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Int J Cardiol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: Int J Cardiol. 2016 January 1; 202: 804–809. doi:10.1016/j.ijcard.2015.09.116.
Medical Factors that Predict Quality of Life for Young Adults with Congenital Heart Disease: What Matters Most? Jamie L. Jackson, PhDa,b, Lauren Hassen, MD, MPHc, Gina M. Gerardo, BSa, Kathryn Vannatta, PhDa,b, and Curt J. Daniels, MDd,e aCenter
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for Biobehavioral Health, Nationwide Children’s Hospital, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data, presented and their discussed interpretation bDepartment
of Pediatrics, Ohio State University, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data presented and their discussed interpretation
cCollege
of Medicine, Ohio State University, Columbus, Ohio
dColumbus
Ohio Adult Congenital Heart Disease Program, The Heart Center, Nationwide Children’s Hospital, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data presented and their discussed interpretation
eDepartments
of Internal Medicine and Pediatrics, Ohio State University, Columbus, Ohio
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Abstract Background—Identify demographic and medical status indicators that account for variability in physical and emotional health-related quality of life (QoL) among young adults with congenital heart disease (CHD) as compared to traditional lesion severity categories. Methods—Cross-sectional study of 218 young adult survivors of CHD (mean = 25.7, SD = 7.1 years). Participants were recruited from pediatric and adult CHD clinics at a pediatric and an adult hospital. Stepwise linear regression examined the unique contribution of demographic (age; sex; estimated income) and medical status indicators (comorbid conditions; treatment modality; ventricular function/functional capacity) on QoL compared to traditional lesion severity categories (simple; moderate; complex).
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Results—Lesion severity category accounted for a small portion of the variance in physical QoL (3%), but was not associated with emotional QoL. Lesion severity did not significantly contribute to the variability in physical QoL once other variables were entered. Having an estimated income of ≤ $30,000, taking more than one cardiac-related medication, and having a New York Heart
Corresponding Author: Jamie L. Jackson, PhD; 700 Children’s Drive, JWest 4th Floor, Columbus, OH 43205; Phone: 614-722-3585, Fax: 614-722-3544, [email protected]. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Conflict of Interest: None.
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Association (NYHA) functional class designation > I was associated with poorer physical QoL and explained 23% of the variability. NYHA class was the only variable that explained a unique proportion of variance (7%) in emotional QoL, and having a NYHA class designation > I was associated with greater risk for poorer emotional functioning. Conclusions—Findings indicated that several indicators readily available to treatment teams may provide important information about the risk for poor patient-reported outcomes of physical and emotional QoL among CHD survivors. Keywords congenital heart defects; quality of life
1. BACKGROUND Author Manuscript Author Manuscript
More than 90% of infants with congenital heart disease (CHD) now survive into adulthood, resulting in over 1 million adults with CHD living in the United States [1]. Many survivors of CHD encounter a range of physical symptoms, require complex interventions, and undergo repeated hospitalizations as part of managing their condition, placing greater burden on the healthcare system [2,3]. Survivors of CHD are also at risk for significant costly complications as they age, including heart failure, arrhythmias, and stroke [3]. In addition, individuals with CHD may experience emotional distress over time, which could interfere with daily functioning and role performance [4–6]. Therefore, there are many aspects of managing CHD that could negatively impact the physical and emotional domains of survivors’ perceptions of health, also known as health-related quality of life (QoL). QoL, not to be confused with health status, is a multidimensional construct of an individual’s evaluation of their physical and emotional functioning as impacted by perceptions of disease burden [7–9]. Because QoL is an independent predictor of mortality [10,11] and future cardiac events [12,13] among other cardiovascular disease populations, the use of patientreported outcomes in clinical research [14] has become vital and may offer medical providers valuable insight into risks for poor outcomes earlier in treatment.
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Historically, CHD severity has been categorized as “simple,” “moderate” or “complex” to simplify communication between medical providers [15]. While useful in conveying basic information about an individual’s lesion, the utility of this categorization system as a predictor of QoL has been challenged, especially when compared to other measures of functional status [16,17]. Individual differences in response to treatment, as well as variation in the trajectory of the disease over time, result in significant heterogeneity within these categories. In particular, the group designated as “moderate” includes a large spectrum of lesions, some of which may never require repair, while others may result in staged surgical interventions and necessitate management with multiple medications and frequent monitoring. Equivocal findings have been reported on the relationship between traditional lesion severity categories and QoL [16,18–22], and those that have reported an association only identify a relationship with physical QoL [21,22]. Other indicators of medical status have also been examined in relation to QoL, but often in isolation and with varying degrees of methodological rigor. A recent literature review of
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QoL among individuals with CHD showed equivocal findings between those with CHD and the general population, presumably due to differences in QoL assessment tools that may vary in content and psychometric properties [23]. Physical functioning was twice as likely to be negatively impacted (17/22 studies) than psychosocial functioning (7/23 studies). Authors concluded that several medical status indicators were associated with QoL, such as cyanosis, exercise capacity, and ventricular function/functional capacity, but the relationships were inconsistent across studies. Variations in how medical status indicators were measured may have contributed to the equivocal findings. Strikingly, most studies have only examined the relationship between one to three medical status indicators and QoL, leaving in question the amount of variability accounted for by other potentially important indicators. By studying medical indicators in relative isolation, it becomes difficult to evaluate their unique predictive value, that is determine whether the contributions of the medical status variables overlapped in explaining levels of QoL.
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Therefore, the aim of the current study was to compare the traditional disease classifications for CHD (simple, moderate and complex) to a variety of common medical status indicators (demographics, cardiac-related conditions, treatment modalities, and ventricular function/ functional capacity) to determine which indicators(s) of medical status would explain the most unique variability in physical and emotional QoL among adolescents and adults with CHD. The indicators of medical status selected have either been previously examined in relative isolation or not yet included as predictors of QoL in previous studies. Results from this study have important clinical implications for identifying patients at risk for poorer QoL using readily available demographic and medical status indicators, therefore allowing patients earlier access to additional resources and referrals.
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2. METHODS 2.1 Participants and Procedures
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Participants were recruited from outpatient cardiology clinics at both a pediatric and adult Midwestern hospital from May, 2012, to December, 2013. Participants were enrolled in a larger survey study examining disease knowledge, QoL, coping, and health behaviors among adolescent and young adult survivors of CHD. All patients who were eligible and had a scheduled clinic appointment were contacted over the phone for recruitment. Criteria for eligibility included: (1) the presence of a structural congenital heart defect, (2) age 15 to 39 years, and (3) the ability to read/write in English. Patients were excluded if diagnosed with a broader genetic syndrome that had cardiac involvement (e.g., Down, Marfan, etc.) or had cognitive impairments that would impede their ability to complete the measures. Of the 281 individuals approached, 18 declined, resulting in a recruitment rate of 94%. All participants provided informed consent. Only the young adult participants from this sample were included in the current study. Participants completed an online measure of QoL. Medical charts were reviewed at the time QoL was measured to abstract information about demographics, diagnosis, comorbidities, treatment history, and cardiac ventricular function/functional capacity. The study protocol was approved by the local Institutional Review Board and conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Int J Cardiol. Author manuscript; available in PMC 2017 January 01.
