Trends in the Use of Evidence-Based Treatments for Coronary Artery Disease Among Women and the Elderly
Findings From the Get With the Guidelines Quality-Improvement Program
Background— Significant disparities have been reported in the application of evidence-based guidelines in the treatment of coronary artery disease (CAD) in women and the elderly. We hypothesized that participation in a quality-improvement program could improve care for all patients and thus narrow treatment gaps over time.
Methods and Results— Treatment of 237 225 patients hospitalized with CAD was evaluated in the Get With the Guidelines–CAD program from 2002 to 2007. Six quality measures were evaluated in eligible patients without contraindications: aspirin on admission and discharge, β-blockers use at discharge, angiotensin-converting enzyme inhibitor or angiotensin receptor antagonist use, lipid-lowering medication use, and tobacco cessation counseling along with other care metrics. Over time, composite adherence on these 6 measures increased from 86.5% to 97.4% (+10.9%) in men and 84.8% to 96.2% (+11.4%) in women. There was a slight difference in composite adherence by sex that remained significant over time (P<0.0001), but this was confined to patients <75 years. Composite adherence in younger patients (<75 years) increased from 87.1% to 97.7% (+10.6%) and from 83.0% to 95.1% (+12.1%) in the elderly (≥75 years) over time.
Conclusions— Among hospitals participating in Get With the Guidelines–CAD, guideline adherence has improved substantially over time for both women and men and younger and older CAD patients, with only slight age and sex differences in some measures persisting.
Received October 13, 2008; accepted August 28, 2009.
The American Heart Association (AHA) and the American College of Cardiology (ACC) have developed guidelines for the treatment of patients with coronary artery disease (CAD).1,2 Significant opportunities for improvement in adherence to evidence-based guidelines exist. It is clear, however, that in many care settings evidence-based therapies for CAD are applied less frequently in women compared to men.3 Similarly, guideline adherence is significantly decreased in elderly patients compared to younger ones.4–6 Although the reasons for these age- and sex-related differences in treatment are multi-factorial, significant opportunities to improve the application of evidence-based therapies exist. The Get With the Guidelines (GWTG) program is the largest hospital-based national performance initiative. Guideline adherence in this program improved over a 1-year period for hospitals participating in this program.7 Because quality-improvement programs have been associated with increases in guideline adherence, we hypothesized that participation in GWTG-CAD could improve the consistent use of evidence-based treatments in all patients, and thus lead to a narrowing of past treatment gaps between men and women and younger and older patients over a 5-year period.
WHAT IS KNOWN
The elderly receive evidence-based treatments for coronary artery disease less frequently than younger counterparts.
Younger women receive evidence-based treatments for coronary artery disease less frequently than younger male counterparts.
Quality-improvement programs increase adherence to evidence-based guidelines in the treatment of coronary artery disease.
WHAT THE STUDY ADDS
The authors suggest that quality-improvement programs can improve adherence to guidelines among women and the elderly nearly eliminating treatment gaps.
The authors offer new information about the opportunities for improvement in adherence that are larger with measures such as lipid lowering therapy, angiotensin-converting enzyme inhibitor treatment, and tobacco cessation counseling compared to other individual guidelines such as aspirin and β-blocker treatment.
GWTG Program Components
The AHA launched the GWTG Program to support and facilitate the improvement in the quality of care of patients with cardiovascular disease. The GWTG-CAD program includes learning sessions, didactic sessions, best practice sharing, interactive workshops, postmeeting follow-up, and a web-based Patient Management Tool (Outcome).8 This web-based tool provides the opportunity for concurrent data collection, ongoing real-time feedback of hospital data, and clinical decision support to enable rapid cycle improvement. As an incentive, GWTG-CAD rewards hospitals using a performance recognition program. The program began in 2000. The length of participation of each hospital depended on the time it entered the program. Baseline data included the first 30 admissions. This was the entry point into the study. Subsequently, time of participation was calculated in 3-month intervals (quarters). Quarters with less than 1000 admissions were excluded to obtain stable and reliable estimates on trends over time (this excluded data obtained in all 4 quarters of 2000 and 2001). Therefore, all GWTG-CAD participating hospitals enrolled from January 1, 2002, to December 31, 2007, were included in the analysis. As GWTG-CAD is a quality-improvement program, hospitals are encouraged to consecutively enroll all eligible patients. The population included patients admitted to the hospital who were entered into the Patient Management Tool with discharge diagnoses of acute myocardial infarction, unstable angina, chronic stable angina, and ischemic heart disease (International Classification of Diseases, 9th revision, diagnoses 410 to 414). Records with missing sex data were excluded (4% of the entire population). Data were collected concurrently or by chart review and included patient demographics, medical history, symptoms on arrival, results of laboratory testing, in-hospital treatment and events, discharge treatment and counseling, and patient disposition. Data entry was performed by highly trained abstracters and clinical personnel using standard data definitions and coding instructions. 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.
