Complex co-morbidities in patients with myocardial infarction Claudio Ceconi

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
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