Abstract

Aims

The aim of this study was to assess the impact of myocardial infarction (MI) on health-related quality of life (HRQL) in MI survivors measured by EuroQol (EQ-5D) and to compare it with the general population.

Methods and results

A follow-up study of all MI survivors included in the MONICA/KORA registry was performed. About 2950 (67.1%) patients responded. Moderate or severe problems were most frequent in EQ-5D dimension pain/discomfort (55.0%), anxiety/depression (29.2%), and mobility (27.9%). Mean EQ VAS score was 65.8 (SD 18.5). Main predictors of lower HRQL included older age, diabetes, increasing body mass index, current smoking, and experience of re-infarction. Type of revascularizational treatment showed no impact on HRQL. Compared with the general population, adjusted EQ VAS was 6.2 (95% confidence interval 3.4–8.9) points lower in 45-year-old MI patients converging with growing age up to the age of 80. With regard to HRQL dimensions, MI survivors had a significantly higher risk of incurring problems in the dimension pain/discomfort, usual activities, and especially in anxiety/depression which was more pronounced in younger age. Mobility was the single dimension, in which MI showed an inverse effect.

Conclusion

MI is combined with significant reduction in HRQL compared with the general population. The main impairments occur in the dimension pain/discomfort, usual activities, and particularly anxiety/depression. The relative impairment decreases with higher ages.

Introduction

Acute myocardial infarction (MI) is among the leading causes of death in Western countries and represents a significant burden on the health-care system and society.1 Measurement of health outcomes after MI has traditionally been focused on clinical outcomes such as survival and event-free life span. In recent years, health-related quality of life (HRQL) is increasingly used in medical research and accepted by physicians as a complementary measure of the medical effectiveness of interventions.2,3 Summary measures of HRQL are used in the calculation of quality-adjusted life years as a major outcome in the economic evaluation of new health technologies, which has a growing influence on allocation decisions in the health-care sector.4

Given the growing importance of HRQL in general and specifically in MI patients, there is a need to identify which characteristics are related to impaired HRQL in cross-sectional as well as in cohort studies. Identification of these predictors would allow physicians to identify, at the time of admission, those patients who are likely to report a worse HRQL, thus permitting appropriate risk stratification.5,6 Furthermore, knowledge of influencing factors of HRQL would allow to adjust the outcomes of alternative interventions accordingly when different treatments are compared. This can also be taken as an important input in health economic decision models, in which the progression of disease is modelled as a sequence of distinct disease states characterized by state specific cost and utility weights.7

Several tools are available to assess HRQL. Whereas disease-specific instruments such as the Seattle Angina Questionnaire8 or the Mac New9 focus on particular symptoms of heart disease, other ‘generic’ questionnaires apply equally to a range of diseases and enable comparison between these and normative, population-based scores. EQ-5D is one of the most frequently used generic measures in the context of health-care decision-making.10

Few studies investigated the impact of MI on HRQL in comparison with normal or unaffected population using EQ-5D.11–14 However, the samples under study tended to be rather limited in size and had a short-term perspective12–14 or were focused on a certain treatment group of patients.11

The aim of this study was to determine the HRQL as measured by EQ-5D in a large unselected cohort of survivors of MI. We wanted to assess in which dimensions MI survivors exhibit problems and which patient characteristics, events, or interventions affected the current health state. Furthermore, we aimed to assess whether survivors' HRQL differed from the general population.

Methods

Patients

Patients for this study were drawn from the MONICA/KORA (Cooperative Health research in the region of Augsburg/Monitoring trends and determinants of cardiovascular disease) Myocardial Infarction Registry. This is a population-based registry that comprises all hospitalized cases of acute non-fatal MI at least surviving 24 h and coronary deaths occurring in inhabitants of a defined study region—the city of Augsburg and the two surrounding counties—who are aged between 25 and 74 years. The registry has been described in detail elsewhere.15,16 Between August and October 2006, a postal questionnaire for self-completion was sent to all registry patients who had suffered initial MI between 1985 and 2004, who had consented to recontact, and were known to be alive by time of the last mortality follow-up (n = 4394). There were no further exclusion criteria. In the case of non-response, patients were reminded by mail.

In addition to questions on comorbidities, cardiovascular risk factors, and current medication, the questionnaire consisted of questions concerning demographic characteristics as well as the German version of EQ-5D. Further medical data about the primary and recurrent MIs were derived from the database of the MI registry.

To describe the impact of a previous MI on HRQL in relation to the general population, the results of the MI registry data were compared with data from the European Study of the Epidemiology of Mental disorders (ESEMeD).17 From this population-based survey, which was conducted in personal interviews, we obtained the EQ-5D ratings along with socio-demographic characteristics from the 1902 German participants within the relevant age range of 45–93. As this study aimed to include a representative sample from the general population, it also included patients with previous MI as well as other diseases.

EQ-5D

EQ-5D is an instrument for describing and valuing health states. It comprises five questions asking for the current health state in five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression.18 The categories of the response offer three levels: no, moderate, and extreme problems.