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2.2 Quality of Life
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The Medical Outcomes Study SF-36v2 was used to assess the impact of physical (PCS) and emotional (MCS) symptoms on daily living. The SF-36v2 is a self-report measure of healthrelated QoL as perceived by the participant. For each component scale, scores range from 0– 100 with higher scores indicating better QoL in that domain. The SF-36v2 is a commonly used tool across the United States and Europe, including in CHD [24], and has strong psychometric properties [25]. The current study has met recent recommendations set forth by Bratt & Moons (2015) for QoL research [26]. If able, missing items were prorated according to the directions of the measure. As noted in Table 1, 208 participants have QoL scores. Three participants were missing too many items to be eligible for proration and seven participants did not complete the measure due to an error in the online survey system. 2.3 Medical Chart Review
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Lesion severity classification was determined based on diagnosis as outlined by the American College of Cardiology/American Heart Association 2008 guidelines [27]. Demographic variables, such as age and sex, were also abstracted from medical charts. Participants’ estimated family income was derived by the Federal Financial Institutions Examination Council (FFIEC) 2013 Census estimate based on participants’ home address. The FFIEC is a government body that uses loan application information, in conjunction with self-reported federal census data to estimate family income within a particular geographic census tract. Other medical chart variables recorded included indicators of medical status for the following categories: cardiac-related conditions, treatment modalities and ventricular function/functional capacity.
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For cardiac-related conditions, the following variables were determined (yes/no) from participants’ medical chart: diagnosis of an arrhythmia (i.e., a sustained atrial or ventricular arrhythmia that required evaluation), diagnosis of heart failure, history of stroke, established coronary artery disease, history of aortic enlargement (i.e., an aorta > 4 centimeters in circumference), diagnosis of pulmonary arterial hypertension (as determined during catheterization), diagnosis of arterial hypertension, and cyanosis at the time of study enrollment. In addition, the number of conditions was tallied to create a total continuous measure of cardiac-related conditions. Body mass index (BMI) was calculated for each participant, and BMI was compared to age norms for adolescent participants. Participants were categorized as “underweight” (BMI ≤ 18.4), “normal” (BMI = 18.5–24.9), “overweight” (BMI = 25.0–29.9), or “obese” (BMI ≥ 30.0) as outlined by the World Health Organization [28].
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Treatment modality variables collected for the current study included the presence of a pacemaker or implantable cardioverter defibrillator (yes/no), number of cardiac-related hospitalizations in the past 5 years, lifetime number of catheterizations, number of openheart surgeries, and number of cardiac-related medications (not including antibiotic prophylaxis for endocarditis). Measures of ventricular function/functional capacity were also recorded, including the systemic left ventricular ejection fraction (LVEF) class, New York Heart Association
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(NYHA) functional class, and peak V02. Few participants with systemic right ventricles (N = 27) or single ventricle lesions (N = 10) had ejection fraction values available and therefore were not included in the analyses. 2.4 Plan of Analysis
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First, non-directional bivariate correlations were computed to determine the relationship between lesion severity classification, demographics, and indicators of medical status with QoL. Variables that were significantly associated with QoL were then included in two stages of forward stepwise linear regressions to determine which variables explained the most unique variance in each domain of QoL (physical [PCS] and emotional [MCS] functioning). Stepwise regression is optimal for this situation because variables that provide redundant information are removed from the model. For the first stage of the regressions (Stage 1), separate stepwise regression models were conducted regressing either physical or emotional QoL on predictors within 1) demographics, 2) cardiac-related conditions, 3) treatment modalities, and/or 4) ventricular function/functional capacity. Stage 1 analyses served to identify a limited number of predictors within each category of demographic and medical status indicators that accounted for a significant portion of unique variance in physical and emotional QoL. Stage 2 of the stepwise linear regressions consisted of adding all indicators that significantly accounted for unique variance in Stage 1 into one model to determine which variables explained unique variance in QoL across demographic and medical status indicators. If lesion severity was significantly correlated with a domain of QoL, it was also included in the combined Stage 2 regression model. Once the final models were determined for each domain of QoL, categorical variables were further explored posthoc using either ttests or analysis of variance (ANOVA) with posthoc Tukey tests to identify the level of the variable at which QoL was significantly reduced. Variables that were unevenly distributed across levels were collapsed and analyses were re-run. The collapsed variables did not change the outcomes of the analyses; therefore, the original levels of all variables are reported. Analyses were performed by two of the co-authors (JL, LH).
3. RESULTS Participants included 218 young adult (range = 18–39 years) survivors of CHD with a variety of diagnoses representing simple, moderate and severe lesion severities (Table 1). Means, standard deviations, percentages, and ranges for demographics, QoL, and the medical variables can be seen in Table 2. 3.1 Physical QoL
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Correlations and the results of Stages 1 and 2 of the stepwise linear regressions for physical QoL are shown in Table 3. The relationship between categories of lesion severity and physical QoL suggested that more complex disease is associated with poorer physical QoL (r = −0.16, p = 0.023). Further examination of lesion severity across classifications using ANOVA (F[2, 205] = 2.76, p = 0.066) and Tukey posthoc analyses suggested a trend for those with complex lesions to
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report poorer physical QoL (M = 47.23, SD = 8.10) than those with simple (M = 50.89, SD = 9.26) or moderate (M = 49.73, SD = 9.28) lesion types. Correlations indicated that demographic variables, as well as several variables within the domains of cardiac-related conditions, treatment modality and ventricular function/ functional capacity, were significantly negatively associated with physical QoL. Once those variables were entered into stepwise regression models (Stage 1), results suggested that the following demographic and medical status indicators accounted for a significant portion of unique variance in physical QoL: 7% demographics (age, estimated family income), 16% cardiac-related conditions (diagnosis of heart failure), 15% treatment modalities (number of cardiac medications), and 22% ventricular function/functional capacity (NYHA class).
