Abstract
Background
After the acute hospital stay, most cardiac patients in Germany are transferred for a 3–4-week period of inpatient cardiac rehabilitation. We aim to describe patient characteristics and risk factor management of cardiac rehabilitation patients with a focus on drug treatment and control status, differentiated by education level (low level, elementary school; intermediate level, secondary modern school; high level, grammar school/university).
Methods
Data covering a time period between 2003 and 2008 from 68,191 hospitalized patients in cardiac rehabilitation from a large-scale registry (Transparency Registry to Objectify Guideline- Oriented Risk Factor Management) were analyzed descriptively. Further, a multivariate model was applied to assess factors associated with good control of risk factors.
Results
In the total cohort, patients with a manifestation of coronary artery disease (mean age 63.7 years, males 71.7%) were referred to cardiac rehabilitation after having received percutaneous coronary intervention (51.6%) or coronary bypass surgery (39.5%). Statin therapy increased from 76.3% at entry to 88.9% at discharge, and low density lipoprotein cholesterol < 100 mg/dL rates increased from 31.1% to 69.6%. Mean fasting blood glucose decreased from 108 mg/dL to 104 mg/dL, and mean exercise capacity increased from 78 W to 95 W. Age and gender did not differ by education. In contrast with patients having high education, those with low education had more diabetes, hypertension, and peripheral arterial disease, had lower exercise capacity, and received less treatment with statins and guideline-orientated therapy in general. In the multivariate model, good control was significantly more likely in men (odds ratio 1.38; 95% confidence interval 1.30–1.46), less likely in patients of higher age (0.99; 0.99–0.99), with diabetes (0.90; 0.85–0.95), or peripheral arterial disease (0.88; 0.82–0.95). Compared with a low level education, a mid level education was associated with poor control (0.94; 0.89–0.99), while high education did not have a significant effect (1.08; 0.99–1.17).
Conclusion
Patients with different levels of education treated in cardiac rehabilitation did not differ relevantly in terms of demographics, but did differ in some clinical aspects. With respect to the ultimate goal of cardiac rehabilitation, ie, optimal control of risk factors, education level does not play an important role.
Keywords: cardiac rehabilitation, registry, inpatients, educational level, factor analysis, risk factor, lipids, diabetes, hypertension
Introduction
Patients are encouraged to embark on a cardiac rehabilitation program after an acute cardiac event, such as a non-ST elevation myocardial infarction (NSTEMI), ST elevation myocardial infarction (STEMI) followed by coronary surgery or intervention, or acute coronary syndrome, ie, unstable angina pectoris. This is conducted in specialized and certified hospitals, and usually lasts 3–4 weeks.1 Intervention programs aim to increase activity levels, improve nutrition, optimize pharmacological therapy, minimize risk factors, and address psychological issues. Cardiac rehabilitation is as cost-effective as coronary artery bypass grafting2 and can reduce mortality by up to 25%.3–5
In recent years, outcomes research on the effectiveness of cardiovascular interventions has increasingly addressed socioeconomic factors, including the education level of patients.6,7 It has been a matter of debate as to whether the patient’s education affects the various phases of these programs (referral, intervention, and long-term follow-up). Previous studies have been diverse in that they have investigated different countries and settings (eg, outpatient and inpatient cardiac rehabilitation), are partly outdated, and results are heterogeneous.8
In Germany, the transfer of cardiac patients from hospital to the cardiac rehabilitation clinic is an established procedure with low barriers. Thus, the situation allows analysis of therapeutic effectiveness under real practice conditions.1,9 We used this setting to investigate whether patients in cardiac rehabilitation with various levels of education differ in terms of demographic or clinical patient characteristics, risk factor management including drug treatment, or control of risk factors.
Methods
Source of data
The Transparency Registry to Objectify Guideline-Oriented Risk Factor Management (TROL) is a noninterventional program initiated in 2003 in cardiac rehabilitation clinics throughout Germany.10,11 Participating physicians documented cardiac rehabilitation inpatients on standardized case report forms. The ethics committee of the Bavarian Physician Chamber approved the registry, and all patients provided informed consent. Patient data protection was closely observed. We report an analysis of the 2003–2008 dataset, which comprises 68,191 patients with information on education level.
Variables
During the observation period, the education level was documented as follows: low level education (elementary school, corresponding to nine-year education), mid level (secondary modern school, corresponding to 10-year education), and high level (grammar school/university, corresponding to at least a 13-year school education, and facultative university diploma). Patient characteristics were also recorded, ie, age, gender, body mass index, length of stay at the rehabilitation hospital, risk factors (diabetes mellitus, hyperlipoproteinemia, arterial hypertension, smoking, family history for cardiac disease), concomitant diseases (peripheral arterial disease, previous stroke [first reporting year 2005]), systolic and diastolic blood pressure at entry and discharge, laboratory parameters at entry and discharge (total cholesterol, low density lipoprotein cholesterol, high density lipoprotein cholesterol, triglycerides, fasting blood glucose), cardiopulmonary exercise testing, and medication use at entry and discharge (statins, acetylsalicylic acid, beta-blockers, angiotensin-converting enzyme inhibitors, and other drugs).
Statistical analysis
Data are presented as absolute numbers, percentages, or means with standard deviations. The frequencies of categorical variables in subgroups were compared by the Chi-square or Kruskal Wallis test. Continuous variables were compared by the two-tailed Wilcoxon rank sum test. No Bonferroni adjustment was made for this post hoc specified analysis. Percentages were calculated on the basis of patients with data for each respective parameter (ie, no percentages for missing values provided).
