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Clinical and Demographic Trends in Cardiac Rehabilitation


Gaalema, Diann E. PhD; Savage, Patrick D. MS; Leadholm, Kevin MD, MPH; Rengo, Jason MS; Naud, Shelly PhD; Priest, Jeffrey S. PhD; Ades, Philip A. MD

Journal of Cardiopulmonary Rehabilitation and Prevention: July 2019 - Volume 39 - Issue 4 - p 266–273
doi: 10.1097/HCR.0000000000000390
Cardiac Rehabilitation

Purpose: Clinical interventions in programs such as cardiac rehabilitation (CR) are guided by clinical characteristics of participating patients. This study describes changes in CR participant characteristics over 20 yr.

Methods: To examine changes in patient characteristics over time, we analyzed data from 1996 to 2015 (n = 5396) garnered from a systematically and prospectively gathered database. Linear, logistic, multinomial logistic or negative binomial regression was used, as appropriate. Effects of sex and index diagnosis were considered both as interactions and as additive effects.

Results: Analyses revealed that mean age increased (from 60.7 to 64.2 yr), enrollment of women increased (from 26.8% to 29.6%), and index diagnosis has shifted; coronary artery bypass surgery decreased (from 37.2% to 21.6%), whereas heart valve repair/replacement increased (from 0% to 10.6%). Risk factors also shifted with increases in body mass index (28.7 vs 29.6 kg/m2), obesity (from 33.2% to 39.6%), hypertension (from 51% to 62.5%), type 2 diabetes mellitus (from 17.3% to 21.7%), and those reporting current smoking (from 6.6% to 8.4%). Directly measured peak aerobic capacity remained relatively stable throughout. The proportion of patients on statin therapy increased from 63.6% to 98.9%, coinciding with significant improvements in lipid levels.

Conclusions: Compared with 1996, participants entering CR in 2015 were older, more overweight, and had a higher prevalence of coronary risk factors. Lipid values improved substantially concurrent with increased statin use. While the percentage of female participants increased, they continue to be underrepresented. Patients with heart valve repair/replacement now constitute 10.6% of the patients enrolled. Clinical programs need to recognize changing characteristics of attendees to best tailor interventions.

Cardiac rehabilitation programming is guided by clinical characteristics. Compared to 1996, participants in 2015 were older, more overweight, and had a higher prevalence of risk factors. Lipid values improved with increased statin use. While female participation increased, women continue to be underrepresented. Patients with heart valve repair/replacement now constitute 11.4% of enrollees.

University of Vermont College of Medicine, Burlington (Drs Gaalema, Naud, Priest, and Ades); and University of Vermont Medical Center, Burlington (Messrs Savage and Rengo and Drs Leadholm and Ades).

Correspondence: Diann E. Gaalema, PhD, University of Vermont College of Medicine, 1 S. Prospect St, UHC OH3 MS482, Burlington, VT 05405 (

The authors declare no conflicts of interest.

Appropriate clinical care should reflect the characteristics of the patients who will receive it. As the population ages,1 and proportions of risk factors change (eg, significant decrease in smoking),2 clinical care must change as well. Only by being cognizant of the changing demographic and clinical profiles of patients can clinical programs deliver appropriate care and interventions. In this research, we used cardiac rehabilitation (CR) as an illustration of how clinical populations are changing over time and how those changes may affect the care that is offered.

Contemporary outpatient CR began in the early 1970s when exercise programs were extended beyond hospital discharge to highly structured, physician-supervised, and electrocardiographically monitored exercise programs.3 The focus was almost entirely on exercise training to reverse physical decline resulting from extended bed rest, and participation was mostly limited to middle-class men younger than 65 yr with uncomplicated myocardial infarction (MI) or coronary bypass surgery (CABG). Since that time, CR has proven highly effective in broader populations, decreasing cardiac mortality by 26% over 3 yr and hospitalizations by 31% in the year after an acute coronary event.4 These positive health outcomes have been shown for a variety of diagnoses, and CR programs grew to include chronic stable angina pectoris and heart transplant.5 More recently, in 2006, heart valve (HV) replacement/repair and percutaneous coronary intervention (PCI) (without MI) were added as eligible diagnoses, followed by chronic systolic heart failure (HF) in 2014 and peripheral artery disease (PAD) in 2017.6–9 Thus, presently, referral to CR is a class 1-A recommendation of the American Heart Association.10

Contemporary CR has evolved into a multidisciplinary disease management program guided by case managers,11–13 and treatment is individualized on the basis of patient characteristics. Exercise programming and counseling vary substantially from an 83-year-old frail woman participant to a 55-year-old but overweight male participant. Similarly, programming is significantly different for an individual with early-onset coronary heart disease (CHD) and multiple coronary risk factors versus an older individual with isolated aortic valvular disease in the absence of CHD or left ventricular dysfunction.