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Lesion severity, demographic variables, and medical status indicators were subsequently combined into a final stepwise regression (Stage 2), revealing that three variables accounted for 23% of the unique variance in physical QoL: estimated family income, taking a greater number of cardiac-related medications, and having a higher NYHA class. Further examination of estimated family income suggested the following breakdown: ≤ $30,000 (6%), $30,001-$50,000 (30%), $50,001-$75,000 (37%), and > $75,000 (27%). Less than $30,000 was chosen for the lowest income cutoff because the U.S. Census Bureau sets the poverty threshold for a family of five people at $28,265. Results of an ANOVA (F[3,202] = 3.03, p = 0.031) with Tukey posthoc tests comparing the four income groups indicated that individuals with estimated family incomes of ≤ $30,000 (M = 43.10, SD = 7.89) reported poorer QoL than those with incomes above $75,000 (M = 51.36, SD = 8.47, 95% CIs [−15.50, −1.02]). A similar examination of cardiac-related medications suggested that 36% of the sample did not take medication, 23% took one, and 41% took two or more medications. An ANOVA (F[2,205] = 6.83, p = 0.001) and Tukey posthoc tests comparing groups who took zero, one, or two or more medications indicated that individuals taking two or more medications reported significantly poorer physical QoL (M = 46.54, SD = 8.99) than those taking no medications (M = 51.46, SD = 8.45, 95% CIs [−8.19, −1.63]) or only one medication (M = 50.39, SD = 8.54, 95% CIs [−7.55, −0.13]). A similar examination of NYHA class revealed that 67% of the sample was given a NYHA class designation of I and 33% had a designation of II or higher. A t-test (t = 7.08, df = 202, p < 0.001, 95% CIs [6.20, 10.98]) confirmed that individuals with a NYHA class of II or greater reported significantly worse physical QoL (M = 43.34, SD = 9.68) than those with a NYHA class of I (M = 51.93, SD = 7.21). 3.2 Emotional QoL
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Several demographic and medical status indicators were significantly associated with emotional QoL (Table 4). Lesion severity category was not correlated with emotional QoL, and therefore was not included in the subsequent analyses. Stage 1 of the stepwise linear regressions identified the following variables as explaining a significant amount of the unique variance for emotional QoL (Table 4): 5% cardiac-related conditions (pulmonary arterial hypertension, number of cardiac-related conditions), 2% treatment modality (number of cardiac medications), and 7% ventricular function/functional capacity (NYHA class).
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For the combined stepwise regression model in Stage 2, only one medical status indicator continued significantly accounting for unique variance in emotional QoL: 7% NYHA class. Results of a t-test (t = 4.22, df = 202, p < 0.001, 95% CIs [3.64, 10.03]) confirmed that individuals with a NYHA class of II or greater report poorer emotional QoL (M = 43.76, SD = 12.95) than those who have a class of I (M = 50.59, SD = 9.61).
4. DISCUSSION
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The current study is the first to use a systematic statistical approach for determining the relative contribution of a large number of readily available demographic and medical status indicator variables in explaining the variability in QoL among adolescents and adults with CHD as compared to traditional lesion severity categories. Several variables significantly outperformed the lesion severity classification system in explaining patient-reported QoL outcomes, suggesting that an approach accounting for an individual’s current medical status, rather than the assigned disease severity category, is necessary for identifying patients at risk for poor QoL. In addition, some of the variance accounted for by the variables was shared, highlighting the importance of examining multiple specific demographic and medical status indicators simultaneously.
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Having an estimated family income of ≤ $30,000, taking two or more cardiac-related medications, and having a NYHA class of II or greater explained 23% of the variance in physical QoL. Among adults with CHD, lower self-reported family income has been associated with greater complaints of somatic symptoms [4] and worse exercise intolerance [29], while a higher income has been identified as a protective factor for loss to follow-up [30]. Conversely, Opić and colleagues (2015) compared adults with CHD to normative data in the Netherlands and found that adults with CHD had lower income as compared to norms, but reported higher QoL [31]. Opić and colleagues did not report the relationship between income and QoL. One possible explanation for why income may be associated with physical QoL is that individuals with more complex disease can experience significant symptoms (e.g., dyspnea, fatigue) that limit daily activities, including the ability to work. However, posthoc analyses suggested that income did not differ between illness severity groups. Eslami and colleagues (2013) speculated that low income may result in less access to care, thereby preventing patients from receiving proper treatments to mitigate symptoms [4]. This hypothesis should be explored further in future prospective studies.
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The number of medications as a contributor of unique variance for physical QoL is likely a reflection of the severity of the cardiac-related conditions. For example, a CHD survivor who has heart failure may be placed on additional medications if symptoms of heart failure worsen or cardiac functioning declines. An alternative interpretation for why the number of medications explains a significant portion of the variance in physical QoL is that individuals taking more medications have more cardiac-related conditions, which was confirmed by posthoc analyses (r = 0.65, p ≤ 0.001), and greater number of cardiac-related conditions was associated with poorer physical QoL (Table 3). Therefore, the number medications taken may be a reflection of the number of cardiac-related conditions significant enough to warrant taking medication. However, the shared variance between the number of
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medications and the number of cardiac-related conditions was accounted for when all of the variables were added to the model simultaneously.
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NYHA class accounted for the most unique variance in physical QoL, and having a NYHA class of II or greater was suggestive of risk for poorer patient-reported QoL. This was not surprising, given that NYHA class was designed as a clinical indicator of limitations in physical functioning related to cardiac function as reported by the patient. However, while NYHA class has been associated with poorer QoL in adults with CHD [15], as well as other cardiovascular disease populations (e.g., pulmonary arterial hypertension [32], coronary artery disease [33]), the amount of variance explained by NYHA class has not been simultaneously tested against a large variety of other potentially important medical status indicators. Posthoc analyses indicated that the presence of a heart failure diagnosis remains as a significant contributor to the variability in physical QoL when NYHA class was not included in the model (β = −0.24, p ≤ 0.001; adjusted R2 = 0.22, p = 0.008). This suggests that in the absence of NYHA class, having a diagnosis of heart failure could also be considered as a risk factor for poorer physical QoL. NYHA class was also the only variable, out of the 25 variables examined, to explain a significant portion of the variance in emotional QoL. This finding suggests that functional limitations due to disease severity are among the most important factors to consider for emotional well-being in this population and age group. Limitations in functional status also have predicted poorer emotional QoL among older individuals with heart failure [34], indicating that being symptomatic, and therefore likely unable to engage in activities that were once enjoyable, may have a particularly deleterious impact on emotional well-being. 4.1 Study Limitations
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The list of potentially important demographic and medical status indicator variables considered for this study was not exhaustive. Although age may partially capture time since diagnosis, this variable was not measured independently and may prove to be important when considering for how long a person has known about their condition. However, an individual diagnosed in adulthood, who was only aware of their condition after recent impairments in functional ability, may have a similar experience as someone diagnosed early in life who has lived relatively symptom-free until adulthood. Furthermore, there are likely other variables accounting for additional variance that were not measured in the current study. The variables selected for these analyses were thought to be most available to medical personnel in a clinic setting and to have greater clinical utility than additional measures that add time to the clinic visit (e.g., depression and anxiety inventories) or may be difficult to acquire (e.g., time on cardiac bypass). Another limitation of this study was the minimal representation of several medical variables, such as the presence of certain cardiacrelated conditions (e.g., stroke, coronary artery disease), presence of an ICD, and LVEF class. Despite the smaller number of participants with some medical variables, the current study had a high rate of recruitment (94%) from both pediatric and adult hospital settings, which is suggestive of a representative sample. Furthermore, posthoc power analysis indicated that the current study was sufficiently powered (1-β = 0.99) to detect small effect sizes (0.19) for multiple regression with α = 0.05 using the lowest available sample size (n =
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208) and the greatest number of variables included in the model (10). One potential limitation to study representativeness was the inclusion of only patients who were engaged in care, in that they were either attending yearly follow-up appointments or had experienced a medical event. Results from the current study may not be generalizable to individuals with CHD who are not engaged in care. 4.2 Conclusions
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Patient-reported QoL is an independent predictor of morbidity and mortality among individuals with various forms of cardiovascular disease [9–13]. Therefore, assessment of QoL, separate from consideration of health status indicators, is critical for optimizing patient-centered care. The current study demonstrated that adults with CHD who have an estimated income ≤ $30,000, take two or more cardiac medications, and have a NYHA class of II or greater, are at increased risk for poor physical QoL. Furthermore, those individuals with a NYHA class of II or greater are also at risk for poorer emotional QoL, which may include increased emotional distress. Poor QoL is suggestive of difficulty performing tasks of daily living, which has important implications for meeting healthcare needs, such as attending clinic appointments and engaging in other self-care activities. Therefore, declines in QoL likely affect morbidity and mortality through multiple avenues and should be closely monitored through screening. In sum, these findings suggest that practitioners may be able to utilize readily available data to better identify individuals at risk for poorer QoL earlier in treatment, resulting in faster referral to appropriate resources, including formal exercise programs, such as cardiac rehabilitation, or psychosocial professionals. Timely referrals of patients for these important services may ultimately reduce hospitalizations and optimize medical and psychosocial outcomes.