The EQ-5D health states can be combined into a single index using valuation formulas that are based on the health state valuation exercises of representative population samples. The EQ-5D index formula applied in this study has been suggested by Dolan,19 an algorithm that is internationally highly accepted. A formula for the German population (based on choice-based methods) has been recently suggested, but is still based on a rather small sample size.20 This first descriptive part of the questionnaire was followed by EQ VAS, a visual analogue scale that ranges from 0 (worst imaginable health state) to 100 (best imaginable health state). Whereas the subjective rating of their own health state on the VAS reflects the respondents' personal valuation, the EQ-5D index reflects health state valuation based on general population preferences. The suitability of EQ-5D for cardiac patients has recently been demonstrated.21

Statistical analysis

For the analysis of categorical variables in the comparison of responders and non-responders as well as different subgroups, χ2 test was used. Student's t-test was applied for the analysis of group differences within continuous variables. Owing to small sample size in young age categories, these comparisons excluded patients younger than 45 years, when age was categorized in 10 years age classes. Parametric 95% confidence intervals were calculated for the EQ-5D scores. Multiple linear regression was applied to evaluate the influence of demographic and medical characteristics including age, sex, education, body mass index (BMI), risk factors, time since MI, and comorbidities on the EQ VAS score. We chose this outcome for regression analysis as it provided a favourable statistical distribution when compared with the EQ-5D index. As there was no linear association between time since MI and HRQL, we chose to categorize time since MI into three categories. These were aimed at reflecting the graphically assessed relationship between the two variables as displayed in smoothed average and partial residual plots.

A small number of other variables were tested for inclusion into the model, but then discarded because of collinearity (professional status) or lack of statistical relevance (complications during acute hospital care and maximum creatinine phosphokinase value in acute hospitalization). To assess the impact of age and sex and their respective interaction, we had included an interaction term between age (continuous) and sex in a separate analysis, which yielded no significant effects and did not improve the model. Regression diagnostic included residual plots to check for heteroscedasticity or leverage points.

To compare HRQL of MI survivors and general population, regression analyses were run on the pooled sample for available covariates of age, sex, education, current smoking, and BMI. Possible interactions between MI survivor status and sex as well as those between MI survivor status and age were explored in separate analyses. Only the significant interactions were included in the final models. For VAS, we employed a linear model that eventually included a group×age interaction. Considering the difference in problem frequency across EQ-5D dimensions between general population and MI survivors, multiple logistic regression models were used also incorporating a group×age interaction. For different ages, the effect of the MI survivor status on the problem rate was calculated using the parameter estimates and their covariance from the logistic regression. Interactions were tested with a likelihood ratio test, considering a P-value of 0.05 for the linear model as significant, and in the logistic model an adjusted P-value of 0.01 accounting for the number dimensions. All P-values reported are two sided.

Statistical analysis was performed using the software package SAS 9.1 (SAS Institute, Cary, NC, USA).

Ethics

The study complies with the Declaration of Helsinki, it was approved by the local Ethics Committee, and informed consent has been obtained from the subjects.

Results

According to follow-up of vital status of patients included, 4570 registered persons with MI were alive at the end of 2005 and were mailed a questionnaire in August 2006. Of these, 72 had died by the time of the survey, 13 had moved away, and 91 had not consented to follow-up yielding a gross sample of n = 4394. In addition, from 1444 patients, no questionnaire could be obtained after mailing a reminding letter. A total of 2950 patients returned the questionnaire, yielding a response rate of 67.1%. The respondents were predominantly male (79.3%), with a mean age of 68 ranging from 32 to 93. Angioplasty with (26%) or without stent (14%) was the most common intervention recorded for the MI treatment in the registry database. The median time since first MI was 7.4 years (interquartile range 8.0).

Compared with the responding sample, non-responders were 1 year younger and more likely to be of female sex and of lower education (Table 1). There were also some differences with respect to clinical characteristics as the durations since first MI as well as most recent MI were slightly shorter.

Table 1

Socio-demographic and clinical characteristics of responders and non-responders

 Responder (n = 2950) Non-responder (n = 1444) P-value* 
 Mean (SD)/n (%) Mean (SD)/n (%)  
Age (years) 68.0 (9.6) 67.0 (11.2) 0.002** 
Male gender 2341 (79.3%) 1212 (74.8%) 0.004* 

 
Education 
 Primary school 1932 (65.5%) 898 (55.4%) <0.001* 
 Medium level 370 (12.5%) 164 (10.1%) 
 University qualifying degree 333 (11.3%) 135 (8.3%) 
 No 315 (10.7%) 423 (26.1%) 
Time since recent cardiac infarction (years) 8.56 (5.3) 8.92 (5.3) 0.028** 
Time since initial infarction (years) 9.1 (5.3) 9.5 (5.7) 0.031** 
Number of infarctions recorded 1.1 (0.4) 1.1 (0.4) 0.989** 

 
Treatment of revascularization at first event 
 CABG 398 (13.5%) 194 (12.0%) <0.001* 
 Angioplasty with stent 766 (26.0%) 357 (22.0%) 
 Angioplasty without stent 413 (14.0%) 240 (14.8%) 
 Pharmaceutical reperfusion 526 (17.8%) 268 (16.5%) 
 No reperfusion treatment 847 (28.7%) 561 (34.6%) 
Residence: urban (vs. rural) 1344 (45.6%) 881 (54.4%) <0.001* 
Origin: German (vs. non-German) 2829 (96.0%) 1408 (87.0%) <0.001* 
 Responder (n = 2950) Non-responder (n = 1444) P-value* 
 Mean (SD)/n (%) Mean (SD)/n (%)  
Age (years) 68.0 (9.6) 67.0 (11.2) 0.002** 
Male gender 2341 (79.3%) 1212 (74.8%) 0.004* 

