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Predictors of adherence to performance measures in patients with acute myocardial infarction
Dharam J Kumbhani1, Gregg C Fonarow, Christopher P Cannon
1Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Insights
Adherence to guideline-recommended treatments for acute myocardial infarction (AMI) varies. Factors like age, sex, and comorbidities influence care, highlighting the need for targeted quality improvement interventions for high-risk patients.
Area of Science:
- Cardiology
- Health Services Research
- Clinical Quality Improvement
Background:
- Evidence-based therapies for acute myocardial infarction (AMI) have improved, but gaps and disparities in care persist.
- Understanding factors associated with suboptimal adherence is crucial for narrowing these gaps.
- Guideline-recommended treatments aim to optimize outcomes for AMI patients.
Purpose of the Study:
- To identify patient and hospital characteristics associated with adherence to guideline-recommended therapies for acute myocardial infarction (AMI).
- To assess adherence to six key performance measures in a large AMI patient cohort.
- To inform targeted quality improvement initiatives.
Main Methods:
- Analysis of the Get with the Guidelines-Coronary Artery Disease registry data from 2002-2009.
- Inclusion of 148,654 patients with acute myocardial infarction (AMI).
- Logistic multivariable regression models using generalized estimating equations to identify factors associated with adherence.
Main Results:
- Ten variables were significantly associated with adherence: greater adherence linked to hypertension, hyperlipidemia, and hospitals with full interventional capabilities; worse adherence linked to age, female sex, congestive heart failure, chronic renal insufficiency, atrial fibrillation, and chronic dialysis.
- Age, sex, and calendar year were significant predictors across all adherence models.
- Performance varied based on patient and hospital characteristics.
Conclusions:
- Adherence to evidence-based treatments for acute myocardial infarction (AMI) is not ideal for all patient groups, particularly high-risk populations.
- Factors influencing adherence are closely correlated, suggesting opportunities for tailored quality improvement interventions.
- Targeted interventions can help improve care delivery for acute myocardial infarction (AMI) patients.
Background:
There have been substantial improvements in the use of evidence-based, guideline-recommended therapies for patients with acute myocardial infarction. Nevertheless, some gaps, disparities, and variations in use remain. To understand how such gaps in recommended care may be narrowed further, it may be useful to determine those factors associated with lessened adherence to guideline-based care.
Methods:
The Get with the Guidelines-Coronary Artery Disease registry measured adherence with 6 performance measures (aspirin within 24 hours, discharge on aspirin and beta-blockers, patients with low ejection fraction discharged on angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, smoking cessation counseling, use of lipid-lowering medications) in 148,654 patients with acute myocardial infarction between 2002 and 2009. Logistic multivariable regression models using generalized estimating equations were utilized to identify patient and hospital characteristics associated with adherence to each of 6 measures, and to a summary score of performance for all measures, in eligible patients.
Results:
We identified 10 variables that were associated significantly with either greater adherence (hypertension, hyperlipidemia, hospital with full interventional capabilities, calendar year) or worse adherence (age, female sex, congestive heart failure, chronic renal insufficiency, atrial fibrillation, chronic dialysis) in at least 4 of the 6 treatment adherence models, as well as the summary score adherence model. Age, sex, and calendar year were significant in all models.
Conclusions:
Use of evidence-based acute myocardial infarction treatments remains less than ideal for certain high-risk populations. The close correlations among factors associated with underperformance highlights the potential for specifically targeting and tailoring quality improvement interventions.
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