The effect of education level on risk factor control was evaluated in a multivariate logistic regression model including age (in 10-year steps), gender, diabetes mellitus, smoking, peripheral arterial disease, body mass index <30 kg/m2, and education. The latter variable included all three levels (low, mid, high level education), and mid level and high level education were compared with low level education. No interaction between predictor variables was noted. The dependent variable was good control, defined as improvement of low density lipoprotein cholesterol and blood pressure during cardiac rehabilitation and achievement of low density lipoprotein cholesterol <100 mg/dL and systolic blood pressure ≤130 mmHg. For each variable, the odds ratio to obtain good control was calculated as a point estimate and 95% confidence interval. P values ≤0.05 in two-sided tests were considered significant. The analysis was performed using SAS 9.1 (SAS Institute Inc, Cary, NC).
Results
Patient demographics and characteristics
In total, 68,191 patients with information on education level were available. Cardiac rehabilitation was undertaken in an inpatient setting in 96.9% and in an outpatient setting in 1.3% (not reported in the remaining cases). Mean duration of rehabilitation was 22.0 ± 40.2 days, with decreased variance in the last documented years of 2007 and 2008. Almost two thirds of patients were retired (60.0%). Patients with elementary school education were the largest group (59.0%), followed by secondary school education (23.6%), and grammar school/university degree (9.6%). Education was not specified in 7.7%.
Demographic and clinical characteristics are summarized in Table 1. The average age was 63.7 years, the proportion of males was 71.7%, and the mean body mass index was 28.0 kg/m2. Cardiovascular risk factors were highly prevalent as expected, in particular dyslipidemia (95.6%), diabetes mellitus (32.8%), arterial hypertension (83.4%), and former or current smoking (45.5% and 16.8%, respectively). In addition to coronary artery disease, 12.2% of patients had peripheral arterial disease and 8.8% had a history of stroke.
Table 1.
Demographic and clinical factors, by education level
| Parameter | Total n = 68,191 |
Low education level n = 40,266 |
Intermediate education level n = 16,110 |
High education level n = 6549 |
Other/unspecified n = 5266 |
|---|---|---|---|---|---|
| Demographics | |||||
| Age, years | 63.7 ± 11.6 | 64.5 ± 11.5 | 61.3 ± 11.6 | 62.9 ± 11.1 | 66.7 ± 11.1 |
| Gender, male | 71.7% | 68.4% | 73.7% | 86.5% | 65.3% |
| Body mass index, kg/m2 | 28.0 ± 4.6 | 28.3 ± 4.7 | 27.3 ± 4.3 | 27.2 ± 4.1 | 28.0 ± 4.7 |
| Diagnosis for CR | |||||
| STEMI | 43.3% (29,496/68,191) | 42.5% (17,101/40,266) | 46.3% (7466/16,110) | 44.1% (2890/6549) | 38.7% (2039/5266) |
| NSTEMI | 18.4% (12,566/68,191) | 17.9% (7225/40,266) | 19.2% (3100/16,110) | 18.4% (1204/6549) | 19.7% (1037/5266) |
| Unstable angina pectoris | 18.3% (12,462/68,191) | 19.2% (7741/40,266) | 18.3% (2952/16,110) | 18.9% (1241/6549) | 10.0% (528/5266) |
| Therapy in acute hospital | |||||
| Percutaneous coronary intervention | 51.6% (33,377/64,670) | 49.4% (18,718/37,856) | 56.1% (8694/15,493) | 54.6% (3452/6318) | 50.2% (2513/5003) |
| Coronary artery bypass surgery | 39.5% (25,784/65,313) | 40.0% (15,392/38,451) | 37.0% (5737/15,506) | 39.4% (2484/6306) | 43.0% (2171/5050) |
| Risk factors | |||||
| Diabetes mellitus | 32.8% (21,903/66,793) | 36.4% (13,967/39,431) | 28.0% (4417/15,760) | 25.4% (1621/6390) | 36.4% (1898/5212) |
| Dyslipidemia | 95.6% (64,905/67,877) | 96.2% (38,571/40,092) | 95.6% (15,316/16,016) | 94.1% (6141/6526) | 93.0% (4877/5243) |
| Arterial hypertension | 83.4% (56,530/67,811) | 84.7% (33,901/40,030) | 80.8% (12,939/16,016) | 81.6% (5314/6515) | 83.4% (4376/5250) |
| Smoking, current | 16.8% (11,143/66,367) | 16.8% (6596/39,197) | 18.4% (2874/15,620) | 15.5% (984/6368) | 13.3% (689/5182) |
| Smoking, previous | 45.5% (29,967/65,815) | 44.3% (17,252/38,969) | 48.8% (7532/15,431) | 49.3% (3101/6291) | 40.6% (2082/5124) |
| Peripheral arterial disease | 12.2% (8177/66,987) | 13.5% (5310/39,474) | 10.5% (1670/15,861) | 9.1% (598/6451) | 11.7% (608/5201) |
| Previous stroke | 8.8% (3963/45,162) | 9.2% (2443/26,469) | 7.5% (757/10,102) | 8.1% (309/3899) | 9.5% (454/4758) |
| Family history of CV disease | 31.3% (20,720/66,099) | 30.8% (11,990/38,962) | 33.9% (5323/15,713) | 34.4% (2188/6362) | 24.1% (1219/5062) |
Notes: Frequencies of categorical variables in subgroups were compared by Chi-square or Kruskal Wallis test. Continuous variables were compared by two-tailed Wilcoxon rank sum test. Due to the very large sample, for all reported items, differences between groups were highly significant (all P values <0.0001).
