OBJECTIVE—To compare the health service utilization and long-term outcomes of acute myocardial infarction (AMI) patients with and without diabetes in Ontario.

RESEARCH DESIGN AND METHODS—We examined 25,697 patients from Ontario (6,052 and 19,645 patients with and without diabetes, respectively) who were hospitalized because of AMI between 1 April 1992 and 31 December 1993. Using linked administrative databases, we determined the use of invasive cardiac procedures at 1 year as well as the intensity of specialty follow-up care and use of evidence-based pharmacotherapies (among elderly individuals) within the first 90 days of hospital discharge. Outcomes examined included mortality and recurrent cardiac admissions at 30 days and 5 years post AMI. Multivariable analyses adjusted for sociodemographic and case-mix characteristics, attending physician specialty, and admitting hospital characteristics.

RESULTS—Despite being at significantly higher risk for death at baseline, diabetic patients were less likely to be followed-up by a cardiologist (22.2 vs. 25.6%, P < 0.001), to receive myocardial revascularization (12.6 vs. 14.9%, P < 0.001), to receive β-blockers (34.2 vs. 44.0%, P < 0.001), and to receive aspirin therapy (59.7 vs. 63.5%, P < 0.001) after AMI than their nondiabetic counterparts. Diabetes was an important independent predictor of 5-year morbidity (adjusted hazard ratio 1.52, 95% CI 1.45–1.59) and 5-year mortality outcomes (1.57, 1.50–1.63). Variations in processes of care were marginally associated with higher nonfatal complication rates for diabetic patients.

CONCLUSIONS—When managing AMI patients with diabetes in Ontario, physician treatment aggressiveness does not correspond appropriately to the baseline risk of patients.

There are ∼2 million people (6% of the total population) who are affected by diabetes in Canada (1). The impact of diabetes on the mortality of a population is significant. Researchers have estimated that at least 5.2% of cardiovascular adult deaths and 3.6% of all adult deaths in the U.S. are attributable to diabetes (2). The deleterious pathophysiologic effects of diabetes seem to operate both before and after an index ischemic cardiovascular event (35). Among patients with acute coronary syndromes, the increase in mortality risk associated with diabetes is of similar magnitude to that of previous myocardial infarction (5,6).

Notwithstanding the importance of biological and clinical factors, acute myocardial infarction (AMI) outcome studies have demonstrated that the use of evidence-based pharmacotherapies and the intensity of specialty cardiac care after discharge from the index AMI may positively impact long-term outcomes (7). One might hypothesize that if physicians appropriately attune themselves to the risk profiles of their patients, then cardiovascular management strategies should be more intensive among diabetic patients, given that baseline risks are what drive the treatment-outcome benefits within a population (8). However, available evidence suggests that physicians do not always tailor management strategies appropriately to reflect the clinical risk profiles of their patients. For example, several studies have demonstrated that treatment aggressiveness in cardiovascular disease inversely correlates with factors such as age, sex, and comorbidity, all of which are markers of poorer prognosis (9,10). The extent to which such observations apply to the management of AMI patients with diabetes in Ontario and the extent to which variations in such process factors can mitigate the deleterious prognostic impact of diabetes on long-term morbidity and mortality outcomes are unknown.

The objective of this study was to compare the health service utilization and long-term outcomes of AMI patients with and without diabetes in Ontario.

### Sources of data

We obtained information from the Ontario Myocardial Infarction Database (OMID), which links together a variety of administrative databases. The accuracy of AMI coding in OMID as well as complete details of the rationale and inclusion-exclusion criteria have been reported previously (11). The cohort consisted of all patients admitted to a hospital with a diagnosis of AMI, code 410 of the ICD-9, between 1 April 1992 and 31 December 1993 (inclusive). The time period was specifically intended for examination of processes of care and their impact on long-term outcomes after AMI. Each patient was tracked for a minimum of 5 years. This cohort recently has been used to demonstrate the long-term process-outcome relationship across sociodemographic and hospital subgroups throughout Ontario (9,12).

Diabetic patients were identified using the Ontario Diabetes Database (ODD) (1). The ODD is an administrative data-derived ongoing registry of all individuals in the province with diagnosed diabetes. Cases are identified on the basis of diagnosis codes recorded in hospital discharge abstracts and physicians’ service claims. Cases of gestational diabetes (diabetes-defining administrative data records occurring within 4 months of an obstetrical event) were removed. Persons who enter the ODD remain until death or migration out of the province, as recorded in the provincial registry, even if they have no diabetes-related claims in subsequent years. Validation against data abstracted from primary care charts has confirmed a sensitivity of 86% and specificity of >97% for identifying patients with diabetes using ODD (1). We further categorized patients with diabetes into two groups: those with complications and those without. Diabetes with complications was identified using ICD-9 codes 250.1–250.9 (inclusive). These codes included metabolic complications (ketoacidosis, hyperosmolarity syndromes) and microvascular and peripheral vascular disease but did not specifically include cardiac complications.

