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Comorbidities and Psychosocial Characteristics as Determinants of Dropout in Outpatient Cardiac Rehabilitation

Pardaens, Sofie PhD; De Smedt, Delphine PhD; De Bacquer, Dirk PhD; Willems, Anne-Marie PhD; Verstreken, Sofie MD; De Sutter, Johan MD, PhD

doi: 10.1097/JCN.0000000000000296
ARTICLES: Cardiac Rehabilitation
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Background: Despite the clear benefits of cardiac rehabilitation (CR), a considerable number of patients drop out early.

Objective: Therefore, we wanted to evaluate dropout in CR with a special focus on comorbidities and psychosocial background.

Methods: Patients who attended CR after acute coronary syndrome, cardiac surgery, or heart failure (N = 489) were prospectively included. Dropout was defined as attending 50% of the training sessions or less (n = 96 [20%]). Demographic and clinical characteristics, exercise parameters, and psychosocial factors were analyzed according to dropout, and those with a trend toward a significant difference (P < .10) were entered in a multivariate logistic model.

Results: The presence of a cerebrovascular accident (4.18 [1.39–12.52]) involved a higher risk of dropout, and a comparable trend was seen for the presence of chronic obstructive pulmonary disease (2.55 [0.99–6.54]). Attending the training program only twice per week also implicated a higher risk of an early withdrawal (3.76 [2.23–6.35]). In contrast, patients on β-blockers were less likely to withdraw prematurely (0.47 [0.22–0.98]). Singles were more likely to drop out (2.89 [1.56–5.35]), as well as those patients who were dependent on others to get to CR (2.01 [1.16–3.47]). Finally, the reporting of severe problems on the anxiety/depression subscale of the EuroQOL-5D questionnaire involved a higher odds for dropout (7.17 [1.46–35.29]).

Conclusions: Neither demographic characteristics nor clinical status or exercise capacity could independently identify patients who were at risk of dropout. The presence of comorbidities and a vulnerable psychosocial background rather seem to play a key role in dropout.

Sofie Pardaens, PhD PhD student, Department of Internal Medicine at Ghent University, Belgium. Dr Pardaens is now a scientific coworker at OLV Hospital in Aalst, Belgium.

Delphine De Smedt, PhD FWO Postdoctoral Fellow, Department of Public Health, Ghent University, Belgium.

Dirk De Bacquer, PhD Professor and Head, Department of Public Health, Ghent University, Belgium.

Anne-Marie Willems, PhD Scientific Coordinator, Department of Cardiology, AZ Maria Middelares, Ghent, Belgium.

Sofie Verstreken, MD Cardiologist, Cardiovascular Center, Onze-Lieve-Vrouw Hospital, Aalst, Belgium.

Johan De Sutter, MD, PhD Professor, Department of Internal Medicine, Ghent University; and Cardiologist, Department of Cardiology, AZ Maria Middelares, Ghent, Belgium.

This work was supported by the Research Foundation Flanders (FWO Vlaanderen; grant G.0628.10N).

The authors have no conflicts of interest to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.jcnjournal.com).

Correspondence Sofie Pardaens, PhD, Department of Internal Medicine, Ghent University, De Pintelaan 185 6K12E, 9000 Ghent, Belgium (Sofie.pardaens@ugent.be).

Cardiac rehabilitation (CR) has been shown to reduce the risk of mortality and hospitalization, particularly in coronary artery disease.1–6 Despite these clear benefits, more than half of eligible patients do not attend CR,7,8 mainly because of personal or logistic reasons.8–10 In addition, a considerable number of those patients who actually attend CR drop out prematurely varying from 22% to 65%.11,12 This may be partly due to personal (perception of self-control of the problem, illness cognition, socioeconomic status) or financial reasons,9,11,13,14 but also physical and psychological health problems may play a key role.9,12

The majority of these studies11,12,14 have often focused on a particular subgroup of cardiac patients who may not always be representative for the total CR population. Therefore, the aim of our study was to evaluate dropout in CR among a cardiac population consisting of patients after acute coronary syndrome, cardiac surgery, and heart failure, with a focus on potential predisposing demographic and clinical characteristics, exercise parameters, and psychosocial factors that could lead to dropout.

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Materials and Methods

Study Population

Four hundred eighty-nine patients who attended CR after hospitalization for acute coronary syndrome (n = 204), cardiac surgery (n = 245), or heart failure (n = 40) in 2 Belgian hospitals (AZ Maria Middelares, Ghent, and Onze-Lieve-Vrouw Hospital, Aalst, Belgium) between April 2011 and March 2013 were prospectively included. In both hospitals, a similar multidisciplinary outpatient CR program was offered to each patient with a maximum of 45 reimbursed sessions, which is further explained in this section. Because the length of phase II rehabilitation programs varies across countries, dropout was defined as attending 50% of the rehabilitation program or less, which is consistent with previous studies.11

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Ethics

This study protocol was approved by the ethical committee of the 2 participating hospitals (AZ Maria Middelares and Onze-Lieve-Vrouw Hospital), and all patients gave informed consent. The clinical investigations conform with the principles outlined in the Declaration of Helsinki.

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Clinical Characteristics

Demographic characteristics, medication use, risk factors, and comorbidities were collected at start of CR, based on chart review. A standard blood sample was taken to measure hematologic parameters, glucose metabolism, lipids, renal function, and N-terminal pro–brain natriuretic peptide. Echocardiography was performed on admission to the hospital in 456 patients with a measurement of left ventricular ejection fraction (Simpson method).15

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Exercise Testing

Cardiopulmonary exercise testing was performed in 411 patients, 24 days after hospitalization (median, 24 days; interquartile range, 10–34 days) on a cycloergometer using a ramp protocol adapted to the patient’s physical status. Ventilatory and respiratory gas measurements were obtained on a breath-by-breath basis using an Oxycon Pro Spirometer (Jaeger; CareFusion, Palm Springs, California). Heart rate was continuously registered by a 12-lead electrocardiogram, and blood pressure was noninvasively measured, using a manual sphygmomanometer every 2 minutes during the exercise test. Patients exercised to the limits of their functional capacities established by a respiratory exchange ratio greater than 1.15 or until the physician stopped the test because of adverse signs and/or symptoms, such as chest pain, dizziness, potentially life-threatening dysrhythmias, significant ST-segment displacement (≥1mm), and marked systolic hypotension or hypertension. The maximal achieved load during incremental exercise was recorded. Peak oxygen consumption was defined as the mean of the last 30 seconds of peak exercise and was expressed as milliliters per minute per kilogram. The slope of the linear relation between VE (y axis) and VCO2 (x axis), the VE/VCO2 slope, was calculated by including all data points to the end of exercise. The anaerobic threshold was defined as the exercise level at which ventilation starts to increase exponentially, relative to the increase in VO2.16 At the beginning of the exercise training program, a 6-minute walk test was also performed in a 30-m hallway. The distance a patient could quickly walk on a flat hard surface in a period of 6 minutes was measured (6-minute walking distance).17

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Psychosocial and Logistic Factors

Educational background and social and occupational status were recorded, as well as the dependency for transport and the distance to the CR center. Health-related quality of life (HRQoL) was evaluated using the EuroQoL-5D-3L (EQ-5D-3L), which consists of 2 parts: the EQ-5D descriptive system and the EQ-5D visual analog scale (EQ-5D VAS). The EQ-5D-3L descriptive system includes 5 dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) with 3 levels of severity of problems for each dimension (no problems, some problems, severe problems). An index value is calculated from the answers on the aforementioned dimensions, with 1 representing perfect health, 0 representing death, and less than 0 representing a health state perceived worse than death. The EQ-5D VAS is a visual analog scale used to record the patient’s self-perceived health, with 0 as the worst imaginable health state and 100 as the best imaginable health state.18,19 The Hospital Anxiety and Depression Scale (HADS) was applied for identifying those patients with symptoms of anxiety or depression. The HADS comprises 14 items, of which 7 are related to anxiety (HADS-A) and 7 to depression (HADS-D). Each item is scored on a 4-point response scale (0–3), and the total score on each subscale ranges between 0 and 21. A score of less than 8 could be regarded as being in the reference range, whereas a score of 11 or higher indicates the probable presence of a mood disorder. A score between 8 and 10 is just suggestive of the presence of the respective state.20 Psychometric evaluation of both EQ-5D and HADS in a large European sample of coronary patients showed that both instruments were reliable and valid for use.21

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Cardiac Rehabilitation

A multidisciplinary rehabilitation program was offered to each patient 29 days (range, 19–40 days) after discharge from the hospital, including exercise training, dietary counseling, smoking cessation, and psychological support. Patients had the choice to train 2 or 3 times weekly for 60 minutes during a period of 3 to 5 months with a maximum of 45 reimbursed sessions. Patients who withdrew prematurely attended 17 training sessions (range, 10–20 training sessions), compared with 44 training sessions (range, 38–45 training sessions) in the group that continued the training program. The exercise training program consisted of a combination of aerobic and strengthening exercises. Body weight and waist were measured at start and end of the rehabilitation program, and dietary counseling was offered to the patient as needed. Patients with an increased risk of anxiety or depression according to HADS were invited for further psychological support.

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Statistical Methods

Statistical analysis was performed with IBM SPSS Statistics for Windows, version 21.0 (IBM Corp, Armonk, New York). Clinical characteristics, baseline exercise capacity, and psychosocial factors were assessed according to dropout using the χ2 test, Fisher exact test, independent-samples t test, and Mann Whitney U test, as appropriate. For all analyses, the level of significance was set at P < .05. Variables with an overall significance value of P < .10 were entered in a multivariable backward logistic regression model to identify the strongest predictors for dropout. Nagelkerke R2 was reported to assess the percentage of variation that was explained by the model. Because the frequency of the training program was significantly different according to dropout, differences in clinical and psychosocial characteristics between patients who trained 2 or 3 times per week were investigated in a supplementary analysis.

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Results

Clinical Characteristics According to Dropout

Clinical characteristics are summarized in Table 1. In total, 96 of the 489 patients (20%) who attended CR dropped out in the first half of the training program. Age and body mass index were comparable, but women tended to drop out earlier than did men during the course of CR (P < .05). A quite similar clinical status was seen in both groups with a comparable left ventricular function, renal function, and level of neurohormonal activation. Patients who were referred after coronary artery bypass graft were less likely to drop out than patients after acute coronary syndrome or heart failure (P < .05). Comorbidities such as chronic obstructive pulmonary disease (COPD) and cerebrovascular accident (CVA) were significantly more present in those patients who ceased in the first half of the training program, and β-blockers were less prescribed in the latter group (all P < .05). No differences were found in exercise capacity, except for a trend toward a slightly lower maximal achieved load in patients who dropped out early in the training program (P = .051). Patients who chose to attend the training program 3 times weekly were clearly less likely to drop out in the first half of CR (P < .001).

TABLE 1

TABLE 1

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Psychosocial and Logistic Factors According to Dropout

Table 2 shows the psychosocial and logistic factors according to dropout. Educational and occupational status was comparable, but patients without a partner were significantly more present in the group that has withdrawn prematurely (P < .05). Evaluation of logistic factors revealed that patients who were dependent on others for getting to CR were more likely to drop out than those who came on their own to the center (P < .01). Patients who dropped out prematurely reported more problems regarding HRQoL on the dimensions of self-care and anxiety/depression of the EQ-5D-3L (all P < .05). Nevertheless, their perception of health (EQ-5D VAS) was not significantly different. Also, a higher percentage of patients with an increased risk of depression (HADS-D ≥11) was seen in the group that dropped out early from CR (P < .05), and a similar trend was seen for patients with an increased risk of anxiety (HADS-A ≥11) (P = .058).

TABLE 2

TABLE 2

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Logistic Regression Model for Dropout

Logistic backward regression was performed to identify the strongest predictors for dropout, and the remaining significant variables are shown in Table 3. The presence of comorbidities involved a higher risk of early withdrawal with patients with a history of CVA having 4 times (4.18 [range, 1.39–12.52]) higher odds for dropout, and a similar trend was present in patients with COPD being nearly 3 times as likely (2.55 [range, 0.99–6.54]) to drop out. Attending the training program only 2 times per week also involved a higher odds for early withdrawal (3.76 [2.23–6.35]). Patients on β-blocker therapy, on the other hand, were less likely to cease in the first half of the training program than patients who did not have β-blocker therapy (0.47 [0.22–0.98]). Also, several psychosocial characteristics remained in the final logistic regression model. Singles had a 3 times higher risk of dropout (2.89 [range, 1.56–5.35]) and being dependent on others to get to CR involved twice as much risk of dropout (2.01 [range, 1.16–3.47]). The reporting of severe problems on the anxiety/depression subscale of the EQ-5D was a final significant predictor for early withdrawal (7.17 [range, 1.46–35.29]). Together, these variables explained approximately one-fifth of the total variation in dropout (Nagelkerke R2 = 0.215).

TABLE 3

TABLE 3

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Clinical, Psychosocial, and Logistic Factors According to the Frequency of the Training Program

Supplementary analyses according to the frequency of the training program are described in Tables, Supplemental Digital Content 1, http://links.lww.com/JCN/A16, and Supplemental Digital Content 2, http://links.lww.com/JCN/A17. Patients who attended CR only twice per week were slightly older and more frequently women (all P < .001). They also had signs of a worse clinical status with a trend toward a decreased left ventricular function (P = .052) and an increased neurohormonal activation (P < .01). Renal function was equal in both groups. No differences in risk factors, comorbidities, or medication were found, except for diuretics, which were more frequently prescribed in patients who trained twice per week (P < .05). An impaired exercise capacity was seen in patients who attended CR twice per week with a decrease in load, peak oxygen consumption, and 6-minute walking distance (all P < .001). A lower education level was seen in the group that trained twice per week as well as more singles (both P < .05). Patients attending CR only twice per week were also more frequently professionally inactive (P < .01). In addition, they reported more problems regarding mobility and self-care influencing HRQoL (both P < .01).

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Discussion

In this prospective analysis of potential determinants of dropout in a fairly large sample of patients participating in CR, one-fifth of our patient population quit in the first half of the CR program, with a higher risk in patients presenting with comorbidities and a vulnerable psychosocial background.

Although demographic factors, age and gender, were predictive of dropout in the report of Yohannes et al,12 these were not crucial in our study. Women were slightly more represented in the group that dropped out early, but this may be explained by the fact that women were more frequently single (26% vs 15%) and dependent on others to get to CR (39% vs 24%). Multivariate analysis revealed that being single and being dependent for transport were main drivers of dropout rather than gender, which is in line with previous findings.11,22 Similarly, clinical status, as expressed by ventricular function, neurohormonal activation, and renal function, was equal in both groups, suggesting that disease severity did not influence dropout. On the other hand, the majority of patients participating in CR in our hospitals were referred after cardiac surgery, and they were also more likely to continue the training program than others, although this could not be confirmed in multivariate analysis. Their greater participation rate is rather due to the lack of perceived need for CR in other patient groups11,23 than to differences in clinical status. Surprisingly, baseline exercise capacity also did not have any influence on withdrawal, suggesting that physically impaired patients may as well successfully complete their program as do patients who are fitter at the start of CR. Traditional cardiovascular risk factors such as smoking and diabetes have been related to dropout in the past,11 but this was not supported by our results. Interestingly, the presence of comorbidities, in particular, a history of CVA and COPD, turned out to play a key role. Chronic obstructive pulmonary disease has generally been recognized as an independent predictor of all-cause mortality in heart failure,24–27 and likewise, the presence of CVA after acute myocardial infarction has been related to a higher risk of mortality.28 In addition, the presence of COPD may implicate a lower use of β-blockers,29 which in turn also involves a higher risk of dropout according to our results. Besides a slightly better preserved left ventricular ejection fraction and a lower level of N-terminal pro–brain natriuretic peptide, patients without β-blocker prescription did not differ in their clinical presentation or exercise capacity from those who did take β-blockers. The role of medication use in dropout has not been investigated in literature thus far. Nevertheless, 1 author recently noticed that patients who were not referred to CR were less likely to receive evidence-based medication.6 A similar association between the use of evidence-based medication and adherence to CR might be a possible explanation but should be further investigated.