Abbreviations: STEMI, ST elevation myocardial infarction; NSTEMI, non-ST elevation myocardial infarction; CR, cardiac rehabilitation; CV, cardiovascular.
Patients with STEMI were the largest group (43.3%), followed by NSTEMI (18.4%) and unstable angina pectoris (18.3%). In terms of therapy in the acute hospital setting, percutaneous coronary intervention was more often reported than coronary artery bypass grafting (51.6% versus 39.5%, respectively). Due to the large sample size, statistically significant differences (P < 0.0001) were noted for all demographic and clinical characteristics. Diabetes, previous smoking, and peripheral arterial disease were reported less frequently, and percutaneous coronary interventions more frequently, in patients with high education than in patients with low education.
Medication
Drug treatment at entry and discharge is shown in Table 2. In the overall population, statins were prescribed at entry to the majority of patients (any drug in 76.3%). In particular simvastatin (48.9%, mean dose 28.6 mg), atorvastatin (13.7%, mean dose 24.3 mg) and fluvastatin (6.6%, mean dose 59.9 mg). At the end of cardiac rehabilitation, rates of simvastatin use had increased, while atorvastatin, pravastatin, and fluvastatin use was reported somewhat less frequently. Overall, the mean dosages of these agents had slightly increased. Cholesterol absorption inhibitor prescriptions substantially increased during cardiac rehabilitation (from 5.6% to 43.6%). Aspirin use remained nearly unchanged at a high level (at discharge 82.5%), while clopidogrel use alone or in combination with aspirin slightly decreased. Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers were frequently used in this registry (73.4% and 13.9%, respectively, at discharge). By education level, generally no major differences were noted for the distribution of the various statins or the mean doses at entry or at discharge. However, in patients with high education, lower rates of angiotensin-converting enzyme inhibitors and higher rates of angiotensin receptor blockers, respectively, were reported, along with lower rates of insulin.
Table 2.
Drug treatment at entry and discharge, by education status
| Parameter at entry and discharge | Total n = 68,191 |
Low education level n = 40,266 |
Intermediate education level n = 16,110 |
High education level n = 6549 |
Other/unspecified n = 5266 | P value* |
|---|---|---|---|---|---|---|
| Statins, any (%) | 76.3 → 88.9 | 75.3 → 88.9 | 78.1 → 89.1 | 79.1 → 86.6 | 75.1 → 90.8 | <0.0001 → <0.0001 |
| Simvastatin | 48.9 → 68.4 | 48.7 → 69.1 | 47.2 → 66.2 | 47.0 → 61.0 | 57.9 → 78.8 | <0.0001 → <0.01 |
| Dose | 28.6 ± 12.4 → 29.4 ± 13.5 | 28.5 ± 12.5 → 29.4 ± 13.5 | 29.0 ± 12.5 → 29.7 ± 13.9 | 28.5 ± 12.5 → 29.6 ± 13.5 | 29.0 ± 12.3 → 28.6 ± 12.7 | <0.0001 → <0.0001 |
| Pravastatin | 6.1→ 4.3 | 6.3 → 4.5 | 6.2 → 4.3 | 6.2 → 4.7 | 4.3 → 2.8 | <0.0001 → <0.05 |
| Dose | 26.8 ± 12.3→ 29.1 ± 12.8 | 26.4 ± 11.8 → 28.8 ±11.4 | 26.9 ± 12.8→ 29.3 ± 15.1 | 26.2 ± 13.7→ 29.2 ± 15.1 | 30.8 ± 12.1→ 31.5 ± 11.8 | <0.0001 → <0.0001 |
| Atorvastatin | 13.7 → 11.1 | 13.3 → 10.6 | 15.2 → 12.4 | 17.9 → 15.3 | 7.4 → 5.7 | <0.0001 → <0.001 |
| Dose | 24.3 ± 15.1 → 25.1 ± 15.0 | 23.5 ± 14.1 → 24.4 ± 14.1 | 25.3 ± 16.3 → 26.3 ± 16.6 | 25.3 ± 16.3 → 25.5 ± 15.3 | 25.8 ± 14.7 → 25.2 ± 14.5 | <0.0001 → <0.0001 |
| Fluvastatin | 6.6 → 4.4 | 6.0 → 4.0 | 8.4 → 5.5 | 6.5 → 4.7 | 5.3 → 3.4 | <0.0001 → <0.34 |
| Dose | 59.9 ± 22.0 → 59.8 ± 22.0 | 53.8 ± 23.3 → 58.8 ± 22.2 | 57.1 ± 22.6 → 60.2 ± 21.8 | 53.8 ± 23.3 → 58.8 ± 22.2 | 57.4 ± 22.4 → 60.4 ± 21.7 | <0.0001 → <0.36 |
| Other statin | 1.2 → 1.6 | 1.2 → 1.5 | 1.2 → 2.0 | 1.7 → 1.8 | 0.3 → 0.5 | 0.45 → <0.0001 |
