Clinical and Sociodemographic Factors Associated With Health-Related Quality of Life in Patients With Adult Congenital Heart Disease　― A Nationwide Cross-Sectional Multicenter Study ―

Shunsuke Tatebe; Satoshi Yasuda; Ryo Konno; Yasuhiko Sakata; Koichiro Sugimura; Kimio Satoh; Takashi Shiroto; Satoshi Miyata; Osamu Adachi; Masato Kimura; Yoshiko Mizuno; Junko Enomoto; Shigeru Tateno; Hiromichi Nakajima; Kotaro Oyama; Yoshikatsu Saiki; Hiroaki Shimokawa

doi:10.1253/circj.CJ-23-0383

Abstract

Background: Little is known about clinical or sociodemographic factors that influence health-related quality of life (HRQoL) in patients with adult congenital heart disease (ACHD).

Methods and Results: We conducted a nationwide prospective cross-sectional multicenter study at 4 large ACHD centers in Japan. From November 2016 to June 2018, we enrolled 1,223 ACHD patients; 1,025 patients had an HRQoL score. Patients completed a questionnaire survey, including sociodemographic characteristics, and the 36-Item Short-Form Health Survey (SF-36). To determine factors associated with HRQoL, correlations between 2 SF-36 summary scores (i.e., physical component score [PCS] and mental component score [MCS]) and other clinical or sociodemographic variables were examined using linear regression analysis. In multivariable analysis, poorer PCS was significantly associated with 11 variables, including older age, higher New York Heart Association class, previous cerebral infarction, being unemployed, and limited participation in physical education classes and sports clubs. Poorer MCS was associated with congenital heart disease of great complexity, being part of a non-sports club, current smoking, and social drinking. Student status and a higher number of family members were positively correlated with MCS.

Conclusions: This study demonstrates that HRQoL in ACHD patients is associated with various clinical and sociodemographic factors. Further studies are needed to clarify whether some of these factors could be targets for future intervention programs to improve HRQoL outcomes.

The World Health Organization defined health as a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.¹ This indicates that, in addition to morbidity and mortality, health-related quality of life (HRQoL) is important when measuring a patient’s health status. It was previously reported that patient-reported outcomes, including HRQoL, should be considered in cardiovascular clinical research to improve the quality of care for patients.²

Editorial p 71

Along with improvements in the treatment of congenital heart disease (CHD) during childhood, the number of patients with adult CHD (ACHD) continues to increase worldwide.³ The HRQoL of ACHD patients also has gained considerable interest because chronic medical conditions cause psychosocial difficulties. It was previously reported that the physical component of HRQoL was significantly worse in ACHD patients than in the general population, although the mental component of HRQoL was comparable.⁴^,⁵ Clinical and sociodemographic factors associated with HRQoL have been investigated in ACHD patients.⁶^–⁹ However, few studies regarding HRQoL in ACHD patients have used multivariable analysis by which independent predictors of HRQoL can be identified.

A previous study revealed that HRQoL scores in ACHD patients differed among 15 countries.⁶ Among these 15 countries, Japanese patients reported the worst quality of life (QoL).¹⁰ In the present study, we conducted a nationwide cross-sectional multicenter study at 4 ACHD centers in Japan, namely Tohoku University Hospital, Chiba Cardiovascular Center, Iwate Medical University, and Chiba Children’s Hospital, to identify potentially modifiable factors associated with HRQoL in ACHD patients and to generate a hypothesis that may lead to future intervention studies for improving HRQoL in ACHD patients.

Methods

Study Population

From November 2016 to June 2018, we prospectively enrolled 1,223 ACHD patients aged 15 to 87 years at the 4 large ACHD centers in Japan. The number of patients enrolled at each institution is listed in Supplementary Table 1. A self-report questionnaire, including HRQoL, was provided to the patients at outpatient clinics. Of the 1,223 patients, 1,025 were able to report by themselves, with the remaining 198 supported by their family. Thus, the final sample size available for statistical analysis was 1,025 (Supplementary Table 1).

Written informed consent was obtained from each participant. The study was performed in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and was approved by the ethics committees of the Tohoku University Graduate School of Medicine and all participating centers.