 
Education 
 Primary school 1932 (65.5%) 898 (55.4%) <0.001* 
 Medium level 370 (12.5%) 164 (10.1%) 
 University qualifying degree 333 (11.3%) 135 (8.3%) 
 No 315 (10.7%) 423 (26.1%) 
Time since recent cardiac infarction (years) 8.56 (5.3) 8.92 (5.3) 0.028** 
Time since initial infarction (years) 9.1 (5.3) 9.5 (5.7) 0.031** 
Number of infarctions recorded 1.1 (0.4) 1.1 (0.4) 0.989** 

 
Treatment of revascularization at first event 
 CABG 398 (13.5%) 194 (12.0%) <0.001* 
 Angioplasty with stent 766 (26.0%) 357 (22.0%) 
 Angioplasty without stent 413 (14.0%) 240 (14.8%) 
 Pharmaceutical reperfusion 526 (17.8%) 268 (16.5%) 
 No reperfusion treatment 847 (28.7%) 561 (34.6%) 
Residence: urban (vs. rural) 1344 (45.6%) 881 (54.4%) <0.001* 
Origin: German (vs. non-German) 2829 (96.0%) 1408 (87.0%) <0.001* 

2 test.

**t-test.

As the number of MI survivors <45 years was small (n = 45), these patients were excluded from the analysis with respect to age groups. Of 2905 study participants aged ≥45 years, 2753 had a complete EQ-5D health status and 2651 had a complete VAS. Sixteen patients did not answer any EQ-5D dimension at all, and 152 had missing values in one or more health items.

EQ-5D health states

Whereas 36.4% of the respondents stated no problems in any of the five EQ-5D dimensions, 55.5% found minor problems in at least one dimension and 8.1% rated the problems in at least one dimension as severe. The dimension in which moderate or severe problems were most frequently stated was pain/discomfort (55.0%), followed by anxiety/depression (29.2%), mobility (27.9%), and usual activities (25.9%). Much less patients reported limitations in the dimension self-care (8.2%).

Across almost all five dimensions, there were considerable differences in the frequency of problems with respect to age even if sex was accounted for (Table 2). With the exception of anxiety/depression, in all other dimensions, the fraction of respondents stating moderate or severe problems increased in higher age classes, most noticeably in the highest age class of 75 and older (P < 0.001 in all dimensions, Cochran–Armitage test for trend). Sex-specific differences could be observed across almost all age groups and in all dimensions, except ‘self-care’ where problem frequency of both sexes coincided. In the other dimensions, problem frequency was equal or slightly in favour of females up to 64, whereas in the upper two age classes, the problems were typically higher in females than in males, with a strong increase of problem rate in the highest age class.

Table 2

Age- and sex-specific proportions of moderate or severe problems in EQ-5D dimensions as well as mean EQ-5D index and EQ VAS score in MI survivors

EQ-5D dimension Age class
 
Total (%) 
 45–54 (%) 55–64 (%) 65–74 (%) ≥75 (%)  
Mobility 
 All 15% 21% 25% 41% 27% 
 Male 14% 22% 23% 38% 26% 
 Female 15% 15% 33% 47% 33%a 

 
Self-care 
 All 4% 5% 7% 14% 8% 
 Male 4% 5% 7% 13% 7% 
 Female 4% 3% 9% 16% 10%b 

 
Usual activities 
 All 26% 24% 21% 34% 25% 
 Male 26% 25% 19% 31% 24% 
 Female 27% 17% 28% 42% 30%b 

 
Pain/discomfort 
 All 47% 54% 52% 61% 54% 
 Male 48% 54% 50% 57% 53% 
 Female 42% 53% 61% 71% 62%a 

 
Anxiety/depression 
 All 36% 34% 25% 27% 29% 
 Male 37% 33% 22% 25% 27% 
 Female 31% 41% 36% 34% 36%a 
EQ-5D scores Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) 

 
EQ-5D indexc 
 All 87.7 (15.3) 87.2 (14.4) 87.2 (15.5) 84.2 (15.8) 86.5 (15.4) 
 Male 87.8 (14.5) 86.9 (14.9) 88.3 (14.5) 85.3 (15.4) 87.1 (14.8) 
 Female 86.9 (21.0) 88.7 (11.7) 83.1 (18.4) 80.9 (16.7) 83.7a (17.0) 

 
EQ VAS 
 All 69.0 (19.1) 67.7 (17.6) 66.9 (18.1) 60.7 (18.9) 65.7 (18.5) 
 Male 69.0 (18.6) 67.7 (17.5) 67.8 (17.8) 61.8 (19.0) 66.4 (18.3) 
 Female 69.1 (22.6) 67.7 (18.2) 63.3 (18.6) 57.5 (18.2) 62.6a (19.0) 
Total (n)d      

 
EQ-5D indexc 
 All 233 652 1151 717 2753 
 Male 207 540 918 540 2205 
 Female 26 112 233 177 548 
EQ-5D dimension Age class
 
Total (%) 
 45–54 (%) 55–64 (%) 65–74 (%) ≥75 (%)  
Mobility 
 All 15% 21% 25% 41% 27% 
 Male 14% 22% 23% 38% 26% 
 Female 15% 15% 33% 47% 33%a 