### Patient, physician, and hospital characteristics

Baseline patient characteristics including age, sex, socioeconomic status, and disease severity were obtained from the hospital discharge abstracts of the index AMI admission. Socioeconomic status was derived using ecological income measures (i.e., median neighborhood household income) from 1991 official Canadian census data. Measures of illness severity incorporated variables included in the Ontario AMI mortality prediction rule for 30-day and 1-year mortality (11,13). In addition to diabetes (with and without complications), clinical variables included the following: shock, congestive heart failure, pulmonary edema, cardiac arrhythmia, stroke, malignancy, acute renal failure, and chronic renal failure. The clinical prediction rule has been validated against several population-based AMI cohorts illustrating good predictive accuracy (areas under the receiver operating curve of 0.77 in validation datasets) (13).

Attending physician specialty was identified using hospital discharge abstracts (9,12). Patients were categorized according to the type of facilities available at the admitting institution (on-site versus no on-site angiography and revascularization facilities), regardless of whether they were subsequently transferred to another hospital. Patients at six hospitals that were on-site catheterization-only facilities were excluded from the analysis because of the small sample size (3.5% of the study cohort) (12).

### Processes of care and outcomes

Previous work by our group and others has demonstrated a positive relationship between resource intensity and long-term AMI outcomes, for selected treatments when applied within the first several months that follow the AMI presentation (12). Accordingly, we concentrated on care provided to patients within the first several months after discharge from the AMI admission during the early postinfarct time intervals. Processes of care variables examined included the use of coronary angiography and myocardial revascularization within the first 6 months and 1 year after AMI, respectively (to allow for the appropriate interval of time for patient risk stratification and waiting lists), the provision of follow-up care, and the prescribing of evidence-based pharmacotherapies within the first 90 days after AMI discharge. The importance of such time intervals in relation to long-term outcomes in Ontario has been previously demonstrated (12). Information pertaining to invasive cardiac procedure use was obtained using both hospital discharge abstracts from the Canadian Institute of Health Information (CIHI) and physician claims data from the Ontario Health Insurance Plan (OHIP) databases; information pertaining to physician prescribing patterns was obtained from the Ontario Drug benefits database (available only for those patients ages 65 years and older), whereas data on outpatient physician visits was obtained from OHIP.

Outcomes included mortality and recurrent emergent cardiac hospitalization rates at 5 years after AMI: i.e., recurrent AMI (ICD-9 code 410), congestive heart failure (ICD-9 code 428), unstable angina (ICD-9 codes 411, 413). Emergency visits were identified using physician claims data. In total, 96.2% of readmissions were accompanied by an emergency visit. Accordingly, recurrent cardiac hospitalizations reflected urgent rather than elective indications for the vast majority of cases (12).

### Statistical analysis

We compared the unadjusted baseline characteristics, processes of care, and outcomes between diabetic and nondiabetic patients using χ2 test of proportions and Student’s t test where appropriate. Multivariable analysis adjusted for age, sex, socioeconomic status, illness severity (predicted probability of 30-day mortality), and attending physician and hospital characteristics using Cox proportional hazards model. All multivariable models were constructed in a similar fashion, incorporating backward stepwise regression techniques and then comparing the −2 log likelihoods of the Cox proportional hazards model. Death was used as the main censoring variable when assessing nonfatal outcomes at 5 years. In our statistical models, congestive heart failure was examined using a four-level categorical variable: those without congestive heart failure, those with congestive heart failure (but without pulmonary edema or shock), those with pulmonary edema (but without shock), and those with shock. Similarly, statistical models incorporated diabetes as a three-level variable: those without diabetes, those with diabetes (but without complications), and those with diabetes with complications. We also reanalyzed each model by combining the two subgroups of diabetes into one category (diabetes with or without complications) to examine its overall association with outcomes.

We undertook a secondary analysis to examine the extent to which outcome differences between diabetic and nondiabetic elderly patients could be explained by intergroup variations in the intensity and follow-up care. Specifically, we determined the magnitude of association between diabetes and outcomes, before and after adjustments for evidence-based therapies, outpatient follow-up care, and revascularization procedures. This secondary analysis was confined to the elderly population because information on the use of evidence-based pharmacotherapies was unavailable for patients <65 years of age. Statistical significance was defined as P < 0.05 in all analyses. SAS statistical software package (version 8.2; SAS Institute, Cary, NC) was used.

### Baseline data

The baseline patient, attending physician, and hospital characteristics are shown in Table 1. The study cohort consisted of 25,697 patients, 6,052 (23.6%) of whom were diabetic. Diabetic patients were more likely to be older, women, less affluent, and sicker than nondiabetic patients. There were no significant interphysician or interhospital differences between patients with and without diabetes.