Guideline Adherence Measurement
Six guideline adherence measures of hospital performance were included in the analysis. Each measure was a Class I ACC/AHA guideline recommendation.1,2 These measures were selected by the GWTG-CAD program for hospital recognition and are applicable to the broadest group of patients hospitalized with CAD. All measures were defined as the percentage of eligible patients without contraindications, intolerance, or other documented medical or patient related reasons for not prescribing the intervention. These included 5 discharge measures: prescription of aspirin, β-blockers, angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers for all patients with acute myocardial infarction (AMI) and left ventricular dysfunction, and prescription of lipid-lowering therapy for patients with a low-density lipoprotein cholesterol level of ≥100 mg/dL, and smoking cessation counseling. Additionally, 1 admission treatment measure was included: aspirin treatment within 24 hours of admission. These 6 measures were then combined into a composite performance measure (CPM) which was calculated as the number of interventions administered to eligible patients divided by the number of possible opportunities for treatment in eligible patients. Adherence to the CPM was aggregated for all patients enrolled by any hospital and was indexed according to the duration of hospital participation.
The data were analyzed in 2 separate dichotomized groups: age, less than 75 years versus greater than or equal to 75 years; and sex, men versus women. Distinct from the composite performance measure, we calculated a patient centered “all or none”9 measure reflecting the number of patients receiving all interventions for which they were eligible divided by the total number of patients expressed as a percentage. Although patients were included in the adherence analysis only if they were eligible for a given measure, patients with AMI were analyzed and reported separately along with additional care metrics. Deceased patients were included in the demographic analysis and were included in the adherence analysis if they were eligible for a given treatment. Thus, deceased patients were excluded from discharge measures.
The disparity in adherence based on sex and age were evaluated over time to see whether such gaps would decrease with increasing participation in the program. Univariate adherence over time by rolling quarters comparing men versus women and younger versus older patients was performed using Cochran-Mantel-Haenszel testing for nonzero correlation, using modified Ridit scores, controlling for site. Logistic multivariable regression models were fit to compare hospital performance on older versus younger patients and women versus men over time. For the CPM, each opportunity of receiving a guideline-based measure (up to 6 measures for each patient) contributed an observation. Because younger men and women and older men and women may have other baseline differences in their characteristics, confounders were adjusted for in the regression to be able to explore the pure age/sex effects on the hospital performance. Confounders adjusted for in the model included race (white versus other), body mass index, insurance status, admission year, type of AMI diagnosis, left ventricular ejection fraction, medical histories (diabetes, heart failure, hypertension, hyperlipidemia, previous MI, peripheral vascular disease, renal insufficiency, stroke, smoking, pulmonary disease, prior percutaneous coronary intervention [PCI]/coronary artery bypass grafting [CABG]). In addition, because individual hospital characteristics may influence age/sex treatment gaps, hospital disparities were also adjusted for, including bed size, heart transplants, teaching status, region of the country, the presence of on-site CABG, and the presence of on-site PCI. In general, because of secular trends in improved adherence over time, hospitals that began participating at a later time would be expected to perform better than those started earlier. This situation was also considered in our model by adding the calendar quarter in as a covariate. Model components are listed in the online-only Data Supplement. The interaction terms were excluded from the final model with probability value >0.1. Generalized estimating equation method was applied to provide valid inference accounting for the within site correlation. Missing data were accounted for as follows: for categorical variables the most frequent category was imputed, and for continuous variables the mean from the same group was imputed. The treatment gap was expressed as the odds ratio of adherence between 2 groups to either the CPM or “all or none” measures. Improvement over time was expressed as the odds ratio for improvement, which is the ratio of the odds of adherence (CPM) at the current time compared with 1 year earlier. All statistical analyses were performed at the Duke Clinical Research Institute (DCRI) using SAS software (version 9.2, SAS Institute Inc). Because the data were deidentified, the institutional review board from MetroHealth Medical Center and DCRI granted a waiver of full institutional review board review. The authors had full access to the data and take full responsibility for its integrity. All authors have read and agreed to the manuscript as written.