Dependency for transport was a major predictor of dropout, but not the distance to the CR center, which was previously mentioned as an important barrier to attend CR.9,23 In contrast to countries with rural areas,23,30 the majority of our patients was living within a radius of 20 km of the CR center, making it unlikely that distance itself was a major issue. Because being dependent on transport and having no partner were key drivers in dropout, this suggests that a vulnerable social situation is more important than logistic barriers.

Furthermore, HRQoL was a major determinant in our study. Patients who dropped out early reported significantly more problems at the start of CR regarding self-care and symptoms of anxiety and depression. In patients with coronary artery disease, an impaired HRQoL has been reported with problems on several dimensions of the EQ-5D except for self-care.31 This discrepancy regarding self-care may be due to a difference in time after the event. Because our patients were in the acute phase after their event (<6 months), problems regarding self-care were more likely than at a later stage as in the previous study.31 Perhaps more important than self-care were the reported problems on anxiety and depression. Strikingly, only the severe problems did have an impact on dropout. Health-related quality of life is known to be related to lifestyle risk factors and may, together with psychological distress, be improved by modifying these risk factors.32–35 However, symptoms of depression and anxiety are associated with a less frequent modification of lifestyle,36 which is in line with our findings on dropout. Consequently, those patients who are most likely to benefit from CR are unfortunately just the ones who are at high risk of dropout.

Another strong predictor of early withdrawal seemed to rely on the choice whether patients preferred to attend CR 2 or 3 times weekly. Supplementary analyses revealed differences in the patient profile. Patients who attended CR only twice per week were slightly older and more frequently women. Moreover, they had a worse clinical status and a lower exercise capacity at the start of CR, and they also differed in their psychosocial profile. Taken together, these demographic, clinical, and psychosocial characteristics, which did not influence withdrawal independently, actually may have an impact on dropout by influencing the choice of the training program. However, this should be subject of further investigations.

The impact of comorbidities and psychosocial factors on dropout emphasizes the importance of a multidisciplinary approach in CR, not only focusing on the heart disease but also taking into account all different aspects of the physical and mental well-being of a patient. In addition, development of a risk stratification instrument, based on predictors for dropout, would be helpful to screen patients from the early onset of CR and to provide an individual-tailored approach for those who are at risk of dropout.

This study has several strengths but also limitations. First, all patients were prospectively included at the beginning of the CR program, regardless of the recruiting diagnosis. Together with the relatively large sample size, our study population may therefore be a fairly good representation of the “real life” CR population. Another strength may be that we did not rely on the reasons for dropout given by the patients, but we have prospectively collected potential predictors at the beginning of CR to see what their impact was on the course of the program. Despite the broad spectrum of included variables, some interesting variables are lacking such as information on illness cognition and perception and the influence of the income, which have been demonstrated to be important in literature.9,12,14,23 Only 21% of the variation was explained, suggesting that other unknown factors are involved in this complex issue. The variation in the organization of CR across countries (eg, the number of sessions) may be a limitation for the generalization of our results. Nevertheless, the importance of comorbidities and a vulnerable psychosocial background in dropout may also be of interest in other cultures and countries.

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Conclusions

This study aimed to evaluate potential predisposing factors for dropout in CR. Neither demographic characteristics nor clinical staus or exercise capacity could independently identify patients who were at risk of dropout. The presence of comorbidities and a vulnerable psychosocial background rather seem to play a key role in dropout.

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What’s New and Important?

  • Twenty percent of CR participants drop out early.
  • Comorbidities and psychosocial factors play a key role.
  • Special attention to these patients is needed to ensure they continue CR.
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Acknowledgments

The authors thank the CR team of AZ Maria Middelares Ghent and Onze-Lieve-Vrouw Hospital Aalst for their contribution to data collection.

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REFERENCES

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Keywords:

cardiac rehabilitation; comorbidities; dropout; exercise capacity; psychosocial characteristics

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