### Process of care characteristics

The relationship between diabetes and processes of care after AMI are shown in Table 2. Despite their higher risk profile, diabetic patients were 13.3% less likely to be followed by a cardiologist (22.2 vs. 25.6%, P < 0.001), 17.8% less likely to receive coronary angiography (20.3 vs. 24.7%, P < 0.001), and 10.2% less likely to receive myocardial revascularization (12.6 vs. 14.9%, P < 0.001) than were nondiabetic patients. Once coronary angiography had been undertaken, patients with diabetes were 21.4% more likely to receive bypass surgery (36.9 vs. 30.4%, P < 0.001), but 25.9% less likely to receive angioplasty (19.5 vs. 26.3%, P < 0.001) compared with patients without diabetes. With regard to medical therapy, the use of evidence-based therapies was extremely low in both diabetic and nondiabetic patients. However, elderly diabetic patients were 18.2% less likely to receive a statin (3.6 vs. 4.4%, P = 0.06), 22.3% less likely to receive β-blockers (34.2 vs. 44.0%, P < 0.001), 6.0% less likely to receive aspirin (59.7 vs. 63.5%, P < 0.001), but 24.4% more likely to receive ACE inhibitors (44.8 vs. 36.0%, P < 0.001) than nondiabetic elderly patients.

Although there were no significant differences in waiting times to invasive coronary interventions or cardiology outpatient visits between the two groups after AMI, patients with diabetes generally waited 1 day less for internal medicine or primary care outpatient visits.

### Outcomes

Table 3 demonstrates that diabetes was associated with significantly higher rates of fatal and nonfatal adverse outcomes compared with nondiabetes. Nonfatal outcome differences were driven predominantly by higher rates of recurrent AMI and congestive heart failure admissions. Intergroup differences in cardiac readmission rates resulted in an average increase of 4.1 days spent in hospital per diabetic patients over the 5-year follow-up (27.6 vs. 23.5 days, P < 0.001).

After adjusting for baseline sociodemographic, clinical, attending physician, and hospital factors, diabetes was a significant independent predictor of long-term morbidity and mortality. Moreover, the magnitudes of association between diabetes (with or without complications) and long-term adverse outcomes were large, ranging from as low as 26% for emergency consultation (i.e., adjusted hazard ratio 1.26, 95% CI 1.22–1.31) to as high as 109% for congestive heart failure hospitalization (2.09, 1.95–2.24). Diabetes (with or without complications) was associated with a 52% higher risk of any cardiac readmission (1.52, 1.45–1.59) and a 54% higher risk of mortality at 5 years (1.57, 1.50–1.63) (Table 4). Apart from age and congestive heart failure (with or without pulmonary edema), diabetes was the strongest predictor of long-term mortality and morbidity after AMI (Table 5).

### Process-outcome relationship (among elderly patients)

We examined whether variations in resource intensity, cardiac procedure use, and evidence-based therapies explain outcome differences between patients with and without diabetes. Figure 1 illustrates that variations in resource intensity, cardiac procedure use, and evidence-based therapies exerted only modest impact on explaining the association between diabetes and nonfatal outcomes in elderly patients. Variations in available process measures could not account for long-term AMI mortality differences between diabetic and nondiabetic elderly patients. Furthermore, among those patients receiving coronary interventions post MI, there was no significant interaction between diabetic outcomes and revascularization modality. Specifically, diabetic patients undergoing percutaneous transluminal coronary angioplasty had similar long-term outcomes as those undergoing coronary artery bypass grafting (20.7 vs. 22.5, percutaneous transluminal coronary angioplasty versus coronary artery bypass grafting, respectively; P = 0.60).

Our study provides a comprehensive assessment of health services and outcome experiences of persons with diabetes who have had an AMI in Ontario. As compared with nondiabetic patients, individuals with diabetes were more likely to be significantly older, women, less affluent, and sicker at presentation. Not surprisingly, diabetes was an important independent predictor of long-term mortality and morbidity after AMI. Despite their higher baseline risk, patients with diabetes were generally managed less aggressively than patients without diabetes. Nonetheless, intergroup process differences only marginally accounted for variations in outcomes between patients with and without diabetes.

Our findings are consistent with the large body of evidence already demonstrating the important independent deleterious impact of diabetes on outcomes for patients presenting with AMI (57,10). For example, among elderly Medicare patients in Michigan, 30-day mortality rates were 21 and 17% for patients with and without diabetes, respectively (10). In an international study consisting of 8,013 patients with acute coronary syndrome enrolled into the Organization to Assess Strategies for Ischemic Syndromes (OASIS) Registry, diabetes was associated with a 1.57-fold risk of long-term mortality after adjusting for baseline clinical and treatment factors, an effect similar in magnitude across many countries (Australia, Brazil, Canada, the U.S., Hungary, and Poland) and one that closely resembles the effect size observed in our own study (5).