Data Collection

HRQoL was measured using the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36).¹¹ The SF-36 is a generic HRQoL assessment tool and has been used most frequently in the past decade in the field of HRQoL research in CHD.¹² Two summary scores, a physical component summary score (PCS) and a mental component summary score (MCS), were derived from 8 components of the SF-36,¹¹ and were the primary outcomes in the present study. Higher scores on the PCS and MCS mean better HRQoL. We also collected sociodemographic data from a self-report questionnaire, including education level, employment status, marital status, participation in physical education classes and school-based extracurricular activities during school days, the number of family members in the household, religious beliefs, smoking, and alcohol consumption. Medical information was obtained through a review of medical records review and included demographics, New York Heart Association (NYHA) functional class, diagnosis of CHD, cardiac repaired status, presence of Fontan circulation and Eisenmenger syndrome, and past medical history, including myocardial infarction, syncope, cerebral infarction, infective endocarditis, cancer, and viral hepatitis. Pulmonary hypertension was defined as mean pulmonary arterial pressure ≥25 mmHg on right heart catheterization¹³ or estimated pulmonary artery systolic pressure ≥40 mmHg on echocardiography. The complexity of CHD was assigned according to the Bethesda classification.¹⁴ These patient characteristics were collected as potential predictors of the primary outcomes.

Statistical Analysis

Continuous variables are expressed as the mean±SD and were assessed by the Student’s t-test or Wilcoxon rank-sum test as appropriate. Categorical variables are expressed as proportions and were analyzed by Fisher’s exact test. Missing data were not imputed for the analysis because less than 3.5% of data were missing for any of the variables. Age–gender weighted average PCS and MCS in ACHD patients were calculated and compared with those in the general Japanese population (n=2,279) reported by Suzukamo et al.¹¹ For calculating these average scores, we excluded those aged <20 or >79 years due to the lack of normative data for these age groups.¹¹ Multiple comparisons were corrected using the Bonferroni method. If the reference value was outside the 95% confidence interval (CI) regarding the ACHD study population, the difference was considered to be statistically significant. Univariable and multivariable linear regression analyses were performed to evaluate the association of PCS and MCS with baseline variables. In the multivariable models, we selected the variables by using a forward stepwise method with variables entered if P<0.05 on univariable linear regression analysis. Because it was previously reported that larger families have positive development effects in childhood and adolescence,¹⁵ but not in midlife,¹⁶ in the general population, the interaction between the number of family members and age for MCS was assessed by the linear regression model with interaction terms, where P value for interaction <0.10 was considered to be statistically significant.

All statistical analyses were performed using JMP^® Pro 14.3 (SAS Institute Inc., Cary, NC, USA) and R version 3.5.1 (The R Project for Statistical Computing; https://www.r-project.org/). Results were considered to be statistically significant at two-tailed P<0.05.

Results

The most common diagnosis of CHD was ventricular septal defect (22%), followed by tetralogy of Fallot (15%) and atrial septal defect (11%; Supplementary Table 2). The baseline characteristics of the study population are presented in Table 1. The prevalence of patients with complex CHD was 60%. Pulmonary hypertension was diagnosed by right heart catheterization in 36 (3.6%) patients and by echocardiography in 56 (5.6%). The prevalence of a past history of myocardial infarction, syncope, and cerebral infarction was 1.4%, 3.2%, and 2.4%, respectively. Approximately 60% of patients had a job and were not married. More than half the patients (51%) had an education level greater than high school. With regard to physical education classes and school-based extracurricular activities during school days, 580 (58%) patients mostly participated in physical education classes and 334 (34%) belonged to a sports club. The mean number of family members was 3.4. There were 88 (8.7%) current smokers in the present study.

Table 1.