The number of hospitals enrolling patients between January 1, 2002, and December 31, 2007, was 472. The mean bed size was 310 beds (interquartile range [IQR], 155 to 406). Forty-nine percent were teaching hospitals. Most hospitals provided on-site PCI (78.5%) and CABG (62.4%) but few were heart transplant hospitals (9.5%). All geographic regions of the United States were represented with 19.8% of hospitals in the northeast, 18.5% in the midwest, 38.3% in the south, and 23.4% in the west.
There were 237 225 patients enrolled in the study. Men comprised 63.1% of the patients, and 68.1% were under the age of 75. The mean age was 66.4 (±13.9) years. Most patients presented with AMI (78.6%). Six percent presented with unstable angina, and 15.4% had coronary artery disease without unstable angina or AMI. The demographic characteristics categorized by age (<75, ≥75 years) and sex are listed in Table 1. In general, younger patients were more likely to carry a diagnosis of hyperlipidemia and have a higher body mass index. Younger patients were also more likely to be smokers. Younger women were significantly more likely to be diabetic compared with younger men (40.5 versus 30.4%, P<0.0001). Older patients were more likely to have atrial fibrillation, hypertension, heart failure, and a prior myocardial infarction or stroke. Women were significantly older than men (70.5±13.8 versus 64.0±13.4, P<0.0001). Women were more likely to have hypertension and heart failure. Men were more likely to be smokers compared to women. Hospital length of stay was 6.3 days (IQR, 3.0 to 8.0) in older patients compared to 4.9 (IQR, 2 to 6) days in younger patients (P<0.0001). Hospital length of stay was 5.8 days (IQR, 2.0 to 7.0) in women compared with 5.2 (IQR, 2 to 6) days in men (P<0.0001). In-hospital mortality was 9.3% in older patients and 2.7% in younger patients (P<0.0001). In-hospital mortality was 6.2% in women and 4.0% in men (P<0.0001). Outcomes for younger and older male and females patients are shown in Table 1.
Younger patients and men, compared with older patients and women, were slightly more likely to be treated with each of the 6 evidence-based therapies. Adherence to the overall Composite Performance Measure was modestly higher in younger patients compared with older patients (92.5% versus 89.6%, P<0.0001). Also, adherence to the overall Composite Performance Measure was slightly higher in men compared with women (92.2% versus 90.7%, P<0.0001). The comparisons for guideline adherence for the 4 patient age/sex groupings are shown in Table 2. The absolute differences in conformity in individual measures ranged from as small as 0.1% for tobacco cessation counseling to as large as large 10.7% for treatment with lipid-lowering medications at discharge. The small sex-related difference in adherence to the CPM measure was confined to younger women compared with younger men (91.3±20.0 versus 92.8±17.9, P<0.001), whereas among those ≥75 years, there was similar composite measure conformity for older men and older women. Figure 1 shows unadjusted adherence to the CPM categorized by age and sex. Each group of patients had substantial improvement in Composite Performance Measure adherence over time (P<0.0001 for younger men, younger women, older men, and older women). Over time, composite adherence on these 6 measures increased from 86.5% to 97.4% (+10.9%) in men and 84.8% to 96.2% (+11.4%) in women. Composite adherence in younger patients (<75 years) increased from 87.1% to 97.7% (+10.6%) and from 83.0% to 95.1% (+12.1%) in the elderly (≥75 years). The gap in adherence between the groups was narrowed, but slight differences remained. The percentage of older patients who were treated with each of the indicated evidence-based therapies (“all or none” performance measure) was 77.8%, compared with younger patients who received all therapies 81.2% of the time (P<0.001). Additionally, men were more likely to receive all of the evidence-based therapies (“all or none” performance measure) compared with women (81.0 versus 78.6%, P<0.0001). Both men and women aged ≥75 years receive similar care quality as indexed by the all or none measures. A significant unadjusted age–sex interaction (P<0.001) was found and implies different sex disparities in younger and older patients. Figure 2 shows that each group of patients had significant improvement in “all or none” measure over time (P<0.0001 for younger men, younger women, older men, and older women).