Although mortality has served as the focus for many of the diabetes outcome studies, relatively few studies have compared the long-term morbidity of patients with and without diabetes after AMI. We demonstrated that diabetic patients experienced significantly higher recurrent cardiac readmission rates, driven predominantly by congestive heart failure and AMI hospitalizations. The prevalence of heart failure and reinfarction among diabetic patients also supports one widely held hypothesis that attributes excess mortality to a higher susceptibility of myocardial pump failure and reinfarction among patients with diabetes (4). Nonfatal outcome differences translated into 410 additional days in hospital per 100 AMI patients, thus highlighting the significant morbidity and economic burden associated with diabetes in Ontario.

The diminished intensity of treatment provided to diabetic patients observed in our study is also consistent with the results of studies published elsewhere (5,7,10). With two notable exceptions (the higher utilization rates of ACE inhibitors and the preferential use of bypass surgery over angioplasty among those patients receiving angiography), diabetic patients were managed less aggressively in relation to specialty cardiac follow-up care, evidence-based pharmacotherapies, and therapeutic interventions after AMI than were their nondiabetic counterparts. The higher use of ACE inhibitors among diabetic patients likely appropriately reflected their higher prevalence of congestive heart failure and diabetic nephropathy (14). Similarly, the predilection toward bypass surgery over angioplasty (among patients referred for angiography) may have appropriately reflected their higher prevalence of underlying multivessel coronary artery disease (7).

If we are to assume that absolute benefits will always be greatest for those patients at highest baseline risk (8), risk-benefit tradeoffs should have resulted theoretically in more aggressive management for patients with diabetes, as compared with patients without diabetes. However, we observed otherwise. The inverse relationship between physician treatment intensity and baseline patient risk, while seemingly counterintuitive, is exemplified frequently in cardiovascular health services research. For example, available evidence has consistently demonstrated the presence of socioeconomic and age-related treatment disparities, whereby physician aggressiveness in the management of cardiovascular disease is disproportionately targeted toward younger, more affluent, and healthier patients (9,15,16).

Although the precise reasons for the paradoxical relationship between baseline risk and physician intensity in the management of patients with diabetes are unknown, several hypotheses exist. First, available evidence suggests that physicians perceive differences in the efficacy or responsiveness of evidence-based therapies and interventions when the number of chronic comorbid conditions increases (17). Uncertainties arising over the expectations of prognostic benefits incurred for patients with chronic disease, coupled with a clinician’s aversion toward doing harm may sway physician decision-making processes in favor of more conservative approaches when managing patients with diabetes (18,19). Second, treatment decisions may be heavily influenced by the intrinsic attitudes or biases of physicians or their patients. For example, in a survey eliciting practice patterns and attitudes of physicians who cared for patients with renal disease, most physicians acknowledged that dialysis services in Ontario were being implicitly rationed based on such factors as short life expectancy, poor quality of life, patient preferences, and age (20). Third, available evidence suggests that clinicians who are caring for patients with one chronic disease are less attentive to the treatment necessities required for managing other concurrent diseases (21).

Admittedly, our study demonstrated that variations in treatment intensity only marginally accounted for nonfatal outcome differences between diabetic and nondiabetic elderly patients; however, available evidence has demonstrated that the relative prognostic benefits associated with evidence-based therapies when applied to patients with diabetes are similar to, if not greater than, those experienced by patients without diabetes (22). Accordingly, one might reasonably hypothesize that the magnitude of outcome differences between diabetic and nondiabetic patients observed in our study might have been further attenuated if physicians had been more aggressive at managing diabetic patients post MI. In summary, our study strongly suggests that physicians undertreat diabetic patients in Ontario after AMI.

There are several limitations to our study. First, we incorporated administrative data, which lack clinical details (e.g., heart rate, blood pressure, infarct location, prior coronary revascularization, risk factors) required to both characterize the initial presentation of the myocardial infarction and to predict outcome. Furthermore, information pertaining to contraindications was unavailable. Second, only a few therapies with proven lifesaving interventions were examined. Third, our cohort is 10 years old, which likely contributed to the overall low utilization rates of evidence-based therapies observed in this study. Contemporary management patterns for diabetic and nondiabetic care in Ontario may have changed. Indeed, available evidence has suggested that the use of evidence-based pharmacotherapies and cardiac interventions have increased over time among patients with and without diabetes (23). Nonetheless, evidence originating from other populations conducted more recently (and in regions other than Ontario) has similarly demonstrated that patients with diabetes are managed less aggressively with regard to evidence-based cardiovascular therapies and specialized cardiovascular interventions than patients without diabetes (10). Furthermore, these limitations must also be balanced against the comprehensiveness of our sample, which is highly representative of the Canadian population.

In conclusion, our findings suggest that diabetic AMI patients in Ontario have more extensive comorbidity, receive fewer evidence-based therapies, and fare far worse than nondiabetic AMI patients. More aggressive cardiovascular treatment initiatives may significantly mitigate the poor outcomes associated with diabetes. Given the high prevalence of diabetes, the wider implementation of aggressive management strategies specifically focused on cardiovascular risk modification applied to patients with diabetes may significantly translate into demonstrable outcome differences for the entire AMI population.