| Dose | 35.5 ± 30.8 → 23.4 ± 24.2 | 35.5 ± 31.0 → 23.3 ± 24.0 | 36.7 ± 32.2 → 22.8 ± 23.4 | 31.6 ± 26.6 → 26.2 ± 28.0 | 45.4 ± 31.1 → 14.6 ± 11.3 | <0.0001 → <0.0001 |
| CAI | 5.6 → 43.6 | 5.5 → 43.8 | 5.7 → 44.7 | 5.5 → 41.2 | 5.5 → 41.5 | 0.76 → <0.0001 |
| Aspirin | 83.0 → 82.5 | 82.6 → 82.4 | 84.4 → 83.2 | 83.6 → 81.7 | 81.5 → 82.0 | <0.0001 → <0.05 |
| Clopidogrel | 46.9 → 43.0 | 44.9 → 40.8 | 45.7 → 42.0 | 51.0 → 47.1 | 50.0 → 47.3 | <0.0001 → <0.0001 |
| Aspirin + clopidogrel | 43.4 → 38.7 | 41.7 → 37.0 | 47.2 → 42.4 | 44.9 → 40.4 | 42.5 → 38.5 | <0.0001 → <0.0001 |
| Beta-blocker | 85.1 → 89.0 | 84.1 → 88.4 | 86.6 → 89.9 | 86.5 → 89.5 | 85.7 → 89.8 | <0.0001 → <0.0001 |
| ACE inhibitor | 70.8 → 73.4 | 71.3 → 74.5 | 71.1 → 73.0 | 66.5 → 67.0 | 71.7 → 74.9 | <0.0001 → <0.0001 |
| ARB | 10.6 → 13.9 | 9.7 → 12.7 | 11.5 → 14.9 | 14.0 → 18.9 | 10.2 → 13.5 | <0.0001 → <0.0001 |
| Oral antidiabetic drug | 14.5 → 16.0 | 15.7 → 17.2 | 12.5 → 13.8 | 11.3 → 12.8 | 15.2 → 18.0 | <0.0001 → <0.0001 |
| Insulin | 11.1 → 11.5 | 12.2 → 12.6 | 9.0 → 9.2 | 8.1 → 8.6 | 13.2 → 13.2 | <0.0001 → <0.0001 |
Notes: Values are percentages at entry and → at discharge. Mean doses are provided with standard deviation.
Frequencies of categorical variables in subgroups were compared by Chi-square or Kruskal Wallis test. Continuous variables were compared by two-tailed Wilcoxon rank sum test.
Abbreviations: ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CAI, cholesterol absorption inhibitor.
Target level attainment
Lipid levels, other surrogate parameters, and target level attainment rates at entry and at discharge are shown in Table 3. In the overall population, mean total cholesterol at entry was 192.1 mg/dL, mean low density lipoprotein cholesterol was 118.8 mg/dL, mean high density lipoprotein cholesterol was 43.5 mg/dL, and mean triglycerides were 160.9 mg/dL.
Table 3.
Outcomes and guideline achievement at entry and discharge, by education level
| Parameter at entry and discharge | Total n = 68,191 |
Low education level n = 40,266 |
Intermediate education level n = 16,110 |
High education level n = 6549 |
Other/unspecified n = 5266 |
P value* |
|---|---|---|---|---|---|---|
| Total cholesterol (mg/dL) | 192.1 ± 47.5 → 158.0 ± 35.9 | 193.3 ± 47.7 → 158.8 ± 36.1 | 193.0 ± 46.9 → 157.5 ± 35.3 | 187.1 ± 46.8 → 154.3 ± 34.9 | 186.3 ± 47.5 → 157.6 ± 37.7 | <0.0001 → <0.0001 |
| LDL-C (mg/dL) | 118.8 ± 38.4 → 89.4 ± 28.2 | 119.7 ± 38.3 → 90.0 ± 28.2 | 119.7 ± 38.5 → 89.0 ± 28.2 | 115.9 ± 38.1 → 87.3 ± 27.5 | 112.9 ± 38.9 → 88.3 ± 29.2 | <0.0001 → <0.0001 |
| HDL-C (mg/dL) | 43.5 ± 12.8 → 43.9 ± 12.1 | 43.7 ± 12.9 → 43.9 ± 12.2 | 43.5 ± 13.0 → 44.2 ± 12.2 | 42.6 ± 12.6 → 43.5 ± 11.9 | 42.9 ± 12.0 → 42.9 ± 11.4 | <0.0001 → <0.0001 |
| Triglycerides (mg/dL) | 160.9 ± 80.6 → 139.1 ± 67.0 | 161.8 ± 81.3 → 140.0 ± 67.7 | 161.6 ± 80.1 → 139.0 ± 64.7 | 156.3 ± 78.1 → 133.9 ± 63.5 | 157.4 ± 79.3 → 138.9 ± 72.9 | <0.0001 → <0.0001 |
| Guideline achievement at entry and at discharge (%) | ||||||
| LDL-C < 100 mg/dL | 31.1 → 69.6 | 30.1 → 69.0 | 29.7 → 70.4 | 33.5 → 72.0 | 39.5 → 69.8 | <0.0001 → <0.0001 |
| TC < 200 mg/dL | 60.1 → 88.1 | 59.1 → 87.6 | 59.5 → 89.0 | 64.3 → 89.6 | 64.6 → 87.9 | <0.0001 → <0.0001 |
| HDL-C > 50 mg/dL (women)/> 40 mg/dL (men) | 44.5 → 46.4 | 44.5 → 45.4 | 44.9 → 48.3 | 46.7 → 50.8 | 41.5 → 41.5 | <0.0001 → <0.0001 |
| Triglycerides < 150 mg/dL | 54.2 → 66.8 | 53.9 → 66.3 | 53.2 → 66.8 | 56.7 → 68.5 | 57.1 → 68.8 | <0.0001 → <0.001 |
| Systolic/diastolic BP at entry and discharge (mmHg) | 130.8 ± 20.3/77.1 ± 11.6 → 122.4 ± 14.4/73.1 ± 9.2 | 131.8 ± 20.3/77.4 ± 11.7 → 123.0 ± 14.5/73.3 ± 9.3 | 129.5 ± 19.9/77.0 ± 11.3 → 121.1 ± 13.7/73.0 ± 8.9 | 128.8 ± 20.3/76.7 ± 11.4 → 120.9 ± 13.7/72.9 ± 8.9 | 130.2 ± 21.1/75.3 ± 11.9 → 123.4 ± 16.0/71.9 ± 9.7 | <0.0001 → <0.0001 <0.0001 → <0.0001 |