Conformity with guideline recommended care metrics for the subgroup of patients hospitalized with AMI were also analyzed and shown in Table 3. Younger AMI patients were more likely to receive aspirin and β-blockers on admission. Younger and male patients without documented contraindications were more likely to undergo acute reperfusion therapy, PCI, CABG, and be enrolled in cardiac rehabilitation. Whereas older women underwent cardiac catheterization less often than older men, younger men and women had this procedure performed at nearly identical rates. A higher percentage of younger men (52.6%) had door to balloon times of 90 minutes or less compared to younger women (46.8%) and older men (43%). The significant unadjusted age-sex interaction (P<0.001) implies different sex disparities in younger and older patients.
The odds ratio (OR) for improvement per year in the composite measure, after adjusting for potential confounding variables and factoring in the correlation of data within each participating sites, demonstrated similar rates of improvement for all 4 patient groups: younger men, 1.33 (95% CI, 1.21 to 1.47); younger women, 1.32 (95% CI, 1.20 to 1.46); older men, 1.33 (95% CI, 1.22 to 1.46); and older women, 1.31 (95% CI, 1.19 to 1.43; all P<0.0001). Over the entire study period, adherence to guidelines was modestly lower in older compared with younger patients (OR, 0.87; 95% CI, 0.84 to 0.89; P<0.0001). Treatment with evidence-based therapies was modestly lower in women compared to men (adjusted OR, 0.90; 95% CI, 0.88 to 0.92; P<0.0001). Significant age–sex interaction (P<0.001) implies different sex disparities in younger and older patient groups as well as different age disparities in men and women patient groups. The adjusted odds ratio for adherence to the CPM comparing younger men versus younger women was 1.150 (95% CI, 1.120 to 1.181; P<0.0001), whereas the difference in adherence comparing elderly women and elderly men was only of borderline statistical significance. The adjusted OR for adherence to the CPM comparing younger men and elderly men was 1.206 (95% CI, 1.163 to 1.251; P<0.0001), whereas adherence in younger women was slightly higher than that in older women (OR, 1.091; 95% CI, 1.054 to 1.130; P<0.0001; Table 4).
In the study group of patients with AMI, the adjusted OR for improvement per year in the composite measure also demonstrated similar rates of improvement for all 4 patient groups: younger men, 1.36 (95% CI, 1.25 to 1.47); younger women, 1.36 (95% CI, 1.26 to 1.47); older men, 1.35 (95% CI, 1.26 to 1.45); and older women, 1.32 (95% CI, 1.24 to 1.41; all P<0.0001). Similar significant age–sex interaction (P<0.001) existed in the subgroup of AMI patients as in the overall study population.
This study demonstrated that participating in the AHA GWTG-CAD program, a quality-improvement program, was associated with increased guideline adherence over time irrespective of sex or age for patients hospitalized with CAD. The use of evidence-based treatments for CAD increased substantially in men and women as well as for older and younger patients as a function of time in the program. The small treatment gaps observed based on age or sex were narrowed but in some cases were not fully eliminated. The substantial improvement in conformity with guideline recommended therapies among eligible men, women, younger, and older patients is notable and merits further consideration.
The AHA and the ACC have published guidelines for the treatment of CAD.1,2 These guidelines recommend administration of evidence-based therapies for all eligible patients with CAD. Despite the widespread publication of these guidelines, adherence is both suboptimal and highly variable.10,11 A source of this variability remains the disparity in treatment afforded to women and the elderly. Although women and the elderly are underrepresented in clinical studies,12 these treatments have been shown to improve outcomes in both groups.13–16 As such, ACC/AHA guidelines recommend secondary prevention therapies equally to men and women and younger and older patients. Unfortunately, treatment gaps exist between these populations in many care settings.
