Abstract
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.
| Original language | English (US) |
|---|---|
| Journal | American Journal of Medicine |
| Volume | 126 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2013 |
Fingerprint
Keywords
- Arteriosclerosis
- Myocardial infarction
- Risk factors
- Sex
- Statins
ASJC Scopus subject areas
- Medicine(all)
Cite this
Predictors of adherence to performance measures in patients with acute myocardial infarction. / Kumbhani, Dharam J.; Fonarow, Gregg C.; Cannon, Christopher P.; Hernandez, Adrian F.; Peterson, Eric D.; Peacock, W. Frank; Laskey, Warren K.; Pan, Wenqin; Schwamm, Lee H.; Bhatt, Deepak L.
In: American Journal of Medicine, Vol. 126, No. 1, 01.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predictors of adherence to performance measures in patients with acute myocardial infarction
AU - Kumbhani, Dharam J.
AU - Fonarow, Gregg C.
AU - Cannon, Christopher P.
AU - Hernandez, Adrian F.
AU - Peterson, Eric D.
AU - Peacock, W. Frank
AU - Laskey, Warren K.
AU - Pan, Wenqin
AU - Schwamm, Lee H.
AU - Bhatt, Deepak L.
PY - 2013/1
Y1 - 2013/1
N2 - 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.
AB - 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.
KW - Arteriosclerosis
KW - Myocardial infarction
KW - Risk factors
KW - Sex
KW - Statins
UR - http://www.scopus.com/inward/record.url?scp=84871587272&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871587272&partnerID=8YFLogxK
U2 - 10.1016/j.amjmed.2012.02.025
DO - 10.1016/j.amjmed.2012.02.025
M3 - Article
C2 - 22925314
AN - SCOPUS:84871587272
VL - 126
JO - American Journal of Medicine
JF - American Journal of Medicine
SN - 0002-9343
IS - 1
ER -