| HbA1c (%) | 6.5 ± 1.1 | 6.5 ± 1.1 | 6.3 ± 1.1 | 6.3 ± 1.1 | 6.6 ± 1.1 | <0.0001 |
| Creatinine (mg/dL) | 1.2 ± 0.8 → 1.2 ± 0.8 | 1.2 ± 0.8 → 1.2 ± 0.9 | 1.1 ± 0.7 → 1.1 ± 0.7 | 1.1 ± 0.7 → 1.2 ± 0.7 | 1.2 ± 0.7 → 1.2 ± 0.8 | <0.0001 → <0.0001 |
| Fasting blood glucose (mg/dL) | 107.7 ± 31.9 → 103.9 ± 26.0 | 108.8 ± 32.5 → 104.7 ± 26.5 | 104.9 ± 29.3 → 101.6 ± 23.0 | 104.2 ± 29.1 → 99.4 ± 22.3 | 110.4 ± 36.4 → 109.0 ± 31.9 | <0.0001 → <0.0001 |
| Maximum exercise (W) | 77.8 ± 37.7 → 94.6 ± 40.0 | 72.8 ± 36.5 → 89.5 ± 38.4 | 83.5 ± 37.7 → 101.9 ± 40.1 | 90.0 ± 40.0 → 109.4 ± 42.7 | 80.4 ± 37.0 → 90.6 ± 40.9 | <0.0001 → <0.0001 |
Notes: HbA1c was measured at entry only.
The frequencies of categorical variables in subgroups were compared by chi square or Kruskal Wallis test. Continuous variables were compared by two-tailed Wilcoxon rank sum test.
Abbreviations: LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; TC, total cholesterol; BP, blood pressure.
At discharge, lipid parameters had considerably improved (total cholesterol 158.0 mg/dL, mean low density lipoprotein cholesterol 89.4 mg/dL, mean high density lipoprotein cholesterol 43.9 mg/dL, and mean triglycerides 139.1 mg/dL). Consequently, between entry and discharge, control of lipid parameters had improved substantially. At discharge, the low density lipoprotein cholesterol goal of <100 mg/dL was achieved by 69.6% (entry 31.1%), total cholesterol <200 mg/dL by 88.1% (entry 60.1%), high density lipoprotein cholesterol >50 mg/dL in women or >40 mg/dL in men by 46.4% (entry 44.5%), and triglycerides <150 mg/dL by 66.8% (54.2% at entry). Mean systolic and diastolic blood pressure decreased to 122/73 mmHg (131/77 mmHg at entry), and exercise capacity increased to 95 W (78 W at entry). Fasting blood glucose values decreased to 104 mg/dL (108 mg/dL at entry).
When assessed by education level, target attainment levels were slightly lower in patients with low education compared with those having high education (for example, low density lipoprotein cholesterol <100 mg/dL in 69.0% versus 72.0%). Effects on blood pressure or HbA1c did not differ substantially across groups. Exercise capacity was lower in patients with low education compared with high education (90 W versus 109 W).
Factors associated with good control
Table 4 shows the multivariate model for the association of various factors with good control. Good control status was significantly more likely in men (odds ratio 1.38), and less likely in the presence of higher age (0.99), diabetes mellitus (0.89), and peripheral arterial disease (0.88), while education did not have an important role (compared with low level education as reference; mid level 0.94, ie, slightly reduced odds for good control, high level 1.01, ie, with no significant effect).
Table 4.
Factors associated with good control in the multivariate regression model
| Parameter | OR | 95% CI |
|---|---|---|
| Age (10-year intervals) | 0.99 | 0.99, 0.99 |
| Gender (male, female) | 1.38 | 1.30, 1.46 |
| Diabetes mellitus | 0.90 | 0.85, 0.95 |
| Current smoking | 0.96 | 0.90, 1.02 |
| Peripheral arterial disease | 0.88 | 0.82, 0.95 |
| BMI ≤ 30 kg/m2 | 0.91 | 0.86, 0.96 |
| Mid level educationa | 0.94 | 0.89, 0.99 |
| High level of educationa | 1.01 | 0.94, 1.09 |
Notes: A total of 49,907 observations was used in this model. Good control was defined as attainment of LDL-C < 100 mg/dL and blood pressure ≤ 130/80 mmHg at end of cardiac rehabilitation, and improvement of these values during cardiac rehabilitation. The c statistic, which measures how well the model predicts which parameters are more likely to result in good control, was 0.56. For further explanation of the model, please refer to the methods section.