Figure 1—

Association between diabetes and nonfatal outcomes in elderly patients.

Figure 1—

Association between diabetes and nonfatal outcomes in elderly patients.

Close modal
Table 1—

Baseline patient, physician, hospital, and in-hospital process characteristics for diabetic and nondiabetic AMI patients in Ontario

VariablesDiabetes (n = 6,052)No diabetes (n = 19,645)P value
Patient factors Demographic Age (years) 68.0 66.1 <0.001
Male sex (%) 59.0 67.1 <0.001
Average household income ()* 48,832 50,105 <0.001 Clinical status at admission Cardiogenic shock (%) 3.1 2.2 <0.001 Congestive heart failure (%) 27.2 15.9 <0.001 Pulmonary edema (%) 2.4 1.1 <0.001 Cardiac dysrhythmia (%) 12.1 13.6 0.005 Malignancy (%) 3.8 3.3 0.05 Diabetes with complications (%) 6.6 0.0 <0.001 Stroke (%) 4.8 3.4 <0.001 Acute renal insufficiency (%) 2.2 1.2 <0.001 Chronic renal insufficiency (%) 3.0 1.5 <0.001 Predicted 30-day mortality (%) (mean days) 16.7 14.3 <0.001 Length of hospital stay 9.6 9.2 0.003 Physician factors Attending physician specialty Cardiology 23.4 24.0 0.28 Internal medicine 49.9 48.9 0.18 General practice 26.4 26.7 0.66 Hospital factors Hospital volume (%) High (≥100 cases/year) 74.4 74.1 0.69 Medium (34–99 cases/year) 19.9 19.4 0.43 Low (≤33 cases/year) 5.8 6.5 0.04 Teaching status (%) 18.1 17.9 0.65 Tertiary facility (%) 10.9 11.1 0.82 Geographical factors >50 km from a tertiary hospital (%) 37.3 38.2 0.23 VariablesDiabetes (n = 6,052)No diabetes (n = 19,645)P value Patient factors Demographic Age (years) 68.0 66.1 <0.001 Male sex (%) 59.0 67.1 <0.001 Average household income ()* 48,832 50,105 <0.001
Clinical status at admission Cardiogenic shock (%) 3.1 2.2 <0.001
Congestive heart failure (%) 27.2 15.9 <0.001
Pulmonary edema (%) 2.4 1.1 <0.001
Cardiac dysrhythmia (%) 12.1 13.6 0.005
Malignancy (%) 3.8 3.3 0.05
Diabetes with complications (%) 6.6 0.0 <0.001
Stroke (%) 4.8 3.4 <0.001
Acute renal insufficiency (%) 2.2 1.2 <0.001
Chronic renal insufficiency (%) 3.0 1.5 <0.001
Predicted 30-day mortality (%) (mean days) 16.7 14.3 <0.001
Length of hospital stay  9.6 9.2 0.003
Physician factors Attending physician specialty
Cardiology 23.4 24.0 0.28
Internal medicine 49.9 48.9 0.18
General practice 26.4 26.7 0.66
Hospital factors Hospital volume (%)
High (≥100 cases/year) 74.4 74.1 0.69
Medium (34–99 cases/year) 19.9 19.4 0.43
Low (≤33 cases/year) 5.8 6.5 0.04
Teaching status (%)  18.1 17.9 0.65
Tertiary facility (%)  10.9 11.1 0.82
Geographical factors >50 km from a tertiary hospital (%)  37.3 38.2 0.23
*

Average household income (in Canadian dollars) was obtained from 1991 Canadian census data and corresponds to the enumeration area of the residents;

rural as defined as a hospital being >50 km from the closest tertiary facility.