There has also been increased effort to include a greater proportion of eligible patients.6 Historically women, older adults, minorities, and patients of low-socioeconomic status have been underrepresented.14 This is despite studies demonstrating that these patients similarly benefit from participation in CR.15–19 As more patients are enrolled (eg, more women), clinical characteristics may also shift.

Over time, CR programs have needed to adapt as patient demographics shifted and diagnoses of participants have expanded.20 To provide the requisite care and programming, understanding the characteristics of patients entering CR is critical. To this end, we examined the demographic and clinical characteristics of patients entering a CR program over a 20-yr period from 1996 to 2015.

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We analyzed prospectively gathered data on 5396 consecutive individuals entering the University of Vermont Medical Center phase 2 CR program between January 1996 and December 2015. For patients with multiple admissions to CR, only data from the initial enrollment were included. During the initial visit, we collected data on age, sex, anthropometric variables (stadiometer obtained height in meters, digital scale measured weight kilograms, and tape measured waist circumference in centimeters [measured at the level of the umbilicus]), conventional risk factors (type 2 diabetes mellitus [DM], diagnosed by medical history and use of hypoglycemic medication; hypertension (HTN), diagnosed by medical history), current smoking, and fasting lipid levels (total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], and triglycerides [TG]). Measures of TC, TG, HDL-C, and glucose were obtained in the morning following a 12-hr fast before starting CR. LDL-C was calculated by using the formula of Friedewald et al.21 Index diagnosis at entry into CR was recorded in a hierarchal fashion (ie, all patients with CABG for any indication were coded as CABG, those with MI without CABG were coded as MI, those without CABG/MI who received PCI were coded as PCI, followed by those who have their angina treated medically and other diagnosis, such as congestive heart failure or valvular heart disease). Information on treatment with evidence-based cardiovascular drugs (antiplatelet agents, β-adrenergic blockers, statins, and angiotensin-converting enzyme inhibitors or angiotensin receptor blockers) was recorded. All patients underwent a symptom-limited treadmill test with collection and analysis of expired gas for determination of peak aerobic capacity (peak

O2) in mL/kg/min (relative) or in L/min (absolute). Comorbidity scores were calculated with a scale used in prior studies but that has been not formally validated.22 A comorbidity score was calculated as a combination of the presence and severity of conditions that could limit activities within the CR program including chronic obstructive pulmonary disease, peripheral vascular disease, orthopedic problems, and cerebrovascular accident/stroke. Presence of a comorbid condition was obtained via electronic health record, whereas severity was determined by direct observation during the baseline exercise test. Absence of a comorbid condition was given a score of 0. For each diagnosis, a score of 1 to 3 signified (1) not symptomatic during exercise, (2) symptomatic during exercise, or (3) exercise-limiting.22

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Frequencies and means are presented visually by four 5-yr time periods (1996-2000, 2001-2005, 2006-2010, and 2011-2015) based on date of entry into CR. In the statistical analyses, however, time was modeled as a linear trend, with year as a continuous variable. The type of regression analysis used depended on outcome type: linear regression analysis for continuous outcomes, logistic or multinomial logistic regression for categorical outcomes, and negative binomial regression for count data (number of comorbidities). The impact of patient sex and index diagnosis (valve replacement vs other) on time trends was considered both as interactions and as additive effects; in other words, whether trends differed significantly in slope/trajectory over time and whether there was a group difference that was consistent over time. All analyses were conducted using SAS 9.4 (SAS Institute). Across all tests, statistical significance was defined as P < .05 (2-tailed).