Other studies have found that women receive evidence-based therapies less frequently compared to men. The Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes With Early Implementation of the ACC/AHA Guidelines (CRUSADE) trial studied 35 875 patients with chest pain and ECG or other biomarkers for myocardial ischemia. After adjustment, women, who represented 41% of the population, were less likely to receive aspirin and β-blocker drugs on admission and aspirin, statin, and ACE inhibitor treatment on discharge.3 The differences observed between men and women in the present study are small and less than those gaps observed in CRUSADE. The absolute differences observed in CRUSADE were larger than those in this study for aspirin on discharge (2.9 versus 1.6%), βblocker on discharge (2.2 versus 1.3%), and lipid-lowering therapy on discharge (7.5 versus 5.6%).3
The clinical relevance and reason for the small sex-based treatment differences in this study are not clear. Many of the differences reported between the age and sex groups are not just small, but are unlikely to be clinically meaningful. A modest disparity was seen among younger women, a group where the diagnosis of CAD has a lower baseline probability and may be delayed until later in the hospitalization—which might be an explanation for less aggressive treatment in the first 24 hours. On the other hand, among older patients, in whom the diagnosis of CAD is more likely, treatment was similar between men and women. This suggests that initial differences in diagnostic certainty between women and men might be a contributing factor to lower guideline adherence. However, delay in treatment and difficulty in diagnosis cannot account for the differences observed for discharge measures. All of these women should have received treatment at discharge because the diagnosis was not in doubt and thus all therapies should have been applied equally. These findings are consistent with Miller and colleagues who found that despite an established diagnosis of CAD, lipid-lowering therapy was not administered equally between sexes.17 Sex bias is the basis for the awareness campaign by the AHA entitled Go Red For Women. The goal is to improve the recognition and treatment of CAD in women. There may be differences in documentation of contraindications or intolerance between patient groups which could contribute to differences in treatment rates.
Previous studies have observed that elderly patients receive appropriate treatment less frequently than younger patients. Tran et al showed that older patients with AMI were more likely to have had a prior AMI or stroke, heart failure, hypertension, or chronic obstructive lung disease.6 In their study, however, elderly patients were less likely to be prescribed aspirin on admission and discharge, βblockers on admission and discharge, and lipid-lowering therapy at discharge compared with younger patients.6 Even within the context of a quality-improvement project, the elderly receive less than optimal care. The Cooperative Cardiovascular Project, a program developed by the Centers for Medicare and Medicaid Services, included only those patients over the age of 65 years but stratified them into groups of increasing age. Older patients were prescribed aspirin and β-blocker drugs less frequently on both admission and discharge compared with younger Medicare patients.5
In the CRUSADE trial, contraindications were more frequently observed in older patients. However, in eligible patients, adjusted prescription of aspirin and βblocker drugs on admission and lipid lowering therapy on discharge were lower in older patients.4 Again, compared to CRUSADE, the differences observed between older and younger patients is less in the current study: absolute differences in aspirin on discharge (3.0 versus 2.1%) and βblocker on discharge (3.1 versus 1.6%).4 Yarzebski et al found that physicians were less likely to prescribe lipid agents to elderly patients. Factors that influenced the decision to prescribe these agents included the cost of medication, the feeling that these drugs are not as effective as other medications, and the attitude that dietary therapy is an alternative treatment.18 Thus, a physician may limit the aggressiveness of their treatment based on a feeling that an elderly patient will have less clinical benefit and greater costs. Additionally, medical treatment lags behind clinical trial evidence. Antman et al demonstrated that even expert recommendations for particular therapies may lag years behind conclusive clinical trials.19 Although there are several studies demonstrating the effects of lipid lowering therapy in younger patients, trials specifically involving the elderly have only recently been published.16 This could explain the slower incorporation of these treatments into standard therapy for the elderly.
Our findings are consistent with those of Jani et al, who found that application of the Guidelines Applied In Practice program improved adherence to guidelines in women and men.20 They did not control for both age and sex terms in their model, which was performed in the present study.
This is the largest study to date which demonstrates improvement in guideline adherence regardless of sex or age over time using a quality-improvement program. The AHA GWTG-CAD Program uses a collaborative model for quality-improvement with workshops and multiple reinforcing strategies between face to face sessions to improve cardiovascular care. In addition, the Patient Management Tool facilitates concurrent review and management. LaBresh et al has shown that participation in this program is associated with improved adherence to guidelines over a 1-year period.7 In addition, guideline adherence in GWTG-CAD hospitals is significantly better than non-GWTG hospitals in the Hospital Compare database.21 The GWTG program was not designed to address differences in clinical presentation between men and women and younger and older patients, and thus therapies should be applied indiscriminately.