 
Self-care 
 All 4% 5% 7% 14% 8% 
 Male 4% 5% 7% 13% 7% 
 Female 4% 3% 9% 16% 10%b 

 
Usual activities 
 All 26% 24% 21% 34% 25% 
 Male 26% 25% 19% 31% 24% 
 Female 27% 17% 28% 42% 30%b 

 
Pain/discomfort 
 All 47% 54% 52% 61% 54% 
 Male 48% 54% 50% 57% 53% 
 Female 42% 53% 61% 71% 62%a 

 
Anxiety/depression 
 All 36% 34% 25% 27% 29% 
 Male 37% 33% 22% 25% 27% 
 Female 31% 41% 36% 34% 36%a 
EQ-5D scores Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) 

 
EQ-5D indexc 
 All 87.7 (15.3) 87.2 (14.4) 87.2 (15.5) 84.2 (15.8) 86.5 (15.4) 
 Male 87.8 (14.5) 86.9 (14.9) 88.3 (14.5) 85.3 (15.4) 87.1 (14.8) 
 Female 86.9 (21.0) 88.7 (11.7) 83.1 (18.4) 80.9 (16.7) 83.7a (17.0) 

 
EQ VAS 
 All 69.0 (19.1) 67.7 (17.6) 66.9 (18.1) 60.7 (18.9) 65.7 (18.5) 
 Male 69.0 (18.6) 67.7 (17.5) 67.8 (17.8) 61.8 (19.0) 66.4 (18.3) 
 Female 69.1 (22.6) 67.7 (18.2) 63.3 (18.6) 57.5 (18.2) 62.6a (19.0) 
Total (n)d      

 
EQ-5D indexc 
 All 233 652 1151 717 2753 
 Male 207 540 918 540 2205 
 Female 26 112 233 177 548 

aP < 0.001 (χ2 and t-test for sex-specific differences in total sample).

bP < 0.01.

cEQ-5D index score suggested by Dolan.19

dSample sizes for EQ VAS were slightly smaller.

EQ VAS and EQ-5D index scores

For the overall sample, the mean EQ VAS score was 65.8 (SD 18.5) and the EQ-5D index score was 86.5 (SD 15.3). In spite of the discrepancy in the absolute level, the difference between males and females was almost the same, looking at both measures (EQ VAS, 3.84 difference, 95% CI 2.07–5.61; EQ-5D index score, difference 3.46, 95% CI 2.03–4.89). Stratifying for age and sex showed almost consistently lower scores for females when compared with their male counterparts (Table 2), with a widening gap in higher age classes. Whereas health status measured by the EQ VAS and the EQ-5D index scores decreased only slightly in the age groups up to 75, it deteriorated significantly for participants of higher ages (Figure 1).

Figure 1

Mean VAS and EQ-5D index by age-group in MI survivors and general population. Means by age group adjusted for difference in sex distribution using least squares means (SAS: LSMEANS), whiskers represent parametric 95% confidence intervals.

Figure 1

Mean VAS and EQ-5D index by age-group in MI survivors and general population. Means by age group adjusted for difference in sex distribution using least squares means (SAS: LSMEANS), whiskers represent parametric 95% confidence intervals.

Predictors of HRQL

Results of the linear regression on EQ VAS are shown in Table 3. Age was a strong predictor over the total age range when entering linearly into the model (P = 0.003). As the bivariate relation of age and HRQL featured a considerable non-linearity, especially in higher ages (>75), we fitted a piecewise linear model with a breakpoint at 75 years, allowing a change in the slope. Both coefficients were significant and pointed to a remarkable decrease in HRQL in age groups over 75. The yearly decline in HRQL increased in this age group from 0.16 to 1.07. The remaining socio-demographic characteristics of sex and education also showed significant influence as males tended to rate their health on average 3.3 points higher than females and low-educated individuals 3 points lower than highly educated individuals. Considering the behavioural variables, negative relationship was found with increasing BMI, thus indicating a drop by 1.5 points with an increase of 5 BMI points. Whereas current smoking lowered VAS significantly (P = 0.008), a history of smoking had no significant influence on HRQL. Within the clinical characteristics, we found the factors that had the greatest impact on HRQL as a diagnosis of diabetes reduced HRQL by 7.2 points and an experience of re-infarction by 5.4 points. Considering time since recent event, we found no significant effect in the model. The procedures that were recorded as primary revascularization treatment did not exhibit significant impact when compared with the no revascularization treatment option.

