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.
EFFECTS OF INCLUSION OF HV PATIENTS
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.
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.
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.
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.
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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Keywords:Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.
cardiac rehabilitation; patient characteristics; risk factors; time