Versus low level education.
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
Discussion
TROL is one of the largest registries on cardiac rehabilitation and has been used, among others, for the description of secular trends in the management of patients in “real-world” rehabilitation.7 The present analysis (2003–2008) provides insight into the effects of education on selected patient characteristics, cardiac drug treatments, and risk factor management, and is, to our knowledge, one of only few studies of its kind. Regarding the main outcomes, in the descriptive analysis of the subgroups there were only limited differences between patients with low, moderate, and high education with respect to characteristics, drug treatment, and outcomes (the highly significant P values in the comparison of groups due to the large sample are somewhat misleading in this context). Considering the effect of the intervention, the remarkable fact is that the changes are very similar, regardless of the baseline differences. While patients with low education (compared with high education) were very similar in terms of age and gender, they more often had diabetes, hypertension, or peripheral arterial disease which might complicate their management. However, according to the factor analysis, the differences in target level achievement were only weakly associated with education level. Compared with low level education, mid level education had slightly reduced odds of achieving good risk factor control, and high level education did not change the odds significantly.
Previous research has covered all three phases of cardiac rehabilitation (referral stage, participation stage, long-term behavioral change stage), while particularly focusing on perceived or actual physician-related or patient-related barriers to inclusion of eligible patients in outpatient12 or inpatient cardiac rehabilitation programs.13 A comprehensive review was published by Jackson et al for 32 studies with 12,804 patients,8 differentiated by cardiac rehabilitation phase. Patients were more likely to participate in cardiac rehabilitation programs when they were actively referred (physician’s endorsement of the effectiveness of such a program), educated, married, showed high self-efficacy, and when the programs were easily accessible. Patients were less likely to participate when they had to travel long distances to participate in a cardiac rehabilitation program, or experienced guilt over family obligations. Women were less often referred and participated less often, even after referral. Interestingly, in seven of 11 studies, high education was a positive predictor of participation.14–20 No studies were available for the participation stage itself, and only six pertinent articles with no clear positive or negative predictor variables have been found for the long-term phase.8
Our analysis adds to the knowledge of the cardiac rehabilitation participation phase, and unlike other studies, it describes (surrogate) outcomes rather than adherence. Male gender was the strongest factor and was associated with a 38% increase in good control. Female gender was consistently described as a negative predictor for referral in three studies, for participation in 13 studies, and for long-term behavioral change in one study (no positive predictor in any study).8 The other factors that we found to be negatively associated with good control were increasing age, peripheral arterial disease (a marker for high cardiovascular risk as noted in many studies21,22), and a high body mass index (≥30 kg/m2). Generally, the effect sizes (ie, difference of the odds ratios from 1) were very small for education, so the effect of this factor can be considered to be negligible.
Some other findings in this registry deserve consideration. Because guidelines for cardiovascular disease usually focus on conditions, such as coronary heart disease or diabetes, rather than on specific settings, only few treatment goals have been specifically developed for cardiac rehabilitation.23 Thus, the target values for individual risk factors, such as lipids,24 blood pressure,25 and blood glucose,26 and the medication recommendations for patients with coronary heart disease do not generally differ in the cardiac rehabilitation setting from those for patients managed by family physicians. Drug treatment in cardiac rehabilitation appears to have been intensified in terms of drug classes, drugs, and doses compared with previous years.10 For example, the proportion of patients treated with beta-blockers and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers was higher compared with previous surveys in the same setting, while the proportion of patients treated with statins and aspirin was stable. Further, modified treatment approaches (eg, combination therapy of cholesterol absorption inhibitors and statins) and comprehensive patient management27 seem to have contributed to higher control rates, eg, for cholesterol and its fractions.
However, when discharged from cardiac rehabilitation, many patients still do not meet the targets of the respective guidelines. Similar findings on suboptimal control of lipids,28,29 diabetes mellitus,30 and hypertension31 have been reported in primary care, which is the setting in which most patients with coronary heart disease are managed after cardiac rehabilitation. Reasons may include the limited time period in which physicians have to initiate lifestyle modifications or drug treatment during rehabilitation (about 3–4 weeks). Further possible explanations include the complexity of patients with coronary heart disease who often have concomitant diseases,32 suboptimal compliance with therapy, or the clinical inertia of the treating physicians.33,34
Some methodological aspects deserve consideration. A strength of using the registry was the consecutive inclusion of unselected patients representative of routine clinical care with close follow-up, resulting in a small number of missing values. In terms of limitations, selection bias may have occurred, because centers participating voluntarily in a registry are more likely to have an interest and probably increased knowledge of the research question. Overall, data quality in a registry is not as high as in a study under Good Clinical Practice rules, and audits with source data verification were performed in a limited number of centers only. With regard to education, categories were relatively crude, given that high education comprised a wide spectrum and variation of years at school or university. Owing to the post-hoc approach, we did not have data on certain characteristics reported in other analyses.
Conclusion
The present analysis provides an overview of the characteristics, treatment, and risk factor control in the cardiac rehabilitation setting in Germany. Patients treated in cardiac rehabilitation with different levels of education did not differ relevantly in demographics, but did differ in some clinical aspects. Nonetheless, similar control rates could be achieved for low versus high education level. Thus, with respect to the ultimate goal of cardiac rehabilitation, ie, optimal control of risk factors, education background does not seem to have an important role. However, control of cardiovascular risk factors is not yet adequate and should be improved in the future.
