SYMPOSIUM ON COMPLEX CHRONIC CO-MORBIDITIES Complex co-morbidities in patients with myocardial infarction Claudio Ceconi, Ferrara Ferrara 10 Marzo 2007 WHO: “Coronary heart disease is now the leading cause of death worldwide. It is on the rise and has become a true pandemic” More can be done to improve the mortality and reduce the burden of cardiovascular disease Chronic diseases represent a huge proportion of human illness 58 million deaths in 2005: • Cardiovascular disease 30% • Cancer 13% • chronic respiratory diseases 7% • Diabetes 2% Horton R. Lancet, 2006 Factors determining different outcomes of Patients 1. Chance Iezzoni I. “Risk adjustment for measuring healthcare outcomes” Health Administration Press, 1997 Factors determining different outcomes of Patients 1. Chance 2. Accuracy of data sources: do they describe accurately the real world? Iezzoni I. “Risk adjustment for measuring healthcare outcomes” Health Administration Press, 1997 Typical subject eligible for randomization in a CAD trial AMI: RCT vs. Real World Age Relatively low Sex M:F >>1 Co-morbidity Uncommon Renal dysfunction Exclusion criteria Disability Uncommon Drugs At target Compliance High Therapy duration 1-3 yy Primary objective mortality Mortality at 1 y 5-15 % Age-specific rates of acute coronary events Population Study on 91 106 in Oxfordshire, UK, in 2002–05 All Coronary Events Rates per 1000 population per year STEMI P M Rothwellet al. The Lancet 2006 The Real World ? AMI: RCT vs. Real World Age 70-75 Sex M:F ~ 1,5 Co-morbidity Common Renal dysfunction 15-30 % Disability Common Drugs Low dose Compliance Low Therapy duration Life-long Primary objective QoL Mortality at 1 y 10-30% Factors determining different outcomes of Patients 1. Chance 2. Accuracy of data sources: do they describe accurately the real world? 3. Risk associated to specific diseases Iezzoni I. “Risk adjustment for measuring healthcare outcomes” Health Administration Press, 1997 Death and re-AMI: Role of Diabetes McGuire DK GUSTO-IIb Eur Heart J 2000 Relation between Renal Dysfunction and Cardiovascular Outcomes N.S. Anavekar et al., N Engl J Med 2004 N.S. Anavekar et al., N Engl J Med 2004; 351: 1285-1295 CAD & COMORBIDITIES Factors determining different outcomes of Patients 1. Chance 2. Accuracy of data sources: do they describe accurately the real world? 3. Risk associated to specific diseases 4. Differences in the efficiency of care and therapies and of quality of the treatments Iezzoni I. “Risk adjustment for measuring healthcare outcomes” Health Administration Press, 1997 The Florence Acute Myocardial Infarction (AMIFlorence) registry ● Population-based, prospective observational study ● 740 Pts with STEMI hospitalised within 12 h ● Chronic comorbidity Score (CCS) calculated from disease specific mortality assessed in an ageadjusted Cox model (Wald test = β coefficient/SE ratio) Balzi et al. AHJ 206 Age-adjusted association of chronic comorbid conditions with 1-year mortality in AMIFlorence Balzi et al. AHJ 206 Baseline Clinical Characteristics and Outcome by chronic CS category in AMIFlorence Balzi et al. AHJ 206 Baseline Clinical Characteristics and Outcome by chronic CS category in AMIFlorence Balzi et al. AHJ 206 Cumulative survival by chronic comorbidity score category and type of treatment from AMI to 12-month Balzi et al. AHJ 2006 Relation to not being dispensed β-blocker Ontario AMI Registry: n=15542 Rochon PA et al. 1999 Factors determining different outcomes of Patients 1. Chance 2. Accuracy of data sources: do they describe accurately the real world? 3. Risk associated to specific diseases 4. Differences in the efficiency of care and therapies and of quality of the treatments Iezzoni I. “Risk adjustment for measuring healthcare outcomes” Health Administration Press, 1997 Age-specific rates of vascular events Population Study on 91 106 in Oxfordshire, UK, in 2002–05 Men Rates per 1000 population per year Women P M Rothwellet al. The Lancet 2006 Risk Factors for COPD Nutrition Infections Socio-economic status Aging Populations 27 Characteristics of MEDICARE beneficiaries admitted to the hospital with AMI Î Î Gan SC et al. NEJM 2000 Myocardial Infarction National Audit Project Patient characteristics and co-morbidities of those patients managed by cardiologists and non-cardiologists Cardiologists Non-cardiologists Age >75yr 9611/30080 32.0% 23668/52140 45.4% Male 20538/30252 67.9% 33256/53065 62.7% STEMI 15282/30383 50.3% 17881/53216 33.6% Heart failure 1229/25156 4.9% 3900/48580 8.0% Diabetes 5033/28076 17.9% 10109/49079 20.6% Cerebrovascular disease 1863/25632 7.3% 4698/48270 COPD 3317/25278 CRF 729/25144 Co-morbidities 1 or more of above 13.1% 2.9% 31.8% 7567/48584 2011/48578 9.7% 15.6% 4.1% 39.8% No significant difference in history of AMI, angina, hyperlipidaemia, hypertension after adjustment for age BMJ 2006;332:1306-1311 Comorbidity with ACS 2004 data 16 14 12 CCF, treated heart failure COPD, chronic obstructive airways disease or asthma CRF, chronic renal failure CVSd, Cerebrovascular disease PVD, peripheral vascular disease 10 8 6 4 2 0 Myocardial Infarction National Audit Project CCF COPD CRF CVSd PVD “…patients with COPD are at high risk for poor outcomes after MI ” Am J Cardiol 2007;99:636–641 Cardiovascular effects of β–agonist use Cardiovascular events in long duration trials Salpeter SL et al. Chest 2004 Risk of acute myocardial infarction associated with the use of oral steroids Í Í Varas-Lorenzo C et al. Atherosclerosis 2006 Morbidity/Mortality Reduction in COPD effect of RAS inhibition and Statins COPD Hospitalization Infarction or Death Mancini et al. JACC 2006