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Over the 20 yr, there were significant changes in clinical characteristics of our total population (Table 1). Age increased significantly from 60.8 to 64.2 yr, with a yearly increase of 0.23 yr (t = 8.5, P < .0001). Accordingly, the percentage of the population 65 yr or older significantly increased from 40.2% to 49.2%, with a yearly increase of 0.6% (χ2 = 21.6, P < .0001), and the percentage of 75 yr or older increased from 11.2% to 19.9%, with a yearly increase of 0.4% (χ2 = 18.6, P < .0001). The percentage of women increased as well from 26.8% to 29.6%, with a yearly increase of 0.3% (χ2 = 7.7, P < .05). Both body mass index (BMI) and the percentage of patients considered obese (BMI >30) increased significantly (from 28.7 to 29.6, yearly increase 0.07, t = 5.1, P < .0001; and from 33.2% to 39.6%, yearly increase 0.5%, χ2 = 15.4, P < .0001, respectively). Mean weight and waist circumference did not change significantly over time. However, this is due to an increasing proportion of women over time. When controlling for sex, both weight and waist circumference increase significantly over time (all P values < .01).

Table 1

Table 1

There were also significant increases in the prevalence of cardiac risk factors (Table 1). Prevalence of DM increased significantly from 17.3% to 21.7%, with a yearly increase of 0.3%, (χ2 = 9.4, P < .05). Those with a diagnosis of HTN have increased from 51% to 62.5%, with a yearly increase of 0.8%, (χ2 = 48.1, P < .0001). Those reporting current smoking have increased from 6.6% to 8.4%, with a yearly increase of 0.2% (χ2 = 7.1, P < .05). Comorbidity scores also significantly increased from 0.3 to 0.6, with a yearly increase of 0.02 (χ2 = 25.8, P < .0001). Baseline aerobic capacity did not change over time. This lack of change persisted even when controlling for sex, age, and diagnosis.

There have also been significant changes in the index diagnosis (χ26 = 371.71, P < .0001; Table 2). The percentage of CABG patients decreased substantially when comparing time periods 1 and 4, from 37.2% to 21.6%, with a yearly decrease of 1.1%. Those entering the program due to medically treated angina also decreased from 5.4% to 1.5%, with a yearly decrease of 0.29%. These changes mirrored significant increases in MI (from 29.7% to 39.6%), PCI for angina (from 18.6% to 22.7%), and HV surgery, which went from 0% to 10.6% of the population.

Table 2

Table 2

Substantial changes have occurred in the percentage of patients taking cardiovascular medications at entry to CR (Table 3). Use of all 4 medications tracked increased. Antiplatelet agent use increased from 91.7% to 98.3%, with a yearly increase of 0.5% (χ2 = 71.8, P < .0001), β-adrenergic blockers increased from 68.7% to 82.7%, with a yearly increase of 1% (χ2 = 95.71, P < .0001), and angiotensin-converting enzyme inhibitors or angiotensin receptor blockers increased from 29.8% to 52.9%, with a yearly increase of 1.6% (χ2 = 180.5, P < .0001). The most striking change was seen in statin use, which increased from 63.6% to 98.9%, with a yearly increase of 2.7% (χ2 = 627.83, P < .0001). The effect of this increase in statin use is reflected in the lipid levels seen in the Figure. At entry into CR, the mean ± standard deviation values for TC (189 ± 40 vs 161 ± 44 mg/dL), TG (198 ± 128 vs 145 ± 112 mg/dL), and LDL-C (113 ± 34 vs 89 ± 37 mg/dL) all decreased whereas those for HDL-C increased (39 ± 10 vs 43 ± 13 mg/dL) (all P values < .0001) from time period 1 to time period 4.



Table 3

Table 3

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A significant main effect of sex was seen for several characteristics (Table 1). Overall, mean age was 3 yr older in the female patients (t = 8.63, P < .0001). Weight and waist circumference were significantly lower in females (14.5 kg lower, t =−29.46, P < .0001; and 8.5 cm lower, t =−18.91, P < .0001, respectively). However, BMI and percent considered obese did not differ significantly by sex.

Both DM and current smoking also did not differ significantly by sex. However, HTN was more common in females (11% higher, χ2 = 11.87, P < .001) and they had higher comorbidity scores (χ2 = 5.47, P = .02). Fitness level was significantly lower in females. Women had a peak

O2 that was on average 3.8 mL O2/kg/min lower than men (t =−23.09, P < .0001).

Index diagnosis differed by sex (Table 2). Patients with a diagnosis of CABG were more likely to be male (11% higher, χ2 = 48.93, P < .001), and patients with MI and HV were more likely to be female (5% higher, χ2 = 25.32, P < .001; and 4% higher, χ2 = 109.57, P < .001, respectively).

Medication use differed in some respects by sex (Table 3). Women were less likely to be taking antiplatelet agents or statins (10% fewer, χ2 = 15.12, P < .001; and 8% fewer, χ2 = 14.22, P < .001, respectively). However, the significant interaction (sex and time) in antiplatelet use and the nonsignificant differences in the use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers and β-adrenergic blockers, as well as visual examination of the data, demonstrate that over time prescription of all 4 medications have increased and differences by sex are dissipating.

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Given that the percentage of HV patients has changed dramatically over the 20-yr period, the inclusion of these patients has shifted the characteristics of the population. Accordingly, data were examined for main effects of inclusion of HV patients as well as potential interactions between time and inclusion of these patients. Table 4 demonstrates how characteristics differ if HV patients were separated out.

Table 4

Table 4

A significant main effect of diagnoses was seen on several characteristics (Table 4). Mean age was 2.3 yr higher in HV patients than in patients with other diagnoses (t = 2.9, P < .05). The percentage of women was higher as well (11% higher, χ2 = 15.02, P < .001). Weight, waist circumference, BMI, and percent considered obese all were lower in the HV population (8 kg lower, t =−6.61, P < .0001; 7.2 cm lower, t =−6.6, P < .0001; 2.1 lower, t =−5.93, P < .0001; and 12% lower, χ2 = 16.77, P < .0001, respectively).

Risk factors and clinical characteristics differed by HV status. HV patients were less likely to have traditional CAD risk factors. Current smoking and DM were significantly lower in HV patients (3% lower, χ2 = 7.83, P < .05; and 11% lower, χ2 = 22.48, P < .0001, respectively). Peak aerobic capacity, at entry, differed by diagnosis. Peak

O2 for HV patients was, on average, 1.4 mL O2/kg/min lower than non-HV patients (t =−3.74, P < .001). HTN is the only characteristic that had a significant interaction between time and diagnosis, which increased over time in patients with other diagnoses but decreased in HV patients (χ2 = 16.77, P < .0001). However, this is likely an artifact of HV patients only being included in the last 2 time periods.

Given these differences in risk factors, medication usage also differed between HV patients and patients with other diagnoses. The use of antiplatelet agents and angiotensin-converting enzyme inhibitors or angiotensin receptor blockers was significantly lower in HV patients (21% lower, χ2 = 20.72, P < .0001; and 14% lower χ2 = 38.54, P < .0001, respectively). However, neither β-adrenergic blockers nor statin use differed significantly in the HV patients compared with those with other diagnoses.

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In the present study of >5000 individuals entering CR between the years of 1996 and 2015, we found that patients have become older, more overweight, and more likely to have risk factors such as DM and current smoking. Patients also have more comorbidities, and a greater percentage of patients were women. Yet, fitness measures were essentially unchanged. The type of patients entering CR has shifted the overall clinical profile. While still underrepresented, more women are entering CR. The women are significantly older, and less fit, and with more comorbidities than men, which shift the overall clinical sample accordingly. In addition, coinciding with the introduction of drug-eluting stents in 2003, there has been an increase in the percentage of patients undergoing PCI and a resulting decrease in the numbers undergoing CABG.23 Other clinical changes have likely affected clinical characteristics. For example, the American Heart Association Get with the Guidelines Initiative may have increased statin prescription and use.24 Finally, the inclusion of other patients (eg, HV patients) also shifts the characteristics of the sample. HV diagnoses increased from 0% to 10.6% in the entrants to CR. HV patients do not necessarily have clinical CHD and differed compared with the traditional population, being less obese, less likely to have DM, less likely to smoke, and older. As a result, HV patients alter the clinical and demographic characteristics of CR (Tables 1 and 4). For example, if HV patients were excluded, the percentage of patients categorized as obese would have increased from 33.2% to 41.2% (instead of 39.6%).

