title: Predictors of adherence to performance measures in patients with acute myocardial infarction.
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.
authors.affiliation: Division of Cardiovascular Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
authors.forename: Dharam J
authors.initials: DJ
authors.lastname: Kumbhani
authors.forename: Gregg C
authors.initials: GC
authors.lastname: Fonarow
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Cannon
authors.forename: Adrian F
authors.initials: AF
authors.lastname: Hernandez
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
authors.forename: W Frank
authors.initials: WF
authors.lastname: Peacock
authors.forename: Warren K
authors.initials: WK
authors.lastname: Laskey
authors.forename: Wenqin
authors.initials: W
authors.lastname: Pan
authors.forename: Lee H
authors.initials: LH
authors.lastname: Schwamm
authors.forename: Deepak L
authors.initials: DL
authors.lastname: Bhatt
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D019317:Evidence-Based Medicine / Q000706:statistics & numerical data*; D005260:Female; D019983:Guideline Adherence / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000628:therapy*; D017064:Process Assessment, Health Care; D058996:Quality Improvement