title: Achieving quality indicator benchmarks and potential impact on coronary heart disease mortality.
abstract: BACKGROUND
  Quality indicators in coronary heart disease (CHD) measure the practice gap between optimal care and current clinical practice. However, the potential impact of achieving quality indicator benchmarks remains unknown.
  
  
  METHODS
  Using a validated, epidemiologic model of CHD in Ontario, Canada, we estimated the potential impact on mortality of improved utilization on CHD quality indicators from 2005 levels to recommend benchmark utilization of 90%. Eight CHD disease subgroups were evaluated, including inpatients with acute myocardial infarction (AMI), acute coronary syndromes, and heart failure, in addition to ambulatory patients who were post-acute myocardial infarction survivors, or had heart failure, chronic stable angina, hypertension, or hyperlipidemia. The primary outcome was the predicted mortality reduction associated with meeting quality indicator targets for each CHD subgroup-treatment combination.
  
  
  RESULTS
  In 2005, there were 10,060 CHD deaths in Ontario, representing an age-adjusted CHD mortality of 191 per 100,000 people. By meeting quality indicator utilization benchmarks, mortality could be potentially reduced by approximately 20% (95% confidence interval 17.8-21.1), representing approximately 1960 avoidable deaths. The bulk of this potential benefit was in ambulatory patients with chronic stable angina (36% of reduction) and heart failure (31% of reduction). The biggest drivers were optimizing angiotensin-converting enzyme inhibitor use in chronic stable angina patients (approximately 440 avoidable deaths) and β-blocker use in heart failure (approximately 400 avoidable deaths).
  
  
  CONCLUSIONS
  These findings reinforce the importance of quality indicators and could aid policy makers in prioritizing strategies to meet the goals outlined in the Canadian Heart Health Strategy and Action Plan for reducing cardiovascular mortality.
authors.affiliation: Division of Cardiology, Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. wijeysundera@gmail.com
authors.forename: Harindra C
authors.initials: HC
authors.lastname: Wijeysundera
authors.forename: Nicholas
authors.initials: N
authors.lastname: Mitsakakis
authors.forename: William
authors.initials: W
authors.lastname: Witteman
authors.forename: Mike
authors.initials: M
authors.lastname: Paulden
authors.forename: Gabrielle
authors.initials: G
authors.lastname: van der Velde
authors.forename: Jack V
authors.initials: JV
authors.lastname: Tu
authors.forename: Douglas S
authors.initials: DS
authors.lastname: Lee
authors.forename: Shaun G
authors.initials: SG
authors.lastname: Goodman
authors.forename: Robert
authors.initials: R
authors.lastname: Petrella
authors.forename: Martin
authors.initials: M
authors.lastname: O'Flaherty
authors.forename: Simon
authors.initials: S
authors.lastname: Capewell
authors.forename: Murray
authors.initials: M
authors.lastname: Krahn
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019985:Benchmarking / Q000379:methods*; D003327:Coronary Disease / Q000401:mortality* / Q000628:therapy; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009204:Myocardial Revascularization / Q000379:methods / Q000592:standards*; D009864:Ontario / Q000453:epidemiology; D011379:Prognosis; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors