Participation in Disease Management Programs and Major Adverse Cardiac Events in Patients After Acute Myocardial Infarction.

doi:10.21203/rs.3.rs-25395/v1

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Research article

Participation in Disease Management Programs and Major Adverse Cardiac Events in Patients After Acute Myocardial Infarction.

https://doi.org/10.21203/rs.3.rs-25395/v1

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published 06 Jan, 2021

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Background Cardiovascular diseases are still the main cause of death in the western world. However, diminishing mortality rates of acute myocardial infarction (AMI) are motivating the need to investigate the process of stationary and ambulatory secondary prevention after AMI. Besides cardiac rehabilitation, disease management programs (DMPs) are an important component of outpatient care after AMI in Germany. This study aims to analyze outcomes after AMI among those who participated in DMPs and stationary cardiac rehabilitation (CR) in a region with overall increased cardiovascular morbidity and mortality. Methods: On the basis of data from a regional myocardial infarction registry and a 2-years follow up period, we assessed the occurrence of major adverse cardiac events (MACE) in relation to participation in CR and DMP, risk factors for complications and individual health, and lifestyle characteristics. Multivariable Cox regression was performed to compare survival time until an adverse event occurred.

Results Out of 1,094 observed patients AMI, 272 were enrolled in a DMP. A weak association between DMP participation and hazard rates for MACE compared to non-enrollees was found in the crude model (hazard ratio = 0.93; 95% confidence interval = 0.65–1.33). When adjusted for possible confounders, this difference disappeared (1.03; 0.72–1.48). Furthermore, smokers and obese patients showed a distinctly lower chance of DMP enrollment. In contrast, participants of CR showed a lower risk for MACE in crude (0.52; 0.41–0.65) and adjusted analysis (0.56; 0.44–0.71).

Conclusions Participation in DMP was not associated with a lower risk of MACE, while CR showed beneficial effects. Adjustment only slightly changed effect estimates in both cases, still potential effects of confounding need to be considered.

Cardiac & Cardiovascular Systems

Cardiothoracic Surgery

myocardial infarction

heart attack

DMP

rehabilitation

MACE

outpatient

health care

coronary heart disease

secondary prevention

Cardiovascular diseases are still the main cause of death in the western world. In 2017, 37% of all deaths in Germany were caused by diseases directly affecting the cardiovascular system [1]. The two most frequent death-related diagnoses were coronary heart disease (CHD) and acute myocardial infarction (AMI) [2]. Due to standardized acute revascularization management by interventional cardiology, AMI mortality rate has been decreasing in the last three decades [3]. However, AMI survivors are a high risk group for death or another severe health event, especially if they maintain additional risk factors like smoking, obesity or diabetes [4]. To avoid subsequent events, secondary prevention is needed [5].

Stationary cardiac rehabilitation (CR) is the best known and most often recommended secondary prevention approach after AMI [5–9]. However, in light of its high costs, delayed return home, and only a short period of intervention, the search for alternatives is well justified [10–13]. One such option could be disease management programs (DMPs) which have been introduced in Germany in 2002 to improve outpatient medical treatment quality and reduce costs in the health care system [14]. DMPs are structured treatment programs, coordinated by the patient’s general practitioner (GP). Since 2012, all statutory health insurance companies have to offer DMPs based on the guidelines of the Federal Joint Committee to achieve nationwide homogeneity. Participating in a DMP is voluntary for the patients as well as the GPs. For patients, inclusion criteria are defined for enrollment in a DMP [15].

However, eighteen years after the introduction of the first DMP for coronary heart disease, there is still insufficient evidence regarding DMP effectiveness. Besides the mandatory evaluations of the insurance companies, only few studies were published, and they mostly focused on DMPs related to diabetes mellitus [16–25]. Since DMPs were introduced all over Germany as a mandatory service, only observational studies can contribute further evidence on their performance [26].

In the federal state of Saxony-Anhalt, the rate of deaths related to cardiac events is 38.5% above the German average [2]. In order to investigate the causes of this increased level, a regional registry of myocardial infarction in urban and rural regions of the federal state (RHESA) was established [27]. Patients who agreed to participate in RHESA are followed over time using questionnaires and health status information [28]. One explanation of the elevated mortality can be suboptimal secondary prevention. Therefore, secondary prevention programs were in the focus of the follow up within the framework of RHESA.

Our aim was to assess the participation of AMI survivors in the secondary prevention programs and its association with the subsequent outcomes.

Sample

We used data from follow-up of patients registered in RHESA. RHESA and its follow-up modalities were described elsewhere [27, 28]. In brief, RHESA collected information about all fatal and non-fatal myocardial infarctions in the city of Halle and in the rural region of the Altmark in Saxony-Anhalt. Patients who survived AMI were asked during their hospital stay to participate in a follow up. The baseline information was obtained via questionnaires from physicians or study nurses in the respective hospitals. Between 2013 and 2017, a first follow-up was conducted six weeks after hospital discharge (n = 804 patients participated); between 2015 and 2018, a second follow up was conducted, in which patients were contacted two years after their hospital discharge (n = 383) (Fig. 1). For the purpose of the current study, we conducted a third follow up of all patients in March-June 2019.This third follow up focused on information about the occurrence of cardiac events: AMI, stroke, death, percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG), and on participation in DMPs or stationary rehabilitation, co-morbidities, and socioeconomic factors as well as smoking/smoking cessations. In order to clarify if patients who died before the third follow up participated in DMPs, we additionally contacted the doctors who signed their death certificates with a short questionnaire regarding their assessment of the cause of death and DMP participation.

We furthermore used the variables obesity (≥ 30 kg/m² vs. <30 kg/m² at the time of the initial AMI), hypertension (elevated arterial blood pressure diagnosed before or at the time of the initial AMI), and ST-elevated myocardial infarction (STEMI) versus non-ST-elevated myocardial infarction (NSTEMI) based on baseline questionnaire of RHESA.

All participants provided written informed consent for participation in RHESA. The Ethics Committee of the Medical Faculty of the Martin-Luther University Halle-Wittenberg approved the initial study and the follow up questionnaires.

End points

The primary endpoint was survival time until the occurrence of the first major adverse cardiac event (MACE). In the composite endpoint, we included: AMI, stroke, the percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), and death. Only events were included which occurred at least fifteen days after the initial AMI. In case of multiple outcomes, only the first was considered. A known risk of using composite endpoints are dilution effects [29]. Thus, we defined two subtypes of MACE: MACE1 included only AMI, stroke, and death. MACE2 included all former as well as the interventions PCI and CABG.

Statistical analysis

Descriptive characteristics of the sample were reported as percentages and mean values with 95% confidence intervals (CI). In order to identify determinants of DMP and CR, we obtained multivariable relative risks from Poisson model with robust error variance as alternative to logistic regression analysis for frequent outcomes [30]. Since investigated determinants were correlated, we formally tested for multicollinearity to exclude erroneous results [31]. We analyzed the effects of CR or DMP participation on the occurrence of MACE using Cox proportional hazard regression models. Observed survival time started 15 days after arriving in hospital for treatment of the participant’s initial AMI in order to avoid immortal time bias [32] and ended 24 months after initial AMI or at the first observed MACE. Occurrence for all MACE as well as beginning of participation in DMPs have been self-reported by the patient or reported by the patient’s GP with exact date. However, due to many participants only reporting the month, we set the day for every event on the 15th of the respective month. In case of discordant information in different follow up questionnaires, we used information most closely following the reported event. Since enrollment into DMP can occur at various times after AMI, we considered DMP as time-dependent exposure. Time of participation in DMPs before hospital treatment was not taken into account to create a proper time zero of the time-dependent covariate [33]. In a sensitivity analysis, those who started participating in DMP before AMI were excluded.

In order to assess potential selection effects related to participation in either DMP or CR, we calculated crude and adjusted estimates. Covariates for adjustment were selected based on the literature and directed acyclic graphs theory. They were: diabetes mellitus (diagnosed before or at the time of the initial AMI), smoking status (being smoker at the time of the initial AMI), sex, obesity, and age at the time of initial AMI. The models for CR and DMP participation were also mutually adjusted for these variables. In an attempt to test for selection bias caused by more motivated patients taking part in CR, crude estimates of rehabilitation effects were stratified by the timing of smoking cessation.

Crude and adjusted effects were calculated as hazard ratios with 95% CI.

All statistical analyzes were performed with SAS 9.4.

Sample characteristics

Of the 1,385 persons who provided informed consent for the follow up and were alive in May 2019, 881 (63.6%) participated in the third follow up of RHESA and filled out the corresponding questionnaire after up to two reminders. Additionally, there were 95 persons who participated in the follow up two years after AMI, but did not participate in the third follow up. Since they provided the relevant information for the current analysis in the earlier questionnaire they were also included in the analysis. Furthermore, we received information about 118 patients, who died before their second follow up, through a questionnaire filled out by their respective GPs.

Of the 1,094 persons that were included in our study, about one quarter (24.9%) has been enrolled in DMP while 58.5% took part in the CR after treatment of the initial AMI and 189 patients (17.3% of all) participated both in DMPs as well as in CR. Of all patients, 33.9% did not participate in either CR or DMP (Table 1). CR participants were more likely to be also enrolled in DMP and vice versa.

Those who participated in CR were younger and more often smokers at the time of AMI than those who did not participate (Table 1). In contrast, those who participated in DMP were less often smokers than those who did not. STEMI was most common in CR participants.

Table 1

Characteristics of the study population
		REHAB		DMP
		(yes)	(no)	(yes)	(no)
		n = 640	n = 454	n = 272	n = 822	n = 1094
Age (mean)	years	64.7	70.1	66.4	67.1	67
Age (mean)	(95% CI)	(63.9–65.7)	(68.9–71.2)	(65.1–67.8)	(66.3–68.0)	(66.2–67.7)
Age groups
25–49		11.7	5.7	9.6	9.1	9.2
50–59		24.2	18.5	20.6	22.3	21.9
60–69		28	20.3	25	24.7	24.8
70–79		26.6	33	36	27	29.3
80 +		9.5	22.5	8.8	16.9	14.9
Male sex	pct	70.8	70.9	70.1	70.8	70.8
	(95% CI)	(67.1–74.3)	(66.5–75.1)	(65.2–76.3)	(67.6–73.9)	(68.1–73.5)
Diabetes	pct	15.9	17.2	15.1	16.9	16.5
Diabetes	(95% CI)	(13.2–19.0)	(13.8–20.1)	(11.0–20.0)	(14.4–19.7)	(14.3–18.8)
Smoker	pct	35.8	26.4	26.8	33.6	31.9
Smoker	(95% CI)	(32.1–39.6)	(22.4–30.1)	(21.7–32.5)	(30.3–39.9)	(29.2–34.8)
Hypertension	pct	82	85.2	83.1	83.5	83.4
Hypertension	(95% CI)	(78.8–84.9)	(81.6–88.4)	(78.1–87.3)	(80.7–85.9)	(81.0-85.5)
Obesity	pct	23.4	19.2	18.4	22.8	21.7
Obesity	(95% CI)	(20.2–26.9)	(15.6–23.1)	(14.0-23.5)	(19.9–25.8)	(19.3–24.2)
STEMI	Pct	50.3	33.0	42.7	43.3	43.1
STEMI	(95% CI)	(46.4–54.3)	(28.7–37.6)	(36.7–48.8)	(39.9–46.8)	(40.2–46.1)
DMP = disease management program, REHAB = cardiac rehabilitation; smoker and diabetes only relevant if being current at the time of the initial myocardial infarction registered in the data base

age/age groups = years at the time of the initial AMI

About one third of all participants experienced MACE within two years of follow up and 9% a reinfarction (Table 2). Those who participated in DMP had more MACE than those who participated in rehabilitation, particularly with respect to deaths in the group of those 83 DMP participants that did not take part in CR. The mean age of this subgroup was 71 years and therefore much higher than the average age of all DMP participants.

Table 2

Proportion of patients experiencing negative relevant outcomes within two years after AMI
	DMP only n = 83	REHAB only n = 451	Both n = 189	None n = 371	Total N = 1094
MACE1	45.8	14.6	19.1	27.5	22.1
MACE1	35.0-57.1	11.5–18.2	13.7–25.4	23.0-32.3	19.7–24.7
MACE2	51.8	24.6	31.2	35.0	31.4
MACE2	40.6–62.9	20.7–28.9	24.6–37.8	30.2–40.1	28.6–34.2
Reinfarction	10.8	8.9	7.4	10.2	9.2
Reinfarction	5.1–19.6	6.4–11.9	4.1–12.1	7.4–13.8	7.6–11.1
Stroke	1.2	3.3	2.7	3.0	2.9
Stroke	0.0-6.5	1.9–5.4	0.9–6.1	1.5–5.2	2.0-4.1
PCI	9.6	8.7	11.1	10.0	9.6
PCI	4.3–18.1	6.1–11.2	7.0-16.5	7.1–13.5	7.9–11.5
CABG	0.0	5.8	5.8	3.0	4.4
CABG	0.0-4.3	3.8–8.3	2.5–9.2	1.5–5.2	3.3–5.8
Cardiac Death	13.3	0.7	2.7	5.7	3.7
Cardiac Death	6.8–22.5	0.1–1.9	0.9–6.1	3.5–8.5	2.6-5.0
Death (other)	25.3	2.9	9.0	11.3	8.5
Death (other)	16.4–36.0	1.5–4.9	5.3–14.0	8.3–15.0	6.9–10.3
MACE 1 = composite endpoint including Reinfarction, Stroke and Death (cardiac / other),
MACE 2 = composite endpoint including MACE 1 plus percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG)
DMP = Disease Management Program; REHAB = cardiac rehabilitation

Determinants of DMP enrollment and participating in cardiac rehabilitation

In the multivariable model, smoking at the time point of AMI was associated with lower participation in DMP, but not with lower participation in CR (Figs. 2 and 3). In contrast, higher age was associated with lower participation in rehabilitation but not in DMP. STEMI was also associated with increased participation in rehabilitation. The broader CI for the estimates for DMP than for rehabilitation were a consequence of the fact that the selected variables better predicted the latter than the former.

Association between participation in DMP or CR after AMI and outcomes during follow up

Participation in DMP was not associated with improved outcomes (crude hazard ratio = 0.93; 95% CI 0.65 − 0.64), while participation in cardiac rehabilitation was associated with risk reduction of about 50% (0.52; 0.41–0.65). These results were virtually unchanged after adjustment for age, sex, several diseases, and a mutual adjustment for DMP and stationary rehabilitation (Fig. 4).

Overall, the effects of stratification for the considered subgroups were small indicating that selection of participants according to these variables did not strongly affect the effects of either CR or DMP. Additional results regarding in context of smoking habits are reported in Table 3.

Table 3

Effects of participation in cardiac rehabilitation stratified by smoking status
Model Effects of rehabilitation in those who	hazard ratio* (95% CI)
…did not smoke at time point of AMI	0.59 (0.45–0.78)
… smoked at time point of AMI	0.45 (0.27–0.74)
… smoked at time point of AMI, but stopped before rehabilitation	0.39 (0.18–0.83)
… smoked at time point of AMI, but did not stop smoking before rehabilitation	0.51 (0.26–1.01)
*participating in cardiac rehabilitation vs. no participation regarding MACE1 occurrence

Being diagnosed with diabetes resulted in an increased risk of MACE (HR = 1.21; 95% CI 0.88–1.67) in our two-year observation. Age, sex and obesity did not show association with change in survival time whereas smoking lead to a lower hazard rate (HR = 0.76; 95% CI = 0.55–1.05).

In smokers, the AMI occurred at the mean age of 57.6 years (56.6–58.6) while nonsmokers were noticeably older with a mean age of 71.3 years (70.6–72.1). We compared the crude hazards of both age groups (mean age +/- 5 years). Thus, we could see a strong association between being in the age group of smokers and longer survival until MACE compared to the age group of non-smokers (HR = 0.61; 95% CI 0.42–0.87).

The Comparison of MACE1 and MACE2 showed higher absolute numbers of events and narrower CI for MACE2 without dilution effects. Thus, MACE2 has been used as the primary endpoint in all cox regression analyses. Sensitivity analysis with 77.6% of all DMP participants who began their program after the AMI did not change the outcome noticeably (data not shown).

Using data from the regional myocardial infarction registry RHESA, we demonstrated that participating in Disease Management Programs (DMPs) does not result in lower rates of cardiac events whereas participation in cardiac rehabilitation (CR) after discharge from hospital was associated with a distinctly lower hazard rate of MACE compared to non-participants.

One main goal of DMPs is to educate patients in everyday behavior in order to reduce the occurrence of complications and contain the effect of risk factors in patients with chronic disease. Therefore, modules like nutritional advices, assistance in smoking cessation or concepts to increase daily physical activity complement the general practitioner’s (GP) guideline care [15]. DMPs were established and repeatedly adapted since 2003, so there could be a spillover effect on the outpatient treatment by GPs on all of their patients, regardless being DMP-enrollees or not [17, 20]. GPs who are working with methods of DMPs over years possibly have improved the process quality and structure of secondary prevention in patients.

Surprisingly, patients with risk factors like a previous AMI, diabetes mellitus, smoking or obesity, tended to be less common DMP participants. Smokers and obese people in our cohort depicted an unexpected lower chance to be enrolled in DMPs. On this account, several key components of the DMPs probably could not unfold their full effect, because patients who would probably benefit the most were participating less often in DMPs. The slightly lower hazard ratio for MACE in the crude model is probably due to the DMP participants being already healthier before the DMP than the control group.

It is also remarkable, that the proportion of enrollment in DMP of about one quarter is substantially lower than the 77%, which was found eight years ago in a comparable study in the region of Augsburg by Laxy et al. [16]. Similar results (enrollment rate of 72%) were found throughout Germany in 2013 by Röttger et al. [23] in patients with coronary heart disease. Possible explanations could be regional socio-economic differences and health characteristics of the respective cohorts as well as the time span between the studies [21, 23]. In conclusion, it is apparent that the DMPs cannot reach their required target group in Saxony-Anhalt. While only about one third of all in RHESA registered patients took part in the baseline survey with at max. 70% answering in the respective follow-up, often participants are more health conscious.

Thus, our results indicate that the process of DMP participant acquisition may be one of the reasons for the lack of effects on MACE in patients after myocardial infarction. This is especially important, considering the higher rates of cardiac mortality, risk factor distribution and demographic structure in our regional study population [37]. These observations are in line with a study by Schäfer et al. about selection effects in current DMP research [35]. While our results match the conclusions of similar studies [16, 17], health insurance evaluations repeatedly described protective effects [36]. The explanation could be a different comparison group.

In contrast, participation in CR after discharge from hospital displayed a distinctly lower hazard rate of MACE compared to non-participants. In an observational study, the question is if this might be related to selection of participants. On the one hand, we found that many of those who smoked at the time of AMI, stopped smoking before starting rehabilitation. The effect of rehabilitation on MACE in this group was stronger than amongst participants that stopped smoking at the time of rehabilitation or never quit smoking during follow up time. This indicates that effects of stopping smoking on subsequent outcomes should not be falsely attributed to the CR. On the other hand, adjusting for the relevant risk factors did not pertinently change the estimated effect of CR in the direction of the null.

According to our results, smoking at the time of the initial AMI shows a weak association with prolonged survival time. This well-known ‘smoker’s paradox’ was reported in several studies [38, 39]. The main suspected reason is that smoker experience AMI at younger age, thus the relation with mortality is diluted.

Strengths and limitations

The strength of the study lies in the prospective, population-based design with a cohort of patients severely affected by the elevated risk of multiple complications after their myocardial infarction. Also, the time dependent covariate design in survival time analysis as well as the possibility to adjust the regression to multiple important co-morbidities and patient’s characteristics with relevant influence on the total effect adds to the novelty and importance in current research.

Immortal time bias is common in prospective cohort studies, but our method of implementing DMP status as time dependent covariate in the analysis can strongly reduce biased treatment effect estimates [32, 42]. This enables our analysis to account for DMP time whether or not the patient enrolled before or after the time of the initial AMI. Hence, our study is not limited by a time fixed control group status which would ignore late onset DMP enrollment.

The findings of our study are limited by the follow-up time of only 2 years, thus later outcomes are not considered. A longer period of time with greater, Germany-wide data could add to our results. Also, the main source of our data are questionnaires or telephone interviews which can cause recall bias or erroneous answers [40]. Selection bias can be caused by more motivated patients responding to the survey as well as the described effect of not covering equal distribution of risk factors in the intervention and control groups [21, 41].

Within the framework of the regional AMI registry in urban and rural regions of the federal state (RHESA), we could not confirm a benefit of participation in DMP for AMI survivors with respect to MACE. The present findings suggest that the reason for the lack of effects of DMPs may be insufficient inclusion of those who would benefit from the DMP. In contrast, we observed a positive effect of CR, while confounding due to differences in personal motivation to participation. Thus, selection effects should be considered.

AMI

acute myocardial infarction; CABG:coronary artery bypass graft; CHD:coronary heart disease; CI:confidence interval; CR:cardiac rehabilitation; GP:general practitioner; MACE:major adverse cardiac events; NSTEMI:non-ST-elevation myocardial infarction; PCI:percutaneous coronary intervention; RC1:RHESA-CARE 1; RC3:RHESA-CARE 3; RHESA:Regional Myocardial Infarction Registry; STEMI:ST-elevation myocardial infarction

Ethics approval and consent to participate

The Ethics Committee of the Medical Faculty of the Martin Luther University Halle-Wittenberg approved the initial study and the follow up questionnaires. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Authors’ Contribution

CF designed the study, drafted the manuscript, conducted the literature search, performed statistical analysis and interpreted the data. JH co-designed the study. JH, RM, MN and SH contributed to the analysis and interpreting the data. RM and JH conducted and coordinated the study. JH and CF conducted the RC3-questionnaire. All authors have read and approved the manuscript.

Acknowledgements

We thank our participants for their willingness to respond to our questionnaires in the RHESA-CARE studies. Furthermore, we want to thank all reporting hospitals, general practitioners and patients for their cooperation in the process of recruitment for the RHESA study and the study/registry personnel Nancy Skoeries, Mareike Kunze and Yves Froese.

Funding

RHESA has been funded by project grants of the German Heart Foundation, the Federal Ministry of Health, the Ministry for Labor and Social Affairs of Saxony-Anhalt, the Ministry of Sciences and Economic Affairs of Saxony-Anhalt, and the statutory health insurance funds AOK, ikk gesund plus and VdEK. RHESA-CARE has been funded by the Wilhelm Roux Program of the Faculty of Medicine of the Martin-Luther-University Halle-Wittenberg [grant number FKZ 28/35]. CF received stipend from the Martin Luther University through HapKom. None of the funding sources had any role in the preparation of the manuscript nor in the decision for publication.

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Participation in Disease Management Programs and Major Adverse Cardiac Events in Patients After Acute Myocardial Infarction.

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Sample

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Statistical analysis

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Sample characteristics

Determinants of DMP enrollment and participating in cardiac rehabilitation

Association between participation in DMP or CR after AMI and outcomes during follow up

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Strengths and limitations

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