Baseline Characteristics of All 1,025 Japanese Adult CHD Patients

Age (years)	33.8±14.9
Age group
<35 years	635/1,025 (62)
≥35 years	390/1,025 (38)
Male sex	468/1,025 (46)
CHD complexity^A
Simple	400/1,005 (40)
Moderate	342/1,005 (34)
Great	263/1,005 (26)
NYHA functional class
I	684/1,018 (67)
II	309/1,018 (30)
III	24/1,018 (2.4)
IV	1/1,018 (0.1)
After intervention for CHD^B	810/1,025 (79)
Pulmonary hypertension
Diagnosed by RHC	36/1,001 (3.6)
Diagnosed by echocardiography	56/1,002 (5.6)
Fontan circulation	77/1,025 (7.5)
Eisenmenger syndrome	11/1,025 (1.1)
Past medical history
Hypertension	68/1,021 (6.7)
Dyslipidemia	32/1,021 (3.1)
Diabetes	24/1,021 (2.4)
Hyperuricemia	16/1,021 (1.6)
Myocardial infarction	14/1,021 (1.4)
Syncope	33/1,021 (3.2)
Cerebral infarction	24/1,021 (2.4)
Infective endocarditis	23/1,020 (2.3)
Cancer	26/1,021 (2.5)
Viral hepatitis	21/1,021 (2.1)
Employment status
Part-time or full-time work	615/1,020 (60)
Unemployed	120/1,020 (12)
Full-time student	182/1,020 (18)
Retired or homemaker	103/1,020 (10)
Education level
University degree	290/1,006 (29)
Junior college or vocational school	217/1,006 (22)
High school	432/1,006 (43)
Less than high school	67/1,006 (6.7)
Participation in PE classes
Rare	183/1,000 (18)
Occasional	237/1,000 (24)
Frequent	580/1,000 (58)
Participation in ECAs
No club	178/989 (18)
Non-sports club	477/989 (48)
Sports club	334/989 (34)
No. family members	3.4±1.6
Living alone	121/993 (12)
Marital status
Never married	589/1,012 (58)
Married or living with a partner	385/1,012 (38)
Divorced or widowed	38/1,012 (3.8)
Religious	63/1,003 (6.3)
Smoking status
Never smoker	761/1,011 (75)
Past smoker	162/1,011 (16)
Current smoker	88/1,011 (8.7)
Alcohol consumption
Non-drinker	449/1,016 (44)
Social drinker	449/1,016 (44)
Regular drinker	118/1,016 (12)

Continuous variables are presented as the mean±SD. Categorical variables are presented as n/N (%), where N is the number of patients for whom data are available. ^ACongenital heart disease (CHD) complexity was assigned according to the Bethesda classification.¹⁴ ^BNumber of patients who underwent surgical repair or a palliative surgical procedure for CHD. ECAs, extracurricular activities; NYHA, New York Heart Association; PE, physical education; RHC, right heart catheterization.

We compared age-gender weighted average PCS and MCS between ACHD patients and the general Japanese population (Figure). In the group aged 20–79 years, PCS was significantly lower in the ACHD group than in the general population (48.8±13.0 vs. 50.0±9.8, respectively), whereas MCS was comparable between the 2 groups (49.9±10.3 vs. 50.0±9.8, respectively). Broken down by age group, PCS was significantly lower in the ACHD patients aged 20–29, 30–39, and 40–49 years (Figure A). MCS for those aged 20–29 years tended to be higher for the ACHD patients (95% CI with Bonferroni correction: 49.9–52.8) than in the general population (95% CI with Bonferroni correction: 46.6–50.2; Figure B).

Figure.

Mean (A) physical component summary (PCS) and (B) mental component summary (MCS) scored on the 36-Item Short-Form Health Survey among Japanese adult congenital heart disease (ACHD) patients and the general Japanese population by age group. Error bars indicate 95% confidence intervals after applying Bonferroni correction to account for multiple comparisons.

Univariable linear regression analysis showed that all the factors, except for status after intervention for CHD, hyperuricemia, the number of family members, and religious beliefs, were significantly associated with PCS (Table 2). In contrast, 13 variables were significantly associated with MCS, including age, complexity of CHD, history of syncope and hepatitis, student status, high school degree, being part of a non-sports club, the number of family members, smoking status, and alcohol consumption (Table 3). PCS was affected by more factors than MCS. Factors associated with both summary scores were age, complexity of CHD, history of syncope and hepatitis, high school degree, being part of a non-sports club, smoking status, and alcohol consumption.

Table 2.

Univariable and Multivariable Linear Regression Analysis of Factors Affecting the Physical Component Summary Score of the 36-Item Short-Form Health Survey

	Univariable (n=1,025)		Multivariable (n=897)
	Coefficient (SE)	P value	Coefficient (SE)	P value
Age	−0.23 (0.03)	<0.001	−0.18 (0.03)	<0.001
Male sex	3.40 (0.80)	<0.001
CHD complexity^A
Simple	Ref.
Moderate	−2.07 (0.93)	0.026
Great	−4.89 (1.00)	<0.001
NYHA functional class
I	Ref.		Ref.
II	−6.10 (0.84)	<0.001	−2.85 (0.81)	<0.001
>II	−20.36 (2.50)	<0.001	−7.62 (2.64)	0.004
After intervention for CHD^B	−0.75 (0.99)	0.447
Pulmonary hypertension
Diagnosed by RHC	−11.70 (2.13)	<0.001
Diagnosed by echocardiography	−11.69 (1.72)	<0.001	−3.44 (1.77)	0.052
Fontan circulation	−4.40 (1.52)	0.004	−4.26 (1.35)	0.002
Eisenmenger syndrome	−20.08 (3.86)	<0.001	−9.17 (3.69)	0.013
Past medical history
Hypertension	−4.68 (1.61)	0.004
Dyslipidemia	−6.67 (2.65)	0.012
Diabetes	−7.54 (2.30)	0.001
Hyperuricemia	−2.47 (3.24)	0.446
Myocardial infarction	−15.28 (3.43)	<0.001	−5.03 (3.22)	0.118
Syncope	−10.30 (2.25)	<0.001	−5.22 (2.05)	0.011
Cerebral infarction	−19.55 (2.59)	<0.001	−15.74 (2.29)	<0.001
Infective endocarditis	−6.04 (2.71)	0.026
Cancer	−6.22 (2.55)	0.015
Viral hepatitis	−7.24 (2.83)	0.011	−3.53 (2.47)	0.152
Employment status
Part-time or full-time work	Ref.		Ref.
Unemployed	−14.25 (1.19)	<0.001	−7.90 (1.17)	<0.001
Full-time student	1.31 (1.00)	0.191	−1.83 (1.02)	0.073
Retired or homemaker	−7.03 (1.27)	<0.001	−1.89 (1.29)	0.144
Education level
University degree	Ref.		Ref.
Junior college or vocational school	−1.98 (1.13)	0.080	0.02 (0.98)	0.980
High school	−4.89 (0.95)	<0.001	−3.82 (0.83)	<0.001
Less than high school	−7.87 (1.70)	<0.001	−2.05 (1.54)	0.183
Participation in PE classes
Rare	Ref.		Ref.
Occasional	5.00 (1.18)	<0.001	1.29 (1.09)	0.240
Frequent	11.23 (1.01)	<0.001	4.27 (1.05)	<0.001
Participation in ECAs
No club	Ref.		Ref.
Non-sports club	3.84 (1.09)	<0.001	1.47 (0.97)	0.131
Sports club	8.84 (1.15)	<0.001	3.28 (1.06)	0.002
No. family members	0.21 (0.26)	0.430
Living alone	2.94 (1.24)	0.018
Marital status
Never married	Ref.
Married or living with partner	−0.59 (0.84)	0.483
Divorced or widowed	−4.91 (2.15)	0.023
Religious	1.47 (1.67)	0.379
Smoking
Never smoker	Ref.
Past smoker	−0.60 (1.11)	0.587
Current smoker	4.00 (1.44)	0.006
Alcohol consumption
Non-drinker	Ref.
Social drinker	4.07 (0.84)	<0.001
Regular drinker	5.78 (1.31)	<0.001

Association of the physical component summary with clinical or sociodemographic variables (model P<0.001 in multivariable analysis). ^ACHD complexity was assigned according to the Bethesda classification.¹⁴ ^BNumber of patients who underwent surgical repair or a palliative surgical procedure for CHD. Abbreviations as in Table 1.

Table 3.

Univariable and Multivariable Linear Regression Analysis of Factors Affecting the Mental Component Summary Score of the 36-Item Short-Form Health Survey