Acknowledgment
The TROL registry is maintained in cooperation with the German Society for Prevention and Rehabilitation.
Footnotes
Disclosure
The registry and the present analysis were supported by MSD Sharp and Dohme GmbH, Munich-Haar, Germany. CJ and BK are full-time employees of MSD, and KB was an employee when the study was in the field phase. The other authors declare no conflict of interest related to the registry. We acknowledge the input of D Pittrow, Institute for Clinical Pharmacology, Dresden, for interpretation of the data and various sections of the manuscript.
References
- 1.Karoff M, Held K, Bjarnason-Wehrens B. Cardiac rehabilitation in Germany. Eur J Cardiovasc Prev Rehabil. 2007;14(1):18–27. doi: 10.1097/HJR.0b013e3280128bde. [DOI] [PubMed] [Google Scholar]
- 2.Oldridge NB, Guyatt GH, Fischer ME, Rimm AA. Cardiac rehabilitation after myocardial infarction. Combined experience of randomized clinical trials. JAMA. 1988;260(7):945–950. [PubMed] [Google Scholar]
- 3.Ades PA. Cardiac rehabilitation and secondary prevention of coronary heart disease. N Engl J Med. 2001;345(12):892–902. doi: 10.1056/NEJMra001529. [DOI] [PubMed] [Google Scholar]
- 4.O’Connor GT, Buring JE, Yusuf S, et al. An overview of randomized trials of rehabilitation with exercise after myocardial infarction. Circulation. 1989;80(2):234–244. doi: 10.1161/01.cir.80.2.234. [DOI] [PubMed] [Google Scholar]
- 5.Jünger C, Rauch B, Schneider S, et al. Effect of early short-term cardiac rehabilitation after acute ST-elevation and non-ST-elevation myocardial infarction on 1-year mortality. Curr Med Res Opin. 2010;26(4):803–811. doi: 10.1185/03007991003604216. [DOI] [PubMed] [Google Scholar]
- 6.Barcelo MA, Saez M, de Tuero GC. Individual socioeconomic factors conditioning cardiovascular disease risk. Am J Hypertens. 2009;22(10):1085–1095. doi: 10.1038/ajh.2009.146. [DOI] [PubMed] [Google Scholar]
- 7.McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Differences in control of cardiovascular disease and diabetes by race, ethnicity, and education: US trends from 1999 to 2006 and effects of medicare coverage. Ann Intern Med. 2009;150(8):505–515. doi: 10.7326/0003-4819-150-8-200904210-00005. [DOI] [PubMed] [Google Scholar]
- 8.Jackson L, Leclerc J, Erskine Y, Linden W. Getting the most out of cardiac rehabilitation: a review of referral and adherence predictors. Heart. 2005;91(1):10–14. doi: 10.1136/hrt.2004.045559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Willich SN, Muller-Nordhorn J, Kulig M, et al. Cardiac risk factors, medication, and recurrent clinical events after acute coronary disease; a prospective cohort study. Eur Heart J. 2001;22(4):307–313. doi: 10.1053/euhj.2000.2294. [DOI] [PubMed] [Google Scholar]
- 10.Völler H, Reibis R, Pittrow D, et al. Secondary prevention of diabetic patients with coronary artery disease in cardiac rehabilitation: risk factors, treatment and target level attainment. Curr Med Res Opin. 2009;25(4):879–890. doi: 10.1185/03007990902801360. [DOI] [PubMed] [Google Scholar]
- 11.Bestehorn K, Wegscheider K, Voller H. Contemporary trends in cardiac rehabilitation in Germany: patient characteristics, drug treatment, and risk-factor management from 2000 to 2005. Eur J Cardiovasc Prev Rehabil. 2008;15(3):312–318. doi: 10.1097/HJR.0b013e3282f40e14. [DOI] [PubMed] [Google Scholar]
- 12.Fernandez RS, Salamonson Y, Griffiths R, Juergens C, Davidson P. Sociodemographic predictors and reasons for participation in an outpatient cardiac rehabilitation programme following percutaneous coronary intervention. Int J Nurs Pract. 2008;14(3):237–242. doi: 10.1111/j.1440-172X.2008.00685.x. [DOI] [PubMed] [Google Scholar]
- 13.Dunlay SM, Witt BJ, Allison TG, et al. Barriers to participation in cardiac rehabilitation. Am Heart J. 2009;158(5):852–859. doi: 10.1016/j.ahj.2009.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ades PA, Waldmann ML, Polk DM, Coflesky JT. Referral patterns and exercise response in the rehabilitation of female coronary patients aged greater than or equal to 62 years. Am J Cardiol. 1992;69(17):1422–1445. doi: 10.1016/0002-9149(92)90894-5. [DOI] [PubMed] [Google Scholar]
- 15.Evenson KR, Rosamond WD, Luepker RV. Predictors of outpatient cardiac rehabilitation utilization: the Minnesota Heart Surgery Registry. J Cardiopulm Rehabil. 1998;18(3):192–198. doi: 10.1097/00008483-199805000-00002. [DOI] [PubMed] [Google Scholar]
- 16.Grace SL, Abbey SE, Shnek ZM, Irvine J, Franche RL, Stewart DE. Cardiac rehabilitation II: referral and participation. Gen Hosp Psychiatry. 2002;24(3):127–134. doi: 10.1016/s0163-8343(02)00179-2. [DOI] [PubMed] [Google Scholar]
