.12%
7.48%
1.07%
2003
0%
7.39%
10.03%
0.68%
2004
0.81%
8.22%
10.07%
1.79%
2005
8.4%
7.45%
9.97%
1.56%
2006
16.75%
11.52%
9.92%
4.02%
2007
23.52%
12.35%
11.05%
6.25%
2008
27.63%
13.33%
12.10%
7.16%