	Univariable (n=1,025)		Multivariable (n=935)
	Coefficient (SE)	P value	Coefficient (SE)	P value
Age	−0.06 (0.02)	0.004
Male sex	−0.13 (0.65)	0.843
CHD complexity^A
Simple	Ref.		Ref.
Moderate	−1.81 (0.76)	0.018	−1.18 (0.78)	0.130
Great	−1.69 (0.82)	0.041	−1.82 (0.86)	0.036
NYHA functional class
I	Ref.
II	−1.35 (0.71)	0.059
>II	0.97 (2.12)	0.648
After intervention for CHD^B	−0.40 (0.80)	0.616
Pulmonary hypertension
Diagnosed by RHC	−0.81 (1.77)	0.646
Diagnosed by echocardiography	−0.56 (1.43)	0.694
Fontan circulation	−0.61 (1.23)	0.620
Eisenmenger syndrome	−3.39 (3.15)	0.283
Past medical history
Hypertension	−1.69 (1.30)	0.197
Dyslipidemia	2.29 (2.15)	0.286
Diabetes	1.10 (1.87)	0.557
Hyperuricemia	−1.37 (2.62)	0.602
Myocardial infarction	−3.40 (2.80)	0.224
Syncope	−3.87 (1.84)	0.036
Cerebral infarction	0.84 (2.15)	0.695
Infective endocarditis	2.68 (2.19)	0.222
Cancer	−1.66 (2.07)	0.422
Viral hepatitis	−4.56 (2.29)	0.046	−3.86 (2.31)	0.094
Employment status
Part-time or full-time work	Ref.		Ref.
Unemployed	−0.06 (1.02)	0.951	−1.04 (1.09)	0.338
Full-time student	5.96 (0.86)	<0.001	4.05 (0.96)	<0.001
Retired or homemaker	1.82 (1.08)	0.094	0.54 (1.16)	0.639
Education level
University degree	Ref.		Ref.
Junior college or vocational school	−0.65 (0.93)	0.483	−0.65 (0.93)	0.483
High school	1.84 (0.78)	0.019	1.84 (0.78)	0.019
Less than high school	−0.92 (1.40)	0.512	−0.92 (1.40)	0.512
Participation in PE classes
Rare	Ref.		Ref.
Occasional	−0.90 (1.02)	0.379	−0.90 (1.02)	0.379
Frequent	1.04 (0.88)	0.236	1.04 (0.88)	0.236
Participation in ECAs
No club	Ref.		Ref.
Non-sports club	−1.81 (0.91)	0.046	−2.14 (0.93)	0.022
Sports club	0.25 (0.96)	0.799	−0.26 (0.99)	0.796
No. family members	0.73 (0.21)	0.001	0.49 (0.22)	0.024
Living alone	−1.16 (1.01)	0.251
Marital status
Never married	Ref.
Married or living with partner	−1.24 (0.68)	0.069
Divorced or widowed	−1.67 (1.74)	0.337
Religious	1.13 (1.36)	0.406
Smoking
Never smoker	Ref.		Ref.
Past smoker	−1.99 (0.89)	0.026	−1.03 (0.97)	0.289
Current smoker	−4.38 (1.16)	<0.001	−3.74 (1.23)	0.002
Alcohol consumption
Non-drinker	Ref.		Ref.
Social drinker	−2.97 (0.69)	<0.001	−1.67 (0.77)	0.030
Regular drinker	−2.98 (1.06)	0.005	−1.53 (1.19)	0.199

Association of the mental component summary with clinical or sociodemographic variables (model P<0.001 in multivariable analysis). ^ACHD complexity was assigned according to the Bethesda classification.¹⁴ ^BNumber of patients who underwent surgical repair or a palliative surgical procedure for CHD. Abbreviations as in Table 1.

In multivariable linear regression analysis, 11 variables remained significant predictors for PCS (Table 2). Notably, both participation in physical education classes and a sports club during school days were independently associated with better PCS, even after adjusting for clinical and sociodemographic factors, such as NYHA functional class, pulmonary hypertension, past medical history, employment status, and education level. Conversely, MCS was significantly associated with 7 variables on multivariable analysis (Table 3). Student status and a higher number of family members were positively correlated with MCS. There was a significant interaction between age and the number of family members on MCS (P for interaction=0.014). When patients were divided into 2 groups by the mean age (35 years) of the overall population, the effect of the number of family members on MCS was significantly different between the 2 groups (P for interaction=0.068; Supplementary Figure). A greater complexity of CHD, being part of a non-sports club, being a current smoker, and being a social drinker were associated with poor MCS (Table 3).

Discussion

We conducted a large-scale prospective cross-sectional study of HRQoL in ACHD patients. By using a questionnaire survey, including the SF-36, we demonstrated that PCS was significantly worse in ACHD patients than in the general population in most age groups, whereas MCS was comparable between the 2 groups. We also characterized factors influencing HRQoL in ACHD patients and, more importantly, identified some modifiable factors, such as less exercise during school days and smoking.