Table 3

Predictors of health-related quality of life (EQ VAS score) in myocardial infarction survivorsa

Parameter Estimate Standard error t-value Pr > |t95% confidence limits
 
Intercept 89.132 4.55 19.59 <0.001 80.21 98.06 
Age (years) −0.156 0.05 −3.00 0.003 −0.26 −0.05 
(Age−75) (years) if age ≥75 −0.918 0.20 −4.53 <0.001 −1.32 −0.52 
Female sex −3.29 0.97 −3.40 <0.001 −5.18 −1.39 
Low educationb −2.97 0.81 −3.65 <0.001 −4.57 −1.38 
Current smoker −3.50 1.31 −2.68 0.008 −6.07 −0.94 
Ex-smoker −0.57 0.86 −0.67 0.50 −2.25 1.11 
BMI −0.31 0.09 −3.60 <0.001 −0.48 −0.14 

 
History of myocardial infarction 
 Recent MI <5 years       
 Recent MI 5–10 years ago 0.46 1.01 0.45 0.650 −1.53 2.44 
 Recent MI >10 years ago −1.82 1.22 −1.49 0.136 −4.21 0.57 
Diabetes −7.17 0.88 −8.18 <0.001 −8.89 −5.45 
Reinfarction −5.41 1.33 −4.08 <0.001 −8.02 −2.81 

 
Reperfusion 
 No reperfusion therapy at all       
 Reperfusion: angioplasty with stent −0.18 1.20 −0.15 0.884 −2.53 2.18 
 Reperfusion: angioplasty without stent −0.23 1.22 −0.19 0.853 −2.61 2.16 
 Reperfusion: CABG 0.47 1.29 0.36 0.716 −2.06 2.99 
 Reperfusion: pharmaceutical reperfusion −0.11 1.10 −0.10 0.922 −2.27 2.06 
Parameter Estimate Standard error t-value Pr > |t95% confidence limits
 
Intercept 89.132 4.55 19.59 <0.001 80.21 98.06 
Age (years) −0.156 0.05 −3.00 0.003 −0.26 −0.05 
(Age−75) (years) if age ≥75 −0.918 0.20 −4.53 <0.001 −1.32 −0.52 
Female sex −3.29 0.97 −3.40 <0.001 −5.18 −1.39 
Low educationb −2.97 0.81 −3.65 <0.001 −4.57 −1.38 
Current smoker −3.50 1.31 −2.68 0.008 −6.07 −0.94 
Ex-smoker −0.57 0.86 −0.67 0.50 −2.25 1.11 
BMI −0.31 0.09 −3.60 <0.001 −0.48 −0.14 

 
History of myocardial infarction 
 Recent MI <5 years       
 Recent MI 5–10 years ago 0.46 1.01 0.45 0.650 −1.53 2.44 
 Recent MI >10 years ago −1.82 1.22 −1.49 0.136 −4.21 0.57 
Diabetes −7.17 0.88 −8.18 <0.001 −8.89 −5.45 
Reinfarction −5.41 1.33 −4.08 <0.001 −8.02 −2.81 

 
Reperfusion 
 No reperfusion therapy at all       
 Reperfusion: angioplasty with stent −0.18 1.20 −0.15 0.884 −2.53 2.18 
 Reperfusion: angioplasty without stent −0.23 1.22 −0.19 0.853 −2.61 2.16 
 Reperfusion: CABG 0.47 1.29 0.36 0.716 −2.06 2.99 
 Reperfusion: pharmaceutical reperfusion −0.11 1.10 −0.10 0.922 −2.27 2.06 

aLinear multiple regression on EQ VAS score, R2 = 0.094, R2adj = 0.089.

bReference: secondary/medium education and University (entry) qualification.

Comparison with general population

Considering the EQ-5D scores by age group solely adjusted for difference in sex distribution as displayed in Figure 1, a large significant difference could be observed in younger age groups, indicating an impaired HRQL in MI survivors compared with the general population [adjusted difference with 95% CI over increasing age groups: −6.0 (−8.2 to −3.9); −5.1 (−6.6 to −3.5); −2.5 (−4.0 to −1.0); −0.7 (−2.7 to 1.3)]. This finding was similarly seen regardless of looking at the EQ VAS or EQ-5D index. As the HRQL scores declined with increasing age much stronger in the normal population than in MI survivors, the gap virtually disappeared in the highest age classes, in which no significant difference could be observed anymore in any of the two measures.

The linear regression on VAS based on the pooled sample using the available covariates as well as a group×age interaction indicated significantly lower VAS in MI survivors compared with the general population in cohorts of younger age (Table 4). As an example, a 45-year-old MI survivor would, on average, report 6.2 (95% CI 3.4–8.9) points lower HRQL than a matched member of the normal population. This difference would decrease year by year, yielding identical HRQL scores by the age of 80.

Table 4

Regression-adjusted comparison of myocardial infarction survivors and general population

Dimension Age Odds ratio MI survivor
 
P-value for interaction MI survivor × age 
  Estimate 95% CI
 
 
Mobilitya 50 0.688 0.502 0.943 0.4812 
60 0.651 0.533 0.796 
70 0.616 0.52 0.729 
80 0.583 0.453 0.751 
Self-carea 50 1.303 0.66 2.569 0.3818 
60 1.145 0.739 1.774 
70 1.006 0.746 1.357 
80 0.884 0.597 1.311 
Usual activitiesa 50 2.408 1.734 3.345 <0.0001 
60 1.719 1.386 2.133 
70 1.227 1.012 1.489 
80 0.876 0.66 1.163 
Pain/discomforta 50 2.036 1.58 2.624 0.0754 
60 1.811 1.514 2.165 
70 1.61 1.342 1.933 
80 1.432 1.102 1.86 
Anxiety/depressiona 50 13.118 8.903 19.33 0.0071 
60 9.694 7.498 12.533 
70 7.163 5.413 9.48 
80 5.293 3.432 8.165 