This data set demonstrates how a change in entry diagnoses, such as the inclusion of HV patients, can significantly change the characteristics of a clinical population. Changes such as this one is likely to continue with inclusion of other populations, such as patients with a diagnosis of HF (added 2014) and patients with symptomatic PAD (added 2017). These changes will likely further increase the heterogeneity of the CR population. In addition, CR continues to be underutilized, with only 35.5% of people who survived an MI attending CR.25 If efforts to increase CR participation are successful, programs will likely see patients with different sets of characteristics than are seen currently. Currently, underrepresented in CR are patients with some of the highest-risk profiles, such as lower-socioeconomic status patients and non–English-speaking individuals.26

CR programming depends upon the characteristics of patients and goals of therapy, related to both physical functioning and prevention of future cardiac events. Historically, most CR patients have had CHD and a large body of literature has demonstrated that improving fitness and decreasing cardiac risk factors yield improved clinical outcomes.4 As other diagnoses are included, patients will have different needs. As demonstrated, HV patients differ from other CHD patients, being older and less aerobically fit. Consequently, training programs for HV patients might have increased focus on improving aerobic fitness and strength rather than on lowering cholesterol or losing weight. Those who have CHF not only will work on improving fitness but will also need education specific to managing their unique clinical characteristics.27,28 PAD patients are more likely to smoke and have DM and thus will need specific interventions around those risk factors. In addition, the focus of their exercise training differs, as specific training protocols have been shown to increase time until onset of claudication and overall walking time.29 It will continue to be important to adjust CR programming based upon the diverse needs of an increasingly heterogeneous patient population. Staffing requirements will need to be considered as well. Given the increase in patient heterogeneity, programs could benefit from having staff with diverse skill sets and able to handle the unique needs of patients with different medical needs. The ability to individualize treatment plans will need to increase. Patient complexity will also differ, suggesting a potential need for increasing staffing ratios. The greater prevalence of obesity, DM, smoking, and comorbidities will require behavioral programs for weight loss, close monitoring of DM, and increasing expertise at smoking cessation interventions. Programs need to ensure that patient needs are addressed and care is delivered in a safe and appropriate fashion with a focus on individualizing treatment plans to optimize patient outcomes.

Limitations of this study include that it was performed at a single, community-based, university-affiliated center with a relatively homogeneous population. Medical history was used to define some variables (eg, DM and HTN), possibly underestimating the true prevalence of these conditions. In addition, diagnostic criteria for these conditions have changed over time. Detailed information on glycosylated hemoglobin levels and blood pressure was not available, although there were no clinically meaningful changes in blood pressure in the sample during the 20-yr time period. Diagnostic categories were not comprehensive because, for example, in the MI category, the proportions of non–ST-segment elevation MI or ST-segment elevation MI were not known and the proportion of PCI or other treatment following MI was not presented. Furthermore, we decided to classify CABG and MI as mutually exclusive categories due to the profound impact of CABG upon patient recovery. In addition, given how diagnoses were categorized, we were unable to detail how characteristics changed within a single diagnosis or within combinations of diagnoses. Detailed psychosocial characteristics were not available for the whole time period, but depression scores were stable throughout. Finally, smoking status was determined by patient history and self-report.

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Over a 20-yr period, cardiac patients entering CR have become older, more obese, and have a higher prevalence of coronary risk factors. Lipid values have improved remarkably associated with the increasing use of statin medications. Diagnoses of patients enrolled have changed, with the percentage of patients enrolling in CR after CABG decreasing (from 37.2% to 21.6%), HV patients now constituting more than 10% of patients, and patients with other diagnoses, such as PCI, having increased as well. Given the diversity of and changing demographics throughout the United States, a consistent monitoring of age, sex, and diagnosis within individual programs will need to take place to tailor interventions to specific patient needs.

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This research was supported in part by National Institutes of Health Center of Biomedical Research Excellence award P20GM103644 from the National Institute of General Medical Sciences and Tobacco Centers of Regulatory Science award P50DA036114 from the National Institute on Drug Abuse and the US Food and Drug Administration. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the US Food and Drug Administration.

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cardiac rehabilitation; patient characteristics; risk factors; time

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