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Acknowledgments Grant support: this work was supported by National Institutes of Health grant T32 HL- 098039 (to J.L. Jackson), The Heart Center at Nationwide Children's Hospital (K. Vannatta and C.J. Daniels) and the Clinical and Translational Science Award grant UL1TR001070 at The Ohio State University and Nationwide Children's Hospital.
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the American Society of Echocardiography, Heart Rhythm Society, International Society for Adult Congenital Heart Disease, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J. Am. Coll. Cardiol. 2008; 52:e143–e263. [PubMed: 19038677] 28. Physical status: the use and interpretation of anthropometry. Report of a WHO expert committee. Vol. 854. Geneva: World Health Organ. Tech. Rep. Ser.; 1995. p. 1-452. 29. Diller G, Inuzuka R, Kempny A, et al. Detrimental impact of socioeconomic status on exercise capacity in adults with congenital heart disease. Int. J. Cardiol. 2013; 165:80–86. [PubMed: 21868115] 30. Mackie AS, Rempel GR, Rankin KN, Nicholas D, Magill-Evans J. Risk factors for loss to followup among children and young adults with congenital heart disease. Cardiol. Young. 2012; 22:307– 315. [PubMed: 22013913] 31. Opić P, Roos-Hesselink JW, Cuypers JAA, et al. Psychosocial functioning of adults with congenital heart disease: outcomes of a 30–43 year longitudinal follow-up. Clin. Res. Cardiol. 2015; 104:388–400. [PubMed: 25481819] 32. Vanhoof JMM, Delcroix M, Vandevelde E, et al. Emotional symptoms and quality of life in patients with pulmonary arterial hypertension. J. Heart Lung Transplant. 2014; 33:800–808. [PubMed: 24854567] 33. Ulvik B, Wentzel-Larsen T, Hanestad BR, et al. Relationship between provider-based measures of physical function and self-reported health-related quality of life in patients admitted for elective coronary angiography. Heart Lung. 2006; 35:90–100. [PubMed: 16543037] 34. Heo S, Moser DK, Chung ML, et al. Social status, health-related quality of life, and event-free survival in patients with heart failure. Eur. J. Cardiovasc. Nurs. 2012; 11:141–149. [PubMed: 21071279]
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Table 1
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Sample composition by diagnosis and surgical history Percentages Lesion Type BAV, COA
29%
ASD, VSD, AVSD, AVC
26%
Tetralogy of Fallot, DORV
21%
D-TGA, L-TGA
15%
Single Ventricle Fontan, PA
8%
Pulmonic Stenosis
8%
Anomalous Pulmonary Venous Return
3%
Ebstein’s Anomaly
3%
Other lesion
6%
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Cardiac Surgeries Valve replacement, valve repair
34%
Patch closure
25%
Shunt, Glenn
19%
Tetralogy of Fallot Repair
17%
Coarctation Repair
9%
Mustard, Senning
9%
Fontan
8%
Arterial switch
2%
Rastelli
2%
Maze
1%
Not available
17%
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ASD = atrial septal defect; AVSD = atrioventricular septal defect; AVC = atrioventricular canal; BAV = bicuspid aortic valve; COA = coarctation of the aorta; DORV = double outlet right ventricle; D-TGA = D-transposition of the great arteries; L-TGA; L-transposition of the great arteries; PA = pulmonary atresia; VSD = ventricular septal defect.
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Table 2
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Means, standard deviations, and ranges for demographics, medical chart variables, and measures of quality of life. Mean (SD) or %
Range
Lesion Severity Simple
25%
Moderate
42%
Complex
33%
Demographics Age
27.7 (6.1)
Sex (female)
18–39
55%
Estimated Family Income ($)
64,461 (28,869)
11,156–167,509
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Comorbid Conditions (yes) Arrhythmia
35%
Heart Failure
26%
HTN
17%
Aortic Enlargement
12%
Cyanosis
6%
Pulmonary Arterial HTN
5%
Stroke
3%
Coronary Disease
2%
Number of Cardiac-Related Conditions
1.1 (1.1)
0–6
BMI Classification
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Underweight
2%
Normal
42%
Overweight
28%
Obese
28%
Treatment Modalities Pacemaker (yes)
17%
ICD (yes)
7%
Cardiac-Related Hospitalizations
0.3 (0.9)
0–9
Catheterizations
1.4 (1.7)
0–10
Open-Heart Surgeries
1.6 (1.3)
0–6
Cardiac-Related Medications
1.6 (1.9)
0–9
Ventricular Function/ Functional Capacity Systemic LVEF Class
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I
91%
II
8%
III/IV
1%
NYHA class I
67%
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Mean (SD) or %
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II
Range
26%
III/IV
7%
Peak VO2
25.7 (43.8)
0–100
Physical Functioning (PCS)
49.2 (9.0)
21–66
Mental Functioning (MCS)
48.2 (11.5)
9–67
Quality of Life †
BMI = body mass index; EF = ejection fraction; HTN = hypertension; ICD = implantable cardioverter defibrillator; LVEF = left ventricle ejection fraction; NYHA = New York Heart Association; RVEF = right ventricle ejection fraction; VO2 = maximal oxygen consumption. † Sample size ranged from 216–218 for all variables with the exception of quality of life (N = 208), total number of catheterizations (N = 208), and systemic LVEF class (N = 161).