 
 Variable
 
Parameter estimates
 
95% CI
 
P-value
 
EQ VASb Age −0.528 −0.608 −0.448 <0.001 
Female sex −1.892 −3.148 −0.635 0.003 
MI survivor −14.364 −21.906 −6.822 <0.001 
MI survivor × age 0.180 0.066 0.294 0.002 
Dimension Age Odds ratio MI survivor
 
P-value for interaction MI survivor × age 
  Estimate 95% CI
 
 
Mobilitya 50 0.688 0.502 0.943 0.4812 
60 0.651 0.533 0.796 
70 0.616 0.52 0.729 
80 0.583 0.453 0.751 
Self-carea 50 1.303 0.66 2.569 0.3818 
60 1.145 0.739 1.774 
70 1.006 0.746 1.357 
80 0.884 0.597 1.311 
Usual activitiesa 50 2.408 1.734 3.345 <0.0001 
60 1.719 1.386 2.133 
70 1.227 1.012 1.489 
80 0.876 0.66 1.163 
Pain/discomforta 50 2.036 1.58 2.624 0.0754 
60 1.811 1.514 2.165 
70 1.61 1.342 1.933 
80 1.432 1.102 1.86 
Anxiety/depressiona 50 13.118 8.903 19.33 0.0071 
60 9.694 7.498 12.533 
70 7.163 5.413 9.48 
80 5.293 3.432 8.165 

 
 Variable
 
Parameter estimates
 
95% CI
 
P-value
 
EQ VASb Age −0.528 −0.608 −0.448 <0.001 
Female sex −1.892 −3.148 −0.635 0.003 
MI survivor −14.364 −21.906 −6.822 <0.001 
MI survivor × age 0.180 0.066 0.294 0.002 

aEffect of MI survivor status on probability of reporting moderate or severe problem (vs. no problem). Estimates and their CI for exemplary ages calculated on the parameter estimates and their variance and covariance from the logistic model with additional control variables sex, education, BMI, and current smoking.

bLinear regression additional control variables: education, BMI, and current smoking.

Logistic regression models to compare problem frequency of MI survivors with the general population across dimensions reproduced largely the results of the raw rates by age group (Table 4). The interaction between MI survivor status and age was highly significant in the dimensions usual activities and anxiety/depression. Mobility was the single dimension in which MI survivors had a significantly lower risk of incurring medium or severe problems. There was no significant difference between groups in the dimension self-care, whereas in the remaining three dimensions usual activities, pain/discomfort, and anxiety/depression, there was a higher likelihood of reporting problems in MI survivors. In each of these three dimensions, there was a decreasing gap between problem rates of the two groups with increasing age.

Whereas there was no interaction between sex and MI status, females reported consistently higher probability of problems in each of the EQ-5D items [odds ratios for dimensions 1–5: 1.25 (1.07–1.47), 1.22 (0.92–1.62), 1.21 (1.02–1.43), 1.37 (1.18–1.57), and 1.58 (1.30–1.92)].

Discussion

In this cross-sectional study, a cohort of 2950 patients who had experienced MI between 1985 and 2004 in the region of Augsburg was followed up concerning HRQL as well as important cardiological risk factors. To judge the size of impairment, outcomes in terms of the EQ-5D scores were compared with a large representative sample of the German general population.

Whereas just 8% of MI patients reported a severe limitation in one of the five dimensions of EQ-5D, 56% stated a minor limitation in at least one dimension. The most frequent problems could be observed in the dimensions pain/discomfort and anxiety/depression, which was even more pronounced in younger age classes <65 years. This adds to the findings of previous studies that found a strong relationship between incidence of MI and mental problems.22,23

In older age groups, a significant deterioration could be observed in EQ-5D scores as well as in the prevalence of problems in the single dimension. The single most important predictors of HRQL turned out to be the comorbidity diabetes and the occurrence of re-infarction. These findings are in line with the literature,11,24,25 although Beck et al.24 found a positive effect of diabetes on HRQL. The lower HRQL scores with regard to demographic characteristics of sex and education are not very surprising as similar patterns are frequently found in populations independent of their medical indication.26,27

Variations in the initial treatment of MI did not show any impact on HRQL scores in our sample. This contrasts somewhat to the findings of studies assessing short-to-medium-term outcomes at 6 and 12 months after MI. Beck et al.24 found significantly lower HRQL in patients who had received a bypass treatment when compared with non-surgical treatment. Our own result may at least partially be explained by the large time span (median 7.4 years), which had occurred between the cardiac event and follow-up and the fact that no ‘acute’ patients with follow-up <9 months were in the study. It may also be, to some extent, triggered by the fact that the treatment pattern of MI varied significantly within the relatively large time span of 22 years in which the initial MI events in our sample occurred, making it difficult to separate treatment and time effects in a cross-sectional study. It has to be noted that the variables available in our data set explain only a rather small proportion of the variance in HRQL. Bradshaw et al.11 report an R2 of 0.26, but they had concomitant measurements of disease symptoms as explanatory variables which we did not have.

Comparison with the general population

To the best of our knowledge, this study was among the first studies to compare the HRQL as measured by EQ-5D of an MI survivor cohort with a representative sample of the general population. It turned out that adjusted for age and sex and further available covariates, the risk of incurring problems was significantly higher in the dimension pain/discomfort and usual activities and even more so in anxiety/depression, whereas no significant effect could be observed in the dimension self-care and even lower risk of problems in mobility. This unexpected latter result is difficult to explain; one might speculate whether it might in part be explained by effects of measures of secondary prevention such as higher physical activity or whether it could result from selection or information bias.