Strengths of the Present Study

One of the strengths of this study was that we enrolled a large number of AHCD patients (n=1,025). This enabled us to compare the results of HRQoL in ACHD patients with those in the general population, including the elderly group. Furthermore, medical information was obtained from medical records in addition to self-reported patient information. Thus, we were able to identify independent clinical or sociodemographic contributors related to HRQoL among a wide range of variables in ACHD patients by using multivariable analysis. Although the exact reason why Japanese patients reported the worst QoL among the 15 countries remains unknown,⁶^,¹⁰ our study enrolled patients only from Japanese centers and excluded the possibility of intercountry variation.

Comparison of HRQoL Between ACHD Patients and the General Population

In the present study, PCS was worse in ACHD patients aged 20–79 years compared with the general population, although MCS was comparable. These results are similar to the previous report by Enomoto et al.⁵ The authors of that study reported that physical functioning was worse in ACHD patients aged 18–39 years compared with the controls, although MCS was comparable between the 2 groups. We confirmed their results in a large number of ACHD patients across a wide range of age groups. Interestingly, PCS was similar between the 2 groups for those aged 50–59, 60–69, and 70–79 years. This result may contribute to the reduced influence of CHD on health status in ACHD patients who can survive into old age. Unexpectedly, ACHD patients aged 20–29 years tended to have a better MCS than the general population of the same age. Multivariable analysis showed that both being a full-time student and a higher number of family members were associated with a better MCS. In the present study, all patients with a student status were aged 20–29 years, and the number of family members was significantly associated with MCS in young ACHD patients. In addition, the proportion of young ACHD patients living with their parents was higher than in the general population.¹⁷ These 2 factors (i.e., better family support and student status) may contribute to relatively less stressful situations for ACHD patients in this age category, resulting in a better mental component of the HRQoL. However, the high proportion of patients living with their parents indicates that ACHD patients establish their independence later in life than the general population.

Past Medical Conditions Associated With HRQoL

In univariable analysis, PCS and MCS were significantly associated with 32 and 13 variables, respectively. As in the present study, previous studies reported that PCS was affected by more factors than MCS.⁶^–⁹ PCS was associated with more clinical factors than MCS (e.g., NYHA functional class, Fontan circulation, Eisenmenger syndrome, history of stroke). Comorbidities, such as heart failure and post-stroke sequelae that result in limitations in physical function, may have contributed to this finding.

Some of the clinical and sociodemographic factors, such as age, NYHA class, cyanosis, being unemployed, and education level, were identified as determinant factors of HRQoL in the present study, as in previous reports.⁶^–⁸^,¹⁸^,¹⁹ However, little is known about the association of the physical component of HRQoL with a past medical history in ACHD patients. We found that previous cerebral infarction and syncope were significantly associated with PCS.

Factors Associated With the Physical Component of HRQoL

In the present study, both participation in physical education classes and being a member of a sports club were significantly associated with a better PCS, even after adjusting for age, comorbid conditions, and NYHA class. It was previously reported that sports participation was associated with better HRQoL in adolescents and young adults with CHD.²⁰ Aerobic exercise improved HRQoL in patients with CHD aged 10–15 years.²¹ Furthermore, nearly one-third of parents tended to excessively limit the physical activity of their children with CHD.²² This indicates that in order to make the physical component of HRQoL better in ACHD patients, healthcare providers should assess the risk of physical activity and provide exercise advice to children, their parents, and their schools about the appropriate intensity of sports.²³^,²⁴ The present results are consistent with this evidence and further demonstrate that reduced exercise habits in childhood and adolescence are associated with long-term HRQoL in ACHD patients. This association may derive from not only increased exercise capacity and muscle strength, but also other benefits of sports, such as self-efficacy and confidence in physical activity.²⁵ We also consider that exercise habits during school days is one of the important targets for future intervention programs.

Factors Associated With the Mental Component of HRQoL

Student status was associated with a better mental component of HRQoL in multivariable analysis. It was previously reported that psychological distress was related to a poor mental component of HRQoL in ACHD patients.⁹ As CHD patients grow from childhood to adulthood, they need to become independent adults and take responsibility for their own lives. Non-student adults are likely to make choices by themselves about their treatment, work, and life more often than students. ACHD patients may not become aware of various stresses until they are employed. This may cause psychological distress, which results in a worse mental component of HRQoL in ACHD patients. Thus, healthcare providers and patients’ parents should pay attention to the decline in the mental component of HRQoL in CHD patients after graduation, including after employment, and support the transition to adulthood.