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Table 3
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Correlations and stepwise linear regressions demonstrating the strength of the relationships between demographics, medical chart variables, and physical functioning. Physical Functioning (PCS) β (P)
r (P) Stage 1 Lesion Severity Category
Stage 2
−0.16 (0.023)
Demographics Age
−0.18 (0.011)
Sex
−0.14 (0.044)
Estimated Family Income Adj
0.23 (0.001)
R2
−0.15 (0.023)
0.22 (0.002)
0.14 (0.020)
0.07
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Cardiac-Related Conditions Arrhythmia
−0.14 (0.039)
Heart Failure
−0.39 (
Int J Cardiol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: Int J Cardiol. 2016 January 1; 202: 804–809. doi:10.1016/j.ijcard.2015.09.116.
Medical Factors that Predict Quality of Life for Young Adults with Congenital Heart Disease: What Matters Most? Jamie L. Jackson, PhDa,b, Lauren Hassen, MD, MPHc, Gina M. Gerardo, BSa, Kathryn Vannatta, PhDa,b, and Curt J. Daniels, MDd,e aCenter
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for Biobehavioral Health, Nationwide Children’s Hospital, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data, presented and their discussed interpretation bDepartment
of Pediatrics, Ohio State University, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data presented and their discussed interpretation
cCollege
of Medicine, Ohio State University, Columbus, Ohio
dColumbus
Ohio Adult Congenital Heart Disease Program, The Heart Center, Nationwide Children’s Hospital, Columbus, Ohio; This author takes full responsibility for all aspects of the reliability and freedom from bias for the data presented and their discussed interpretation
eDepartments
of Internal Medicine and Pediatrics, Ohio State University, Columbus, Ohio
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Abstract Background—Identify demographic and medical status indicators that account for variability in physical and emotional health-related quality of life (QoL) among young adults with congenital heart disease (CHD) as compared to traditional lesion severity categories. Methods—Cross-sectional study of 218 young adult survivors of CHD (mean = 25.7, SD = 7.1 years). Participants were recruited from pediatric and adult CHD clinics at a pediatric and an adult hospital. Stepwise linear regression examined the unique contribution of demographic (age; sex; estimated income) and medical status indicators (comorbid conditions; treatment modality; ventricular function/functional capacity) on QoL compared to traditional lesion severity categories (simple; moderate; complex).
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Results—Lesion severity category accounted for a small portion of the variance in physical QoL (3%), but was not associated with emotional QoL. Lesion severity did not significantly contribute to the variability in physical QoL once other variables were entered. Having an estimated income of ≤ $30,000, taking more than one cardiac-related medication, and having a New York Heart
Corresponding Author: Jamie L. Jackson, PhD; 700 Children’s Drive, JWest 4th Floor, Columbus, OH 43205; Phone: 614-722-3585, Fax: 614-722-3544, [email protected]. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Conflict of Interest: None.
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Association (NYHA) functional class designation > I was associated with poorer physical QoL and explained 23% of the variability. NYHA class was the only variable that explained a unique proportion of variance (7%) in emotional QoL, and having a NYHA class designation > I was associated with greater risk for poorer emotional functioning. Conclusions—Findings indicated that several indicators readily available to treatment teams may provide important information about the risk for poor patient-reported outcomes of physical and emotional QoL among CHD survivors. Keywords congenital heart defects; quality of life
1. BACKGROUND Author Manuscript Author Manuscript
More than 90% of infants with congenital heart disease (CHD) now survive into adulthood, resulting in over 1 million adults with CHD living in the United States [1]. Many survivors of CHD encounter a range of physical symptoms, require complex interventions, and undergo repeated hospitalizations as part of managing their condition, placing greater burden on the healthcare system [2,3]. Survivors of CHD are also at risk for significant costly complications as they age, including heart failure, arrhythmias, and stroke [3]. In addition, individuals with CHD may experience emotional distress over time, which could interfere with daily functioning and role performance [4–6]. Therefore, there are many aspects of managing CHD that could negatively impact the physical and emotional domains of survivors’ perceptions of health, also known as health-related quality of life (QoL). QoL, not to be confused with health status, is a multidimensional construct of an individual’s evaluation of their physical and emotional functioning as impacted by perceptions of disease burden [7–9]. Because QoL is an independent predictor of mortality [10,11] and future cardiac events [12,13] among other cardiovascular disease populations, the use of patientreported outcomes in clinical research [14] has become vital and may offer medical providers valuable insight into risks for poor outcomes earlier in treatment.
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Historically, CHD severity has been categorized as “simple,” “moderate” or “complex” to simplify communication between medical providers [15]. While useful in conveying basic information about an individual’s lesion, the utility of this categorization system as a predictor of QoL has been challenged, especially when compared to other measures of functional status [16,17]. Individual differences in response to treatment, as well as variation in the trajectory of the disease over time, result in significant heterogeneity within these categories. In particular, the group designated as “moderate” includes a large spectrum of lesions, some of which may never require repair, while others may result in staged surgical interventions and necessitate management with multiple medications and frequent monitoring. Equivocal findings have been reported on the relationship between traditional lesion severity categories and QoL [16,18–22], and those that have reported an association only identify a relationship with physical QoL [21,22]. Other indicators of medical status have also been examined in relation to QoL, but often in isolation and with varying degrees of methodological rigor. A recent literature review of
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QoL among individuals with CHD showed equivocal findings between those with CHD and the general population, presumably due to differences in QoL assessment tools that may vary in content and psychometric properties [23]. Physical functioning was twice as likely to be negatively impacted (17/22 studies) than psychosocial functioning (7/23 studies). Authors concluded that several medical status indicators were associated with QoL, such as cyanosis, exercise capacity, and ventricular function/functional capacity, but the relationships were inconsistent across studies. Variations in how medical status indicators were measured may have contributed to the equivocal findings. Strikingly, most studies have only examined the relationship between one to three medical status indicators and QoL, leaving in question the amount of variability accounted for by other potentially important indicators. By studying medical indicators in relative isolation, it becomes difficult to evaluate their unique predictive value, that is determine whether the contributions of the medical status variables overlapped in explaining levels of QoL.
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Therefore, the aim of the current study was to compare the traditional disease classifications for CHD (simple, moderate and complex) to a variety of common medical status indicators (demographics, cardiac-related conditions, treatment modalities, and ventricular function/ functional capacity) to determine which indicators(s) of medical status would explain the most unique variability in physical and emotional QoL among adolescents and adults with CHD. The indicators of medical status selected have either been previously examined in relative isolation or not yet included as predictors of QoL in previous studies. Results from this study have important clinical implications for identifying patients at risk for poorer QoL using readily available demographic and medical status indicators, therefore allowing patients earlier access to additional resources and referrals.