The GWTG program with quality-improvement processes and Patient Management Tool promotes standardized care and should limit age and sex treatment disparities. The cause of small persistent differences in care is not clear. Diagnostic uncertainty is an unlikely cause, as patients would not have been entered into the database without a confirmed diagnosis. Intolerance or contraindication to medications should not be relevant as patients would not have been eligible for treatments if they were intolerant. Further study is necessary to determine the cause of these remaining treatment disparities and develop techniques for closing these treatment gaps. As demonstrated in Table 2, treatment gaps for some individual performance measures were narrow. However, for some measures, the treatment gaps were large representing significant opportunities for improvement. The largest gaps in sex and age were observed in lipid treatment. Additionally, treatment gaps between younger and older patients included the administration of ACE inhibitors or ARB for left ventricular dysfunction and tobacco cessation counseling. Focusing further education on these individual measures represents the best opportunity to narrow treatment gaps in guideline adherence.
This study was not a randomized clinical trial, and the improvements in performance measures may have been influenced by factors other than GWTG-CAD participation such as secular trends. Data were collected by medical chart review and depend on the accuracy and completeness of documentation. As such, a proportion of patients reported to be eligible for treatment who did not receive recommended treatments may have had contraindications or intolerance to specific interventions that were present but not documented. Participation in GWTG is voluntary and may select for higher performing hospitals. It is possible that, as such, higher adherence may have been observed; however, this is unlikely to have affected the differences between age and sex. Despite multivariable adjustment we cannot exclude that residual measured and unmeasured confounding may account for these observations. Each patient contributes several measures to the CPM. The generalized estimating equation model may not fully account for the within patient correlation. The term “disparity” in this study included patient and hospital factors and thus differs with the Institute of Medicine definition. As GWTG does not collect data on postdischarge outcomes, the full implications of these improvements in process measure treatment rates for women and older patients over time, but without elimination of the treatment gaps, could not be directly explored.
Improvement in adherence to guidelines, including pharmacological and nonpharmacologic management, for the treatment of CAD was demonstrated in younger and older women as well as younger and older men over a 5-year period among GWTG-CAD participating hospitals. These patterns differ from prior studies showing reductions in evidence-based therapy in relation to age and sex among CAD patients, and thereby suggest that clinicians may have become more adherent with guideline-based therapeutic recommendations for their older and women patients, particularly in the framework of a guideline-based performance improvement program. Small treatment differences observed between men and women less than age 75 years and between younger and older patients irrespective of sex were not eliminated, however, over the study period. Further study to determine whether these small remaining treatment differences are clinically relevant is warranted.
Sources of Funding
This study was funded by the American Heart Association.
Dr Lewis has received speaker honoraria from Reliant Pharmaceuticals and has served as a consultant for Medtronic. Dr Peterson has received research funding from Schering Plough/Merck and BMS/Sanofi. Dr Hernandez has received research funding and/or honoraria from Johnson & Johnson/Scios, GlaxoSmithKline, Medtronic, Novartis, and AstraZeneca. Dr LaBresh is an employee of Research Triangle Institute International. Dr Cannon has received research funding from Accumetrics, AstraZeneca, Bristol-Myers Squibb/Sanofi Partnership, GlaxoSmithKline, Merck, Merck/Schering Plough Partnership and has ownership interest in Automedics Medical Systems. Dr Fonarow has received research funding and honoraria from and served as a consultant for Medtronic, GlaxoSmithKline, and Novartis; has received other research support from Pfizer; and has served as a consultant for and received honoraria from BMS/Sanofi, Merck, and Schering.
The online-only Data Supplement is available at http://circoutcomes.ahajournals.org/cgi/content/full/CIRCOUTCOMES.108.824763/DC1.
Canadian Cardiovascular Society; American Academy of Family Physicians; American College of Cardiology; American Heart Association, Antman EM, Hand M, Armstrong PW, Bates ER, Green LA, Halasyamani LK, Hochman JS, Krumholz HM, Lamas GA, Mullany CJ, Pearle DL, Sloan MA, Smith SC Jr, Anbe DT, Kushner FG, Ornato JP, Pearle DL, Sloan MA, Jacobs AK, Adams CD, Anderson JL, Buller CE, Creager MA, Ettinger SM, Halperin JL, Hunt SA, Lytle BW, Nishimura R, Page RL, Riegel B, Tarkington LG, Yancy CW. 2007 focused update of the ACC/AHA 2004 guidelines for the management of patients with ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2008; 51: 210–247.