Family support was one of the factors influencing the mental component of HRQoL in ACHD patients.⁹ In the general population, a larger family size was correlated with better mental health in children²⁶ and employed heads of households in Japan.²⁷ In contrast, family size was not related to mental distress in the general population in Colombia.²⁸ In a birth cohort in Scotland, family size was associated with psychiatric admissions but not with suicide.²⁹ Thus, the effect of family size on mental health seems to be influenced by environmental, cultural, and social factors. In the present study, we demonstrated a positive correlation between the number of family members and MCS in young ACHD patients. More detailed studies are needed that focus on the relationship between the mental component of HRQoL in ACHD patients and their families.

Regarding lifestyle factors, current smokers had a worse mental component of HRQoL than never or past smokers in the general population.³⁰ We observed the same result in our study. The mental component of HRQoL may also be improved by quitting smoking in this population, as reported in the general population.³¹

Participating in a non-sports club was significantly associated with a worse mental component of HRQoL in multivariable analysis. This may reflect introversion or restriction of participation in sports activities in patients who belonged to a non-sports club during school days. Pearsonality traits were also reported to be a significant predictor of HRQoL in ACHD patients.⁹

It was previously reported that poor HRQoL and QoL were associated with depression and anxiety in adolescents and adults with CHD.³²^–³⁴ Introversion and smoking were associated with anxiety and depression, respectively, in previous reports.³⁵^,³⁶ The effects of factors associated with MCS may be mediated by psychiatric disorders, such as anxiety and depression. Further studies are needed to clarify this point.

Study Limitations

Several limitations of this study should be mentioned. First, because this is a cross-sectional study, causality cannot be confirmed. There may be different interpretations of the correlation results. For example, smoking may be the result of mental stress. It remains possible that CHD patients with higher physical HRQoL were more likely to have participated in sports clubs. Second, although this is a multicenter study with a large number of ACHD patients, all participants were enrolled only from Japanese institutions. Our results may have limited generalizability. However, some of the factors associated with HRQoL in ACHD in the present study were consistent with previous overseas studies.⁶^–⁹ Third, the possibility of selection bias cannot be ruled out because we enrolled only patients who were managed at centers that specialize in ACHD and could complete the SF-36 by themselves. Fourth, there were missing data in the questionnaire because of omission or reluctance to answer personal questions. However, the proportion of missing data was small. The variable with the highest percentage of missing data was participation in school-based extracurricular activities during school days (3.5%), followed by the number of family members (3.1%). Fifth, factors influencing the physical component of HRQoL are complex and influenced by clinical practice as well as the sociodemographic environment. It is conceivable that unmeasured or unknown factors are involved in the study outcomes. Finally, the hemodynamic definition of pulmonary hypertension was modified in the 2022 European Society of Cardiology guidelines.³⁷ Thus, the results of this study can be interpreted according to the previous criteria.

Conclusions

The physical component of HRQoL was significantly worse in ACHD patients than in the general population, although the mental component of HRQoL was comparable. Participation in physical education classes and a sports club during school days remained significant predictors of a better physical component of HRQoL, even after adjusting for clinical and sociodemographic factors. In contrast, student status, the number of family members, and smoking were significantly associated with a poor mental component of HRQoL. Some of these factors could be targets for future intervention programs to improve HRQoL outcomes in this population.

Acknowledgments

The authors thank the staff of Tohoku University Hospital, Chiba Cardiovascular Center, Chiba Children’s Hospital, and Iwate Medical University for their valuable contributions.

Sources of Funding

This research was supported, in part, by the Practical Research Project for Life-Style-Related Diseases, Including Cardiovascular Diseases and Diabetes Mellitus from the Japan Agency for Medical Research and Development (AMED; JP16ek0210068).

Disclosures

S.Y., Y. Sakata, K. Satoh, Y. Saiki, and H.S. are members of Circulation Journal’s Editorial Team. The remaining authors have no conflicts of interest to disclose.

IRB Information

This study was approved by the ethics committees of the Tohoku University Graduate School of Medicine (No. 2019-1-492), Iwate Medical University (No. H28-125), Chiba Cardiovascular Center (No. 610), and Chiba Children’s Hospital (No. 2016-10-31).

Data Availability

The deidentified participant data will not be shared.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-23-0383

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