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2. METHODS 2.1 Participants and Procedures
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Participants were recruited from outpatient cardiology clinics at both a pediatric and adult Midwestern hospital from May, 2012, to December, 2013. Participants were enrolled in a larger survey study examining disease knowledge, QoL, coping, and health behaviors among adolescent and young adult survivors of CHD. All patients who were eligible and had a scheduled clinic appointment were contacted over the phone for recruitment. Criteria for eligibility included: (1) the presence of a structural congenital heart defect, (2) age 15 to 39 years, and (3) the ability to read/write in English. Patients were excluded if diagnosed with a broader genetic syndrome that had cardiac involvement (e.g., Down, Marfan, etc.) or had cognitive impairments that would impede their ability to complete the measures. Of the 281 individuals approached, 18 declined, resulting in a recruitment rate of 94%. All participants provided informed consent. Only the young adult participants from this sample were included in the current study. Participants completed an online measure of QoL. Medical charts were reviewed at the time QoL was measured to abstract information about demographics, diagnosis, comorbidities, treatment history, and cardiac ventricular function/functional capacity. The study protocol was approved by the local Institutional Review Board and conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Int J Cardiol. Author manuscript; available in PMC 2017 January 01.
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2.2 Quality of Life
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The Medical Outcomes Study SF-36v2 was used to assess the impact of physical (PCS) and emotional (MCS) symptoms on daily living. The SF-36v2 is a self-report measure of healthrelated QoL as perceived by the participant. For each component scale, scores range from 0– 100 with higher scores indicating better QoL in that domain. The SF-36v2 is a commonly used tool across the United States and Europe, including in CHD [24], and has strong psychometric properties [25]. The current study has met recent recommendations set forth by Bratt & Moons (2015) for QoL research [26]. If able, missing items were prorated according to the directions of the measure. As noted in Table 1, 208 participants have QoL scores. Three participants were missing too many items to be eligible for proration and seven participants did not complete the measure due to an error in the online survey system. 2.3 Medical Chart Review
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Lesion severity classification was determined based on diagnosis as outlined by the American College of Cardiology/American Heart Association 2008 guidelines [27]. Demographic variables, such as age and sex, were also abstracted from medical charts. Participants’ estimated family income was derived by the Federal Financial Institutions Examination Council (FFIEC) 2013 Census estimate based on participants’ home address. The FFIEC is a government body that uses loan application information, in conjunction with self-reported federal census data to estimate family income within a particular geographic census tract. Other medical chart variables recorded included indicators of medical status for the following categories: cardiac-related conditions, treatment modalities and ventricular function/functional capacity.
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For cardiac-related conditions, the following variables were determined (yes/no) from participants’ medical chart: diagnosis of an arrhythmia (i.e., a sustained atrial or ventricular arrhythmia that required evaluation), diagnosis of heart failure, history of stroke, established coronary artery disease, history of aortic enlargement (i.e., an aorta > 4 centimeters in circumference), diagnosis of pulmonary arterial hypertension (as determined during catheterization), diagnosis of arterial hypertension, and cyanosis at the time of study enrollment. In addition, the number of conditions was tallied to create a total continuous measure of cardiac-related conditions. Body mass index (BMI) was calculated for each participant, and BMI was compared to age norms for adolescent participants. Participants were categorized as “underweight” (BMI ≤ 18.4), “normal” (BMI = 18.5–24.9), “overweight” (BMI = 25.0–29.9), or “obese” (BMI ≥ 30.0) as outlined by the World Health Organization [28].
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Treatment modality variables collected for the current study included the presence of a pacemaker or implantable cardioverter defibrillator (yes/no), number of cardiac-related hospitalizations in the past 5 years, lifetime number of catheterizations, number of openheart surgeries, and number of cardiac-related medications (not including antibiotic prophylaxis for endocarditis). Measures of ventricular function/functional capacity were also recorded, including the systemic left ventricular ejection fraction (LVEF) class, New York Heart Association
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(NYHA) functional class, and peak V02. Few participants with systemic right ventricles (N = 27) or single ventricle lesions (N = 10) had ejection fraction values available and therefore were not included in the analyses. 2.4 Plan of Analysis
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First, non-directional bivariate correlations were computed to determine the relationship between lesion severity classification, demographics, and indicators of medical status with QoL. Variables that were significantly associated with QoL were then included in two stages of forward stepwise linear regressions to determine which variables explained the most unique variance in each domain of QoL (physical [PCS] and emotional [MCS] functioning). Stepwise regression is optimal for this situation because variables that provide redundant information are removed from the model. For the first stage of the regressions (Stage 1), separate stepwise regression models were conducted regressing either physical or emotional QoL on predictors within 1) demographics, 2) cardiac-related conditions, 3) treatment modalities, and/or 4) ventricular function/functional capacity. Stage 1 analyses served to identify a limited number of predictors within each category of demographic and medical status indicators that accounted for a significant portion of unique variance in physical and emotional QoL. Stage 2 of the stepwise linear regressions consisted of adding all indicators that significantly accounted for unique variance in Stage 1 into one model to determine which variables explained unique variance in QoL across demographic and medical status indicators. If lesion severity was significantly correlated with a domain of QoL, it was also included in the combined Stage 2 regression model. Once the final models were determined for each domain of QoL, categorical variables were further explored posthoc using either ttests or analysis of variance (ANOVA) with posthoc Tukey tests to identify the level of the variable at which QoL was significantly reduced. Variables that were unevenly distributed across levels were collapsed and analyses were re-run. The collapsed variables did not change the outcomes of the analyses; therefore, the original levels of all variables are reported. Analyses were performed by two of the co-authors (JL, LH).
3. RESULTS Participants included 218 young adult (range = 18–39 years) survivors of CHD with a variety of diagnoses representing simple, moderate and severe lesion severities (Table 1). Means, standard deviations, percentages, and ranges for demographics, QoL, and the medical variables can be seen in Table 2. 3.1 Physical QoL
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Correlations and the results of Stages 1 and 2 of the stepwise linear regressions for physical QoL are shown in Table 3. The relationship between categories of lesion severity and physical QoL suggested that more complex disease is associated with poorer physical QoL (r = −0.16, p = 0.023). Further examination of lesion severity across classifications using ANOVA (F[2, 205] = 2.76, p = 0.066) and Tukey posthoc analyses suggested a trend for those with complex lesions to
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report poorer physical QoL (M = 47.23, SD = 8.10) than those with simple (M = 50.89, SD = 9.26) or moderate (M = 49.73, SD = 9.28) lesion types. Correlations indicated that demographic variables, as well as several variables within the domains of cardiac-related conditions, treatment modality and ventricular function/ functional capacity, were significantly negatively associated with physical QoL. Once those variables were entered into stepwise regression models (Stage 1), results suggested that the following demographic and medical status indicators accounted for a significant portion of unique variance in physical QoL: 7% demographics (age, estimated family income), 16% cardiac-related conditions (diagnosis of heart failure), 15% treatment modalities (number of cardiac medications), and 22% ventricular function/functional capacity (NYHA class).