The magnitude of the effect estimate in the dimension anxiety/depression seems very high, even taking account of the qualitatively corresponding findings in the literature. This very high risk might at least to some extent be explained by the unexpected low problem frequency found within this dimension in the German sample of ESEMeD. The authors argued that the particularly low problem frequency might partly be attributed to the survey methods as participants might be unwilling to state problems in a face-to-face interview, which led the authors to a rather cautious interpretation of these results.26

Looking at global scores significantly lower HRQL was observed with MI in younger ages. This difference decreased with growing age and disappeared in our model by the age of 80 years. This diminishing gap presumably reflects the increasing morbidity in the normal population, a finding that was similarly seen in the analysis of other HRQL scores than EQ-5D.24,28,29 As an alternative explanation, this could also be an effect of time since MI which is moderately correlated with age. However, we investigated this possibility by secondary analyses and found no evidence supporting it.

Limitations

A potential limitation of this study arises from the fact that it is based on a postal survey that always poses the threat of selection bias when response is not complete. With 67%, we reached a reasonable net response rate in the expected magnitude, which allows meaningful investigation.

The analysis of non-respondents showed significant but rather small differences between responders and non-responders with respect to socio-demographic characteristics, which do not indicate severe selection bias.

Further limitations of the study concern the sample selection of MI registry patients living in just one region in southern Germany. Some research has been performed on this issue, and it has been argued that with some restrictions, epidemiological results can be transferred on a national level as the population in the region of Augsburg resembles a fairly good cross-section of the general German population.15

Conclusion

This study showed that MI is combined with a significant and remarkable reduction in HRQL. Compared with the general population, the main impairment occurs in the dimensions pain/discomfort, usual activities, and most notably anxiety/depression. The relative impairment compared with general population decreases with higher ages.

Improving quality of life after MI remains a challenge, which would take measures of secondary prevention, especially against mental problems.

Funding

The KORA research platform and the MONICA Augsburg studies were initiated and financed by the Helmholtz Zentrum München—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education, Science, Research, and Technology and by the State of Bavaria. Since the year 2000, the myocardial infarction registry is co-financed by the German Federal Ministry of Health to provide population-based myocardial infarction morbidity data for the official German Health Report (see www.gbe-bund.de).

Conflict of interest: none declared.

Acknowledgements

We thank all members of the Helmholtz Zentrum München, Institute of Health Economics and Health Care Management, Institute of Epidemiology, and the field staff in Augsburg who were involved in the planning and conduct of the study. Finally, we wish to thank the local health departments for their support.

References

1
British Heart Foundation
BHF Coronary heart disease statistics at www.heartstats.org
 
www.heartstats.org (December 2007)
2
Bullinger
M
Assessing health related quality of life in medicine. An overview over concepts, methods and applications in international research
Restor Neurol Neurosci
 , 
2002
, vol. 
20
 (pg. 
93
-
101
)
3
Leidl
R
Sintonen
H
Abbühl
B
Hoffmann
C
von der Schulenburg
J
König
H
Do physicians accept quality of life and utility measurement? An Austrian, Finnish, and German survey
Eur J Health Econ
 , 
2001
, vol. 
2
 (pg. 
170
-
175
)
4
Weinstein
MC
Siegel
JE
Gold
MR
Kamlet
MS
Russell
LB
Recommendations of the Panel on Cost-effectiveness in Health and Medicine
JAMA
 , 
1996
, vol. 
276
 (pg. 
1253
-
1258
)
5
Michaels
AD
Goldschlager
N
Risk stratification after acute myocardial infarction in the reperfusion era
Prog Cardiovasc Dis
 , 
2000
, vol. 
42
 (pg. 
273
-
309
)
6
NICE (National Institute for Clinical Excellence)
Guide to the Methods of Technology Appraisal
 , 
2004
London
National Institute for Clinical Excellence
 
7
Weinstein
MC
O'Brien
B
Hornberger
J
Jackson
J
Johannesson
M
McCabe
C
Luce
BR
Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices—Modeling Studies
Value Health
 , 
2003
, vol. 
6
 (pg. 
9
-
17
)
8
Spertus
JA
Winder
JA
Dewhurst
TA
Deyo
RA
Prodzinski
J
McDonell
M
Fihn
SD
Development and evaluation of the Seattle Angina Questionnaire: a new functional status measure for coronary artery disease
J Am Coll Cardiol
 , 
1995
, vol. 
25
 (pg. 
333
-
341
)
9
Hofer
S
Lim
L
Guyatt
G
Oldridge
N
The MacNew Heart Disease health-related quality of life instrument: a summary
Health Qual Life Outcomes
 , 
2004
, vol. 
2
 pg. 
3
 