Table 2—

Processes of care after discharge among diabetic and nondiabetic AMI patients*

VariablesDiabetesNo diabetesP value
Medication use within 90 days of discharge
Aspirin (%) 59.7 63.5 <0.001
β-blockers (%) 34.2 44.0 <0.001
Aspirin or β-blockers (%) 66.1 71.7 <0.001
ACE inhibitors (%) 44.8 36.0 <0.001
HMG-CoA reductase inhibitors (%) 3.6 4.4 0.06
Any evidence-based therapy (%) 78.5 80.4 0.02
Calcium channel blockers (%) 39.7 36.7 0.003
Physician follow-up by 90 days
Cardiology (%) 22.2 25.6 <0.001
Internal medicine (%) 56.2 55.8 0.66
General practice (%) 84.7 82.9 0.004
Any specialist (%) 69.3 73.1 <0.001
No physician (%) 5.2 4.7 0.15
Mean (median) time to 90-day outpatient visit (days)
Cardiology 38 (36) 37 (35) 0.71
Internal medicine 30 (26) 31 (27) 0.01
Any specialist 30 (26) 31 (28) 0.002
General practice 17 (11) 18 (11) <0.001
Invasive cardiac procedure rates among entire AMI cohort
Coronary angiography (%) 20.3 24.7 <0.001
Angioplasty (%) 4.3 7.2 <0.001
Bypass surgery (%) 8.7 8.4 0.38
Myocardial revascularization (%) 12.6 14.9 <0.001
Angioplasty (%) 19.5 26.3 <0.001
Bypass surgery (%) 36.9 30.4 <0.001
Any revascularization (%) 53.9 54.0 0.96
Mean (median) time to invasive cardiac procedures (days)
Coronary angiography 52 (29) 51 (31) 0.55
Revascularization 104 (70) 98 (65) 0.08
VariablesDiabetesNo diabetesP value
Medication use within 90 days of discharge
Aspirin (%) 59.7 63.5 <0.001
β-blockers (%) 34.2 44.0 <0.001
Aspirin or β-blockers (%) 66.1 71.7 <0.001
ACE inhibitors (%) 44.8 36.0 <0.001
HMG-CoA reductase inhibitors (%) 3.6 4.4 0.06
Any evidence-based therapy (%) 78.5 80.4 0.02
Calcium channel blockers (%) 39.7 36.7 0.003
Physician follow-up by 90 days
Cardiology (%) 22.2 25.6 <0.001
Internal medicine (%) 56.2 55.8 0.66
General practice (%) 84.7 82.9 0.004
Any specialist (%) 69.3 73.1 <0.001
No physician (%) 5.2 4.7 0.15
Mean (median) time to 90-day outpatient visit (days)
Cardiology 38 (36) 37 (35) 0.71
Internal medicine 30 (26) 31 (27) 0.01
Any specialist 30 (26) 31 (28) 0.002
General practice 17 (11) 18 (11) <0.001
Invasive cardiac procedure rates among entire AMI cohort
Coronary angiography (%) 20.3 24.7 <0.001
Angioplasty (%) 4.3 7.2 <0.001
Bypass surgery (%) 8.7 8.4 0.38
Myocardial revascularization (%) 12.6 14.9 <0.001
Angioplasty (%) 19.5 26.3 <0.001
Bypass surgery (%) 36.9 30.4 <0.001
Any revascularization (%) 53.9 54.0 0.96
Mean (median) time to invasive cardiac procedures (days)
Coronary angiography 52 (29) 51 (31) 0.55
Revascularization 104 (70) 98 (65) 0.08

Data are means (median) unless otherwise indicated.

*

With the exception of invasive cardiac procedure use, the denominator reflects the number of patients discharged alive from the index AMI admission;

medication use pertaining to patients aged 65 years and older;

evidence-based secondary prevention therapy = aspirin, β-blockers, ACE inhibitors, or HMG-CoA reductase inhibitor.

Table 3—

Outcomes among diabetic and nondiabetic AMI patients*

VariablesDiabetesNo diabetesP value
Fatal outcomes
30-day mortality 19.1 13.3 <0.001
5-year mortality 55.2 36.3 <0.001
Nonfatal outcomes
At 30 days AMI readmission (%) 3.4 2.3 0.003
Congestive heart failure readmission (%) 4.9 2.3 <0.001
Angina readmissions (%) 4.7 3.8 0.01
Any cardiac readmission (%) 12.5 8.2 <0.001
Any emergency department visit (%) 20.4 16.7 <0.001
At 5 years AMI readmission (%) 21.3 14.2 <0.001
Congestive heart failure readmission (%) 26.4 12.4 <0.001
Angina readmissions (%) 23.2 19.4 <0.001
Any cardiac readmission (%) 51.4 36.6 <0.001
Any emergency visit (%) 80.7 74.4 <0.001
Average number of AMI readmissions (per 100 AMI patients) 28.4 17.1 <0.001
Average number of CHF readmission (per 100 AMI patients) 57.0 23.5 <0.001
Average number of angina readmissions (per 100 AMI patients) 42.5 32.0 <0.001
Average number of readmissions (per 100 AMI patients) 127.4 72.6 <0.001
Time to first AMI readmission (days) 503 557 0.04
Time to first congestive heart failure readmission (days) 461 484 0.18
Time to first angina readmission (days) 439 455 0.34
Time to first cardiac readmission (days) 383 437 <0.001
Average number of hospital bed-days resulting from readmissions 27.6 23.5 <0.001
VariablesDiabetesNo diabetesP value
Fatal outcomes
30-day mortality 19.1 13.3 <0.001
5-year mortality 55.2 36.3 <0.001
Nonfatal outcomes
At 30 days AMI readmission (%) 3.4 2.3 0.003
Congestive heart failure readmission (%) 4.9 2.3 <0.001
Angina readmissions (%) 4.7 3.8 0.01
Any cardiac readmission (%) 12.5 8.2 <0.001
Any emergency department visit (%) 20.4 16.7 <0.001
At 5 years AMI readmission (%) 21.3 14.2 <0.001
Congestive heart failure readmission (%) 26.4 12.4 <0.001
Angina readmissions (%) 23.2 19.4 <0.001
Any cardiac readmission (%) 51.4 36.6 <0.001
Any emergency visit (%) 80.7 74.4 <0.001
Average number of AMI readmissions (per 100 AMI patients) 28.4 17.1 <0.001
Average number of CHF readmission (per 100 AMI patients) 57.0 23.5 <0.001
Average number of angina readmissions (per 100 AMI patients) 42.5 32.0 <0.001
Average number of readmissions (per 100 AMI patients) 127.4 72.6 <0.001
Time to first AMI readmission (days) 503 557 0.04
Time to first congestive heart failure readmission (days) 461 484 0.18
Time to first angina readmission (days) 439 455 0.34
Time to first cardiac readmission (days) 383 437 <0.001
Average number of hospital bed-days resulting from readmissions 27.6 23.5 <0.001
*