Smith SC Jr, Allen J, Blair SN, Bonow RO, Brass LM, Fonarow GC, Grundy SM, Hiratzka L, Jones D, Krumholz HM, Mosca L, Pasternak RC, Pearson T, Pfeffer MA, Taubert KA; AHA/ACC; National Heart, Lung, and Blood Institute. AHA/ACC guidelines for secondary prevention for patients with coronary and other atherosclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute. Circulation. 2006; 113: 2363–2372.
Blomkalns AL, Chen AY, Hochman JS, Peterson ED, Trynosky K, Diercks DB, Brogan GX Jr, Boden WE, Roe MT, Ohman EM, Gibler WB, Newby LK; CRUSADE Investigators. Gender disparities in the diagnosis and treatment of non-ST-segment elevation acute coronary syndromes: large-scale observations from the CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes With Early Implementation of the American College of Cardiology/American Heart Association Guidelines) National Quality Improvement Initiative. J Am Coll Cardiol. 2005; 45: 832–837.
Alexander KP, Roe MT, Chen AY, Lytle BL, Pollack CV Jr, Foody JM, Boden WE, Smith SC Jr, Gibler WB, Ohman EM, Peterson ED; CRUSADE Investigators. Evolution in cardiovascular care for elderly patients with non-ST-segment elevation acute coronary syndromes: results from the CRUSADE National Quality Improvement Initiative. J Am Coll Cardiol. 2005; 46: 1479–1487.
LaBresh KA, Fonarow GC, Smith SC Jr, Bonow RO, Smaha LC, Tyler PA, Hong Y, Albright D, Ellrodt AG; Get With The Guidelines Steering Committee. Improved treatment of hospitalized coronary artery disease patients with the get with the guidelines program. Crit Pathw Cardiol. 2007; 6: 98–105.
Institute of Medicine (U.S.). Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academy Press; 2001.
Eagle KA, Montoye CK, Riba AL, DeFranco AC, Parrish R, Skorcz S, Baker PL, Faul J, Jani SM, Chen B, Roychoudhury C, Elma MA, Mitchell KR, Mehta RH; American College of Cardiology’s Guidelines Applied in Practice (GAP) Projects in Michigan; American College of Cardiology Foundation (Bethesda, Maryland) Guidelines Applied in Practice Steering committee. Guideline-based standardized care is associated with substantially lower mortality in medicare patients with acute myocardial infarction: the American College of Cardiology’s Guidelines Applied in Practice (GAP) Projects in Michigan. J Am Coll Cardiol. 2005; 46: 1242–1248.
Olsson G, Wikstrand J, Warnold I, Manger Cats V, McBoyle D, Herlitz J, Hjalmarson A, Sonneblick EH. Metoprolol-induced reduction in postinfarction mortality: pooled results from five double-blind randomized trials. Eur Heart J. 1992; 13: 28–32.
Deedwania P, Stone PH, Bairey Merz CN, Cosin-Aguilar J, Koylan N, Luo D, Ouyang P, Piotrowicz R, Schenck-Gustafsson K, Sellier P, Stein JH, Thompson PL, Tzivoni D. Effects of intensive versus moderate lipid-lowering therapy on myocardial ischemia in older patients with coronary heart disease: results of the Study Assessing Goals in the Elderly (SAGE). Circulation. 2007; 115: 700–707.
Miller M, Byington R, Hunninghake D, Pitt B, Furberg CD. Sex bias and underutilization of lipid-lowering therapy in patients with coronary artery disease at academic medical centers in the United States and Canada. Prospective Randomized Evaluation of the Vascular Effects of Norvasc Trial (PREVENT) Investigators. Arch Intern Med. 2000; 160: 343–347.
Jani SM, Montoye C, Mehta R, Riba AL, DeFranco AC, Parrish R, Skorcz S, Baker PL, Faul J, Chen B, Roychoudhury C, Elma MA, Mitchell KR, Eagle KA; American College of Cardiology Foundation Guidelines Applied in Practice Steering Committee. Sex differences in the application of evidence-based therapies for the treatment of acute myocardial infarction: the American College of Cardiology’s Guidelines Applied in Practice projects in Michigan. Arch Intern Med. 2006; 166: 1164–1170.
Lewis WR, Peterson ED, Cannon CP, Super DM, LaBresh KA, Quealy K, Liang L, Fonarow GC. An organized approach to improvement in guideline adherence for acute myocardial infarction: results with the Get With The Guidelines Quality Improvement Program. Arch Intern Med. 2008; 168: 1813–1819.