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Lesion severity, demographic variables, and medical status indicators were subsequently combined into a final stepwise regression (Stage 2), revealing that three variables accounted for 23% of the unique variance in physical QoL: estimated family income, taking a greater number of cardiac-related medications, and having a higher NYHA class. Further examination of estimated family income suggested the following breakdown: ≤ $30,000 (6%), $30,001-$50,000 (30%), $50,001-$75,000 (37%), and > $75,000 (27%). Less than $30,000 was chosen for the lowest income cutoff because the U.S. Census Bureau sets the poverty threshold for a family of five people at $28,265. Results of an ANOVA (F[3,202] = 3.03, p = 0.031) with Tukey posthoc tests comparing the four income groups indicated that individuals with estimated family incomes of ≤ $30,000 (M = 43.10, SD = 7.89) reported poorer QoL than those with incomes above $75,000 (M = 51.36, SD = 8.47, 95% CIs [−15.50, −1.02]). A similar examination of cardiac-related medications suggested that 36% of the sample did not take medication, 23% took one, and 41% took two or more medications. An ANOVA (F[2,205] = 6.83, p = 0.001) and Tukey posthoc tests comparing groups who took zero, one, or two or more medications indicated that individuals taking two or more medications reported significantly poorer physical QoL (M = 46.54, SD = 8.99) than those taking no medications (M = 51.46, SD = 8.45, 95% CIs [−8.19, −1.63]) or only one medication (M = 50.39, SD = 8.54, 95% CIs [−7.55, −0.13]). A similar examination of NYHA class revealed that 67% of the sample was given a NYHA class designation of I and 33% had a designation of II or higher. A t-test (t = 7.08, df = 202, p < 0.001, 95% CIs [6.20, 10.98]) confirmed that individuals with a NYHA class of II or greater reported significantly worse physical QoL (M = 43.34, SD = 9.68) than those with a NYHA class of I (M = 51.93, SD = 7.21). 3.2 Emotional QoL
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Several demographic and medical status indicators were significantly associated with emotional QoL (Table 4). Lesion severity category was not correlated with emotional QoL, and therefore was not included in the subsequent analyses. Stage 1 of the stepwise linear regressions identified the following variables as explaining a significant amount of the unique variance for emotional QoL (Table 4): 5% cardiac-related conditions (pulmonary arterial hypertension, number of cardiac-related conditions), 2% treatment modality (number of cardiac medications), and 7% ventricular function/functional capacity (NYHA class).
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For the combined stepwise regression model in Stage 2, only one medical status indicator continued significantly accounting for unique variance in emotional QoL: 7% NYHA class. Results of a t-test (t = 4.22, df = 202, p < 0.001, 95% CIs [3.64, 10.03]) confirmed that individuals with a NYHA class of II or greater report poorer emotional QoL (M = 43.76, SD = 12.95) than those who have a class of I (M = 50.59, SD = 9.61).
4. DISCUSSION
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The current study is the first to use a systematic statistical approach for determining the relative contribution of a large number of readily available demographic and medical status indicator variables in explaining the variability in QoL among adolescents and adults with CHD as compared to traditional lesion severity categories. Several variables significantly outperformed the lesion severity classification system in explaining patient-reported QoL outcomes, suggesting that an approach accounting for an individual’s current medical status, rather than the assigned disease severity category, is necessary for identifying patients at risk for poor QoL. In addition, some of the variance accounted for by the variables was shared, highlighting the importance of examining multiple specific demographic and medical status indicators simultaneously.
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Having an estimated family income of ≤ $30,000, taking two or more cardiac-related medications, and having a NYHA class of II or greater explained 23% of the variance in physical QoL. Among adults with CHD, lower self-reported family income has been associated with greater complaints of somatic symptoms [4] and worse exercise intolerance [29], while a higher income has been identified as a protective factor for loss to follow-up [30]. Conversely, Opić and colleagues (2015) compared adults with CHD to normative data in the Netherlands and found that adults with CHD had lower income as compared to norms, but reported higher QoL [31]. Opić and colleagues did not report the relationship between income and QoL. One possible explanation for why income may be associated with physical QoL is that individuals with more complex disease can experience significant symptoms (e.g., dyspnea, fatigue) that limit daily activities, including the ability to work. However, posthoc analyses suggested that income did not differ between illness severity groups. Eslami and colleagues (2013) speculated that low income may result in less access to care, thereby preventing patients from receiving proper treatments to mitigate symptoms [4]. This hypothesis should be explored further in future prospective studies.
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The number of medications as a contributor of unique variance for physical QoL is likely a reflection of the severity of the cardiac-related conditions. For example, a CHD survivor who has heart failure may be placed on additional medications if symptoms of heart failure worsen or cardiac functioning declines. An alternative interpretation for why the number of medications explains a significant portion of the variance in physical QoL is that individuals taking more medications have more cardiac-related conditions, which was confirmed by posthoc analyses (r = 0.65, p ≤ 0.001), and greater number of cardiac-related conditions was associated with poorer physical QoL (Table 3). Therefore, the number medications taken may be a reflection of the number of cardiac-related conditions significant enough to warrant taking medication. However, the shared variance between the number of
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medications and the number of cardiac-related conditions was accounted for when all of the variables were added to the model simultaneously.