With the exception of mortality rates, the denominator reflects the number of patients discharged alive from the index AMI admission.

Table 4—

Adjusted hazard ratio of fatal and nonfatal outcomes by 5 years after AMI among diabetic patients with or without complications (as compared with nondiabetic patients)*

VariablesDiabetes classificationAdjusted hazard ratio (±95% CI)P value
Fatal outcomes
Among those discharged alive from the index AMI admission Diabetes with complications 2.35 (2.01–2.74) <0.001
Diabetes without complications 1.67 (1.59–1.77) <0.001
Diabetes total (with or without complications) 1.71 (1.62–1.80) <0.001
Among the entire AMI cohort Diabetes with complications 2.03 (1.79–2.31) <0.001
Diabetes without complications 1.54 (1.47–1.61) <0.001
Diabetes total (with or without complications) 1.57 (1.50–1.63) <0.001
Nonfatal outcomes
AMI readmission Diabetes with complications 1.66 (1.30–2.11) <0.001
Diabetes without complications 1.57 (1.46–1.69) <0.001
Diabetes total (with or without complications) 1.57 (1.46–1.69) <0.001
Congestive heart failure readmission Diabetes with complications 2.67 (2.13–3.18) <0.001
Diabetes without complications 2.08 (1.92–2.21) <0.001
Diabetes total (with or without complications) 2.09 (1.95–2.24) <0.001
Angina readmissions Diabetes with complications 1.33 (1.05–1.67) 0.02
Diabetes without complications 1.31 (1.23–1.41) <0.001
Diabetes total (with or without complications) 1.32 (1.23–1.41) <0.001
Any cardiac readmission Diabetes with complications 1.66 (1.41–1.91) <0.001
Diabetes without complications 1.51 (1.44–1.58) <0.001
Diabetes total (with or without complications) 1.52 (1.45–1.59) <0.001
Any emergency visit Diabetes with complications 1.44 (1.28–1.63) <0.001
Diabetes without complications 1.25 (1.21–1.30) <0.001
Diabetes total (with or without complications) 1.26 (1.22–1.31) <0.001
Fatal or nonfatal outcomes
Death or recurrent cardiac admission Diabetes with complications 1.76 (1.53–1.99) <0.001
Diabetes without complications 1.47 (1.41–1.53) <0.001
Diabetes total (with or without complications) 1.48 (1.43–1.54) <0.001
VariablesDiabetes classificationAdjusted hazard ratio (±95% CI)P value
Fatal outcomes
Among those discharged alive from the index AMI admission Diabetes with complications 2.35 (2.01–2.74) <0.001
Diabetes without complications 1.67 (1.59–1.77) <0.001
Diabetes total (with or without complications) 1.71 (1.62–1.80) <0.001
Among the entire AMI cohort Diabetes with complications 2.03 (1.79–2.31) <0.001
Diabetes without complications 1.54 (1.47–1.61) <0.001
Diabetes total (with or without complications) 1.57 (1.50–1.63) <0.001
Nonfatal outcomes
AMI readmission Diabetes with complications 1.66 (1.30–2.11) <0.001
Diabetes without complications 1.57 (1.46–1.69) <0.001
Diabetes total (with or without complications) 1.57 (1.46–1.69) <0.001
Congestive heart failure readmission Diabetes with complications 2.67 (2.13–3.18) <0.001
Diabetes without complications 2.08 (1.92–2.21) <0.001
Diabetes total (with or without complications) 2.09 (1.95–2.24) <0.001
Angina readmissions Diabetes with complications 1.33 (1.05–1.67) 0.02
Diabetes without complications 1.31 (1.23–1.41) <0.001
Diabetes total (with or without complications) 1.32 (1.23–1.41) <0.001
Any cardiac readmission Diabetes with complications 1.66 (1.41–1.91) <0.001
Diabetes without complications 1.51 (1.44–1.58) <0.001
Diabetes total (with or without complications) 1.52 (1.45–1.59) <0.001
Any emergency visit Diabetes with complications 1.44 (1.28–1.63) <0.001
Diabetes without complications 1.25 (1.21–1.30) <0.001
Diabetes total (with or without complications) 1.26 (1.22–1.31) <0.001
Fatal or nonfatal outcomes
Death or recurrent cardiac admission Diabetes with complications 1.76 (1.53–1.99) <0.001
Diabetes without complications 1.47 (1.41–1.53) <0.001
Diabetes total (with or without complications) 1.48 (1.43–1.54) <0.001

Data are n (range).