10
EuroQol Group
EuroQol—a new facility for the measurement of health-related quality of life
Health Policy
 , 
1990
, vol. 
16
 (pg. 
199
-
208
)
11
Bradshaw
PJ
Jamrozik
KD
Gilfillan
IS
Thompson
PL
Asymptomatic long-term survivors of coronary artery bypass surgery enjoy a quality of life equal to the general population
Am Heart J
 , 
2006
, vol. 
151
 (pg. 
537
-
544
)
12
Calvert
MJ
Freemantle
N
Cleland
JG
The impact of chronic heart failure on health-related quality of life data acquired in the baseline phase of the CARE-HF study
Eur J Heart Fail
 , 
2005
, vol. 
7
 (pg. 
243
-
251
)
13
Lalonde
L
Clarke
AE
Joseph
L
Mackenzie
T
Grover
SA
Health-related quality of life with coronary heart disease prevention and treatment
J Clin Epidemiol
 , 
2001
, vol. 
54
 (pg. 
1011
-
1018
)
14
Lacey
EA
Walters
SJ
Continuing inequality: gender and social class influences on self perceived health after a heart attack
J Epidemiol Community Health
 , 
2003
, vol. 
57
 (pg. 
622
-
627
)
15
Lowel
H
Meisinger
C
Heier
M
Hormann
A
The population-based acute myocardial infarction (AMI) registry of the MONICA/KORA study region of Augsburg
Gesundheitswesen
 , 
2005
, vol. 
67
 
Suppl. 1
(pg. 
S31
-
S37
)
16
Holle
R
Happich
M
Lowel
H
Wichmann
HE
KORA—a research platform for population based health research
Gesundheitswesen
 , 
2005
, vol. 
67
 
Suppl. 1
(pg. 
S19
-
S25
)
17
Alonso
J
Angermeyer
MC
Bernert
S
Bruffaerts
R
Brugha
TS
Bryson
H
de Girolamo
G
Graaf
R
Demyttenaere
K
Gasquet
I
Haro
JM
Katz
SJ
Kessler
RC
Kovess
V
Lepine
JP
Ormel
J
Polidori
G
Russo
LJ
Vilagut
G
Almansa
J
Arbabzadeh-Bouchez
S
Autonell
J
Bernal
M
Buist-Bouwman
MA
Codony
M
Domingo-Salvany
A
Ferrer
M
Joo
SS
Martinez-Alonso
M
Matschinger
H
Mazzi
F
Morgan
Z
Morosini
P
Palacin
C
Romera
B
Taub
N
Vollebergh
WA
Sampling and methods of the European Study of the Epidemiology of Mental Disorders (ESEMeD) project
Acta Psychiatr Scand
 , 
2004
, vol. 
Suppl
 (pg. 
8
-
20
)
18
Brooks
R
EuroQol: the current state of play
Health Policy
 , 
1996
, vol. 
37
 (pg. 
53
-
72
)
19
Dolan
P
Modeling valuations for EuroQol health states
Med Care
 , 
1997
, vol. 
35
 (pg. 
1095
-
1108
)
20
Greiner
W
Claes
C
Busschbach
JJV
von der Schulenburg
JM
Validating the EQ-5D with time trade off for the German population
Eur J Health Econ
 , 
2005
, vol. 
6
 (pg. 
124
-
130
)
21
Schweikert
B
Hahmann
H
Leidl
R
Validation of the EuroQol questionnaire in cardiac rehabilitation
Heart
 , 
2006
, vol. 
92
 (pg. 
62
-
67
)
22
Lauzon
C
Beck
CA
Huynh
T
Dion
D
Racine
N
Carignan
S
Diodati
JG
Charbonneau
F
Dupuis
R
Pilote
L
Depression and prognosis following hospital admission because of acute myocardial infarction
Can Med Assoc J
 , 
2003
, vol. 
168
 (pg. 
547
-
552
)
23
Ellis
JJ
Eagle
KA
Kline-Rogers
EM
Erickson
SR
Depressive symptoms and treatment after acute coronary syndrome
Int J Cardiol
 , 
2005
, vol. 
99
 (pg. 
443
-
447
)
24
Beck
CA
Joseph
L
Belisle
P
Pilote
L
Predictors of quality of life 6 months and 1 year after acute myocardial infarction
Am Heart J
 , 
2001
, vol. 
142
 (pg. 
271
-
279
)
25
Bourdel-Marchasson
I
Dubroca
B
Manciet
G
Decamps
A
Emeriau
JP
Dartigues
JF
Prevalence of diabetes and effect on quality of life in older French living in the community: the PAQUID Epidemiological Survey
J Am Geriatr Soc
 , 
1997
, vol. 
45
 (pg. 
295
-
301
)
26
König
HH
Bernert
S
Angermeyer
MC
[Health Status of the German population: results of a representative survey using the EuroQol questionnaire]
Gesundheitswesen
 , 
2005
, vol. 
67
 (pg. 
173
-
182
)
27
Brazier
J
Jones
N
Kind
P
Testing the validity of the Euroqol and comparing it with the SF-36 health survey questionnaire
Qual Life Res
 , 
1993
, vol. 
2
 (pg. 
169
-
180
)
28
Wiklund
I
Herlitz
J
Hjalmarson
A
Quality of life five years after myocardial infarction
Eur Heart J
 , 
1989
, vol. 
10
 (pg. 
464
-
472
)
29
Brown
N
Melville
M
Gray
D
Young
T
Munro
J
Skene
AM
Hampton
JR
Quality of life four years after acute myocardial infarction: short form 36 scores compared with a normal population
Heart
 , 
1999
, vol. 
81
 (pg. 
352
-
358
)

Author notes

Analysis was performed at the Institute for Health Economics and Health Care Management, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany. Data were collected at the MONICA/KORA registry Augsburg, Germany.

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