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NYHA class accounted for the most unique variance in physical QoL, and having a NYHA class of II or greater was suggestive of risk for poorer patient-reported QoL. This was not surprising, given that NYHA class was designed as a clinical indicator of limitations in physical functioning related to cardiac function as reported by the patient. However, while NYHA class has been associated with poorer QoL in adults with CHD [15], as well as other cardiovascular disease populations (e.g., pulmonary arterial hypertension [32], coronary artery disease [33]), the amount of variance explained by NYHA class has not been simultaneously tested against a large variety of other potentially important medical status indicators. Posthoc analyses indicated that the presence of a heart failure diagnosis remains as a significant contributor to the variability in physical QoL when NYHA class was not included in the model (β = −0.24, p ≤ 0.001; adjusted R2 = 0.22, p = 0.008). This suggests that in the absence of NYHA class, having a diagnosis of heart failure could also be considered as a risk factor for poorer physical QoL. NYHA class was also the only variable, out of the 25 variables examined, to explain a significant portion of the variance in emotional QoL. This finding suggests that functional limitations due to disease severity are among the most important factors to consider for emotional well-being in this population and age group. Limitations in functional status also have predicted poorer emotional QoL among older individuals with heart failure [34], indicating that being symptomatic, and therefore likely unable to engage in activities that were once enjoyable, may have a particularly deleterious impact on emotional well-being. 4.1 Study Limitations
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The list of potentially important demographic and medical status indicator variables considered for this study was not exhaustive. Although age may partially capture time since diagnosis, this variable was not measured independently and may prove to be important when considering for how long a person has known about their condition. However, an individual diagnosed in adulthood, who was only aware of their condition after recent impairments in functional ability, may have a similar experience as someone diagnosed early in life who has lived relatively symptom-free until adulthood. Furthermore, there are likely other variables accounting for additional variance that were not measured in the current study. The variables selected for these analyses were thought to be most available to medical personnel in a clinic setting and to have greater clinical utility than additional measures that add time to the clinic visit (e.g., depression and anxiety inventories) or may be difficult to acquire (e.g., time on cardiac bypass). Another limitation of this study was the minimal representation of several medical variables, such as the presence of certain cardiacrelated conditions (e.g., stroke, coronary artery disease), presence of an ICD, and LVEF class. Despite the smaller number of participants with some medical variables, the current study had a high rate of recruitment (94%) from both pediatric and adult hospital settings, which is suggestive of a representative sample. Furthermore, posthoc power analysis indicated that the current study was sufficiently powered (1-β = 0.99) to detect small effect sizes (0.19) for multiple regression with α = 0.05 using the lowest available sample size (n =
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208) and the greatest number of variables included in the model (10). One potential limitation to study representativeness was the inclusion of only patients who were engaged in care, in that they were either attending yearly follow-up appointments or had experienced a medical event. Results from the current study may not be generalizable to individuals with CHD who are not engaged in care. 4.2 Conclusions
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Patient-reported QoL is an independent predictor of morbidity and mortality among individuals with various forms of cardiovascular disease [9–13]. Therefore, assessment of QoL, separate from consideration of health status indicators, is critical for optimizing patient-centered care. The current study demonstrated that adults with CHD who have an estimated income ≤ $30,000, take two or more cardiac medications, and have a NYHA class of II or greater, are at increased risk for poor physical QoL. Furthermore, those individuals with a NYHA class of II or greater are also at risk for poorer emotional QoL, which may include increased emotional distress. Poor QoL is suggestive of difficulty performing tasks of daily living, which has important implications for meeting healthcare needs, such as attending clinic appointments and engaging in other self-care activities. Therefore, declines in QoL likely affect morbidity and mortality through multiple avenues and should be closely monitored through screening. In sum, these findings suggest that practitioners may be able to utilize readily available data to better identify individuals at risk for poorer QoL earlier in treatment, resulting in faster referral to appropriate resources, including formal exercise programs, such as cardiac rehabilitation, or psychosocial professionals. Timely referrals of patients for these important services may ultimately reduce hospitalizations and optimize medical and psychosocial outcomes.
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Acknowledgments Grant support: this work was supported by National Institutes of Health grant T32 HL- 098039 (to J.L. Jackson), The Heart Center at Nationwide Children's Hospital (K. Vannatta and C.J. Daniels) and the Clinical and Translational Science Award grant UL1TR001070 at The Ohio State University and Nationwide Children's Hospital.
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Table 1
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Sample composition by diagnosis and surgical history Percentages Lesion Type BAV, COA
29%
ASD, VSD, AVSD, AVC
26%
Tetralogy of Fallot, DORV
21%
D-TGA, L-TGA
15%
Single Ventricle Fontan, PA
8%
Pulmonic Stenosis
8%
Anomalous Pulmonary Venous Return
3%
Ebstein’s Anomaly
3%
Other lesion
6%
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Cardiac Surgeries Valve replacement, valve repair
34%
Patch closure
25%
Shunt, Glenn
19%
Tetralogy of Fallot Repair
17%
Coarctation Repair
9%
Mustard, Senning
9%
Fontan
8%
Arterial switch
2%
Rastelli
2%
Maze
1%
Not available
17%
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ASD = atrial septal defect; AVSD = atrioventricular septal defect; AVC = atrioventricular canal; BAV = bicuspid aortic valve; COA = coarctation of the aorta; DORV = double outlet right ventricle; D-TGA = D-transposition of the great arteries; L-TGA; L-transposition of the great arteries; PA = pulmonary atresia; VSD = ventricular septal defect.
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Table 2
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Means, standard deviations, and ranges for demographics, medical chart variables, and measures of quality of life. Mean (SD) or %
Range
Lesion Severity Simple
25%
Moderate
42%
Complex
33%
Demographics Age
27.7 (6.1)
Sex (female)
18–39
55%
Estimated Family Income ($)
64,461 (28,869)
11,156–167,509
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Comorbid Conditions (yes) Arrhythmia
35%
Heart Failure
26%
HTN
17%
Aortic Enlargement
12%
Cyanosis
6%
Pulmonary Arterial HTN
5%
Stroke
3%
Coronary Disease
2%
Number of Cardiac-Related Conditions
1.1 (1.1)
0–6
BMI Classification
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Underweight
2%
Normal
42%
Overweight
28%
Obese
28%
Treatment Modalities Pacemaker (yes)
17%
ICD (yes)
7%
Cardiac-Related Hospitalizations
0.3 (0.9)
0–9
Catheterizations
1.4 (1.7)
0–10
Open-Heart Surgeries
1.6 (1.3)
0–6
Cardiac-Related Medications
1.6 (1.9)
0–9
Ventricular Function/ Functional Capacity Systemic LVEF Class
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I
91%
II
8%
III/IV
1%
NYHA class I
67%
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Mean (SD) or %
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II
Range
26%
III/IV
7%
Peak VO2
25.7 (43.8)
0–100
Physical Functioning (PCS)
49.2 (9.0)
21–66
Mental Functioning (MCS)
48.2 (11.5)
9–67
Quality of Life †
BMI = body mass index; EF = ejection fraction; HTN = hypertension; ICD = implantable cardioverter defibrillator; LVEF = left ventricle ejection fraction; NYHA = New York Heart Association; RVEF = right ventricle ejection fraction; VO2 = maximal oxygen consumption. † Sample size ranged from 216–218 for all variables with the exception of quality of life (N = 208), total number of catheterizations (N = 208), and systemic LVEF class (N = 161).
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Table 3
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Correlations and stepwise linear regressions demonstrating the strength of the relationships between demographics, medical chart variables, and physical functioning. Physical Functioning (PCS) β (P)
r (P) Stage 1 Lesion Severity Category
Stage 2
−0.16 (0.023)
Demographics Age
−0.18 (0.011)
Sex
−0.14 (0.044)
Estimated Family Income Adj
0.23 (0.001)
R2
−0.15 (0.023)
0.22 (0.002)
0.14 (0.020)
0.07
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Cardiac-Related Conditions Arrhythmia
−0.14 (0.039)
Heart Failure
−0.39 (