- 17.Missik E. Women and cardiac rehabilitation: accessibility issues and policy recommendations. Rehabil Nurs. 2001;26(4):141–147. doi: 10.1002/j.2048-7940.2001.tb01937.x. [DOI] [PubMed] [Google Scholar]
- 18.Schuster PM, Wright C, Tomich P. Gender differences in the outcomes of participants in home programs compared with those in structured cardiac rehabilitation programs. Rehabil Nurs. 1995;20(2):93–101. doi: 10.1002/j.2048-7940.1995.tb01603.x. [DOI] [PubMed] [Google Scholar]
- 19.Yates BC, Braklow-Whitton JL, Agrawal S. Outcomes of cardiac rehabilitation participants and nonparticipants in a rural area. Rehabil Nurs. 2003;28(2):57–63. doi: 10.1002/j.2048-7940.2003.tb02030.x. [DOI] [PubMed] [Google Scholar]
- 20.Harlan WR, 3rd, Sandler SA, Lee KL, Lam LC, Mark DB. Importance of baseline functional and socioeconomic factors for participation in cardiac rehabilitation. Am J Cardiol. 1995;76(1):36–39. doi: 10.1016/s0002-9149(99)80797-8. [DOI] [PubMed] [Google Scholar]
- 21.Heald CL, Fowkes FG, Murray GD, Price JF. Risk of mortality and cardiovascular disease associated with the ankle-brachial index: Systematic review. Atherosclerosis. 2006;189(1):61–69. doi: 10.1016/j.atherosclerosis.2006.03.011. [DOI] [PubMed] [Google Scholar]
- 22.Diehm C, Allenberg JR, Pittrow D, et al. Mortality and vascular morbidity in older adults with asymptomatic versus symptomatic peripheral artery disease. Circulation. 2009;120(21):2053–2061. doi: 10.1161/CIRCULATIONAHA.109.865600. [DOI] [PubMed] [Google Scholar]
- 23.Balady GJ, Ades PA, Comoss P, et al. Core components of cardiac rehabilitation/secondary prevention programs: A statement for healthcare professionals from the American Heart Association and the American Association of Cardiovascular and Pulmonary Rehabilitation Writing Group. Circulation. 2000;102(9):1069–1073. doi: 10.1161/01.cir.102.9.1069. [DOI] [PubMed] [Google Scholar]
- 24.Expert Panel on Detection and Evaluation Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) JAMA. 2001;285(19):2486–2497. doi: 10.1001/jama.285.19.2486. [DOI] [PubMed] [Google Scholar]
- 25.Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2571. doi: 10.1001/jama.289.19.2560. [DOI] [PubMed] [Google Scholar]
- 26.Ryden L, StandL E, Bartnik M, et al. Guidelines on diabetes, pre- diabetes, and cardiovascular diseases: executive summary: The Task Force on Diabetes and Cardiovascular Diseases of the European Society of Cardiology (ESC) and of the European Association for the Study of Diabetes (EASD) Eur Heart J. 2007;28(1):88–136. doi: 10.1093/eurheartj/ehl260. [DOI] [PubMed] [Google Scholar]
- 27.[No authors listed] Cardiac rehabilitation programs A statement for healthcare professionals from the American Heart Association. Circulation. 1994;90(3):1602–1610. doi: 10.1161/01.cir.90.3.1602. [DOI] [PubMed] [Google Scholar]
- 28.Gitt AK, Junger C, Smolka W, Bestehorn K. Prevalence and overlap of different lipid abnormalities in statin-treated patients at high cardiovascular risk in clinical practice in Germany. Clin Res Cardiol. 2010;99(11):723–733. doi: 10.1007/s00392-010-0177-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Böhler S, Scharnagl H, Freisinger F, et al. Unmet needs in the diagnosis and treatment of dyslipidemia in the primary care setting in Germany. Atherosclerosis. 2007;190(2):397. doi: 10.1016/j.atherosclerosis.2006.02.025. [DOI] [PubMed] [Google Scholar]
- 30.Pittrow D, Stalla GK, Zeiher AM, et al. Prevalence, drug treatment and metabolic control of diabetes mellitus in primary care. Med Klin (Munich) 2006;101(8):635–644. doi: 10.1007/s00063-006-1093-x. German. [DOI] [PubMed] [Google Scholar]
- 31.Sharma A, Wittchen H, Kirch W, et al. High prevalence and poor control of hypertension in primary care: cross sectional study. J Hypertens. 2004;22(3):479–486. doi: 10.1097/00004872-200403000-00009. [DOI] [PubMed] [Google Scholar]
- 32.Pittrow D, Kirch W, Bramlage P, et al. Patterns of antihypertensive drug utilization in primary care. Eur J Clin Pharmacol. 2004;60(2):135–142. doi: 10.1007/s00228-004-0731-6. [DOI] [PubMed] [Google Scholar]
- 33.Phillips L, Branch W, Cook C, et al. Clinical inertia. Ann Intern Med. 2001;135(9):825–834. doi: 10.7326/0003-4819-135-9-200111060-00012. [DOI] [PubMed] [Google Scholar]
- 34.Ziemer DC, Doyle JP, Barnes CS, et al. An intervention to overcome clinical inertia and improve diabetes mellitus control in a primary care setting: improving primary care of African Americans with diabetes (IPCAAD) 8. Arch Intern Med. 2006;166(5):507–513. doi: 10.1001/archinte.166.5.507. [DOI] [PubMed] [Google Scholar]