*

The association between diabetes (with and without complications) and outcomes is compared with the subgroup of patients without diabetes. Statistical models are adjusted for age, sex, socioeconomic status, shock, congestive heart failure (with or without pulmonary edema), arrhythmias, stroke, malignancy, acute renal failure, chronic renal failure, attending physician specialty, and admitting hospital characteristics. Diabetes with complications were identified using ICD-9 codes 450.1–450.9.

Among patients discharged alive from the index AMI admission.

Table 5—

Independent predictors of death or recurrent cardiac admissions by 5 years after AMI*

VariableHazard ratio for death or recurrent cardiac admission (95% CI)χ2P value
Age (per 10-year increase) 1.33 (1.31–1.35) 1,243.04 <0.001
Congestive heart failure without pulmonary edema 1.74 (1.66–1.82) 579.1 <0.001
Diabetes without complications 1.47 (1.41–1.53) 331.0 <0.001
Diabetes with complications 1.75 (1.53–1.99) 71.4 <0.001
Congestive heart failure with pulmonary edema 1.68 (1.46–1.94) 51.7 <0.001
Chronic renal failure 1.61 (1.43–1.81) 59.6 <0.001
Stroke 1.34 (1.23–1.47) 43.2 <0.001
Malignancy 1.31 (1.20–1.44) 33.9 <0.001
Acute renal failure 1.53 (1.30–1.80) 26.5 <0.001
Neighborhood income (per 10,000 increase) 0.97 (0.96–0.99) 20.5 <0.001 Arrhythmias 1.12 (1.06–1.18) 18.5 <0.001 Hospitals >50 km from a revascularization facility 1.09 (1.04–1.13) 16.1 <0.001 Hospitals with on-site revascularization 0.94 (0.88–1.00) 4.2 0.04 VariableHazard ratio for death or recurrent cardiac admission (95% CI)χ2P value Age (per 10-year increase) 1.33 (1.31–1.35) 1,243.04 <0.001 Congestive heart failure without pulmonary edema 1.74 (1.66–1.82) 579.1 <0.001 Diabetes without complications 1.47 (1.41–1.53) 331.0 <0.001 Diabetes with complications 1.75 (1.53–1.99) 71.4 <0.001 Congestive heart failure with pulmonary edema 1.68 (1.46–1.94) 51.7 <0.001 Chronic renal failure 1.61 (1.43–1.81) 59.6 <0.001 Stroke 1.34 (1.23–1.47) 43.2 <0.001 Malignancy 1.31 (1.20–1.44) 33.9 <0.001 Acute renal failure 1.53 (1.30–1.80) 26.5 <0.001 Neighborhood income (per10,000 increase) 0.97 (0.96–0.99) 20.5 <0.001
Arrhythmias 1.12 (1.06–1.18) 18.5 <0.001
Hospitals >50 km from a revascularization facility 1.09 (1.04–1.13) 16.1 <0.001
Hospitals with on-site revascularization 0.94 (0.88–1.00) 4.2 0.04

Data are n (range) or n.

*

The effect size (±95% CI) for each variable was derived using Cox proportional hazard model adjusted for age, sex, and each of the above listed factors. Sex was not a significant predictor of death or recurrent cardiac admissions (P = 0.14) and is therefore not represented in the figure. Variables are presented in order of the importance as determined from their overall χ2.

This project was supported by an operating grant from the Canadian Institutes of Health Research. The Institute for Clinical Evaluative Sciences is supported, in part, by a grant from the Ontario Ministry of Health. D.A.A. is a new investigator of the Canadian Institutes of Health Research and the Heart and Stroke Foundation of Canada. J.E.H. is a career scientist of the Ontario Ministry of Health and Long Term Care. J.V.T. is supported by a Canada Research Chair in Health Services Research.

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Address correspondence and reprint requests to Dr. David A. Alter, Institute for Clinical Evaluative Sciences G106-2075 Bayview Ave., Toronto, Ontario M4N 3M5. E-mail: david.alter@ices.on.ca.

Received for publication 16 September 2002 and accepted in revised form 17 January 2003.

The results, conclusions, and opinions are those of the authors, and no endorsement by the Ontario Ministry of Health, the Institute for Clinical Evaluative Sciences, or the CIHR is intended or should be inferred.

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.