Assessing the diversity and determinants of health‐related quality of life measures in patients with acute heart failure

Abstract

Aims

Heart failure with reduced ejection fraction (HFrEF) significantly impacts health‐related quality of life (HR‐QoL). Existing HR‐QoL questionnaires can show inconsistencies, potentially misrepresenting patient self‐reports. This study examines the variation in HR‐QoL measurement tools for HFrEF patients, identifying related determinants.

Methods and results

We retrospectively analysed 134 hospitalized patients with acute decompensated HFrEF at a Taiwanese tertiary centre's Heart Failure Post‐Acute‐Care (HF‐PAC) programme. Participants completed the EuroQol‐5 dimension (EQ‐5D) questionnaire, the EQ‐5D visual analogue scale (VAS), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Utility values were obtained from the EQ‐5D questionnaire. Demographic features were depicted using descriptive statistics, while multivariate regression was used to ascertain relationships between HR‐QoL measurements and determinants. Average scores for EQ‐5D, MLHFQ, EQ‐5D utility, and VAS were 6.1 ± 1.6, 21.8 ± 21.3, 81.7 ± 27.0, and 59.5 ± 14.6, respectively. Significant correlations were observed among the three tools. The New York Heart Association functional class showed a notable association with all tool scores. Other associations encompassed EQ‐5D with coronary artery disease, mineralocorticoid receptor antagonists, and the 6 min walk test; EQ‐5D VAS with chronic kidney disease; and MLHFQ with age.

Conclusions

This study illuminates the variance in HR‐QoL measurement tools for Taiwanese HFrEF patients. Using a range of these tools is beneficial in unveiling diverse determinants and approaching comprehensive patient‐centred care. However, for a more precise HR‐QoL assessment in Taiwanese HFrEF patients, recalibrating the EQ‐5D‐derived utility scores might be necessary, emphasizing the importance of patient‐specific considerations within the HF‐PAC programme.

Introduction

Heart failure (HF) is characterized by the inability of the heart to pump enough blood to fulfil the body's needs.¹ Its prevalence is increasing globally, particularly in aging populations.² These symptoms are burdensome and add complexity to treatment strategies for HF patients.³ Among its subtypes, HF with reduced ejection fraction (HFrEF), distinguished by a significantly reduced left ventricular ejection fraction (LVEF), poses unique challenges in patient management and impacts health-related quality of life (HR-QoL) differently compared with other HF forms.⁴

HR-QoL is a critical aspect of managing HFrEF patients, as its thorough assessment enables healthcare providers to tailor treatment plans to align with each patient's unique needs and goals.⁵ For instance, a patient experiencing significant dyspnoea may require a different therapeutic approach compared with one primarily struggling with emotional distress. Furthermore, appropriate assessment tools can aid in understanding the factors affecting a patient's HR-QoL, such as depression, anxiety, or social isolation, and can help improve their overall well-being, potentially reducing the disease's progression.^6,7

In HF management, both disease-specific and generic HR-QoL questionnaires are essential. The Minnesota Living with Heart Failure Questionnaire (MLHFQ) offers detailed insights into HF's impact on daily life by focusing on specific symptoms and concerns.⁸ Conversely, generic tools like the EuroQol-5 dimension (EQ-5D) assess broader health dimensions, including physical and mental well-being.⁹ The EQ-5D visual analogue scale (VAS) is a patient-driven assessment tool providing a self-rated health status, complementing the structured approach of EQ-5D and the disease-specific focus of MLHFQ.⁹ The combined use of these questionnaires can yield a comprehensive view of a patient's HR-QoL, bridging the gap between specific disease impacts and overall health, thereby enhancing patient-centred care.

While HR-QoL questionnaires are commonly used, discrepancies often arise between the results they yield and the patient's self-reported HR-QoL (PSRQoL) in HF patients.¹⁰ This divergence stems from the subjective nature of PSRQoL, contrasting with the standardized format of HR-QoL questionnaires. Factors contributing to this mismatch include variance in patient expectations, potential misunderstandings of questionnaire items, and social desirability bias affecting honest responses.^11,12

To investigate the diversity of HR-QoL measurement tools and the gap between these tools and PSRQoL in HF patients, with a particular emphasis on those with HFrEF, this study aimed to assess the variability in HR-QoL measures among this specific group and explore the determinants of HR-QoL within a Taiwanese context. This focus on HFrEF patients seeks to offer insightful contributions for a more nuanced and patient-centred care approach in this subset of the HF population.

Methods

This retrospective study was reviewed and approved by the Institutional Review Board of Chi Mei Medical Center (10811-001). The study adhered to the guidelines for reporting observational studies and was reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement.

Study design and subject selection

We identified the patients from the National Heart Failure Post-Acute-Care (HF-PAC) programme at a tertiary care centre in southern Taiwan from October 2018 to March 2021. This programme provides a multidisciplinary, patient-centred care model with guideline-directed medical therapies and enrols hospitalized patients above 18 years with acute decompensated HFrEF (i.e. LVEF ≤ 40%), as diagnosed by certified cardiologists and the American College of Cardiology/American Heart Association Heart Failure Stage C or D.¹³ Patients who did not complete HR-QoL questionnaires and had functional or cognitive impairments, dependence on mechanical respiratory support, or end-stage renal disease were excluded from the programme.

Data collection and variables

Baseline demographics, cardiovascular medication data, HF stage, and imaging reports were obtained from electronic health records and face-to-face interviews conducted by case managers. Physical activity was gauged via the 6 min walk test (6MWT) (Supporting Information, Methods S1), with distances categorized into four grades.¹⁴ If patients had difficulty understanding the questionnaire, case managers provided oral explanations in person for better understanding when completing the assessment.

We considered demographic factors; HF-related comorbidities, that is, hypertension, coronary artery disease (CAD), diabetes, and chronic kidney disease (CKD); guideline-directed medical therapies, that is, angiotensin receptor-neprilysin inhibitors (ARNIs) or angiotensin-converting enzyme inhibitors (ACE-Is)/angiotensin II receptor blockers (ARBs), beta-blockers, mineralocorticoid receptor antagonists (MRAs), and sodium-glucose cotransporter 2 inhibitors (SGLT2is); and functional capacity indicators [New York Heart Association functional class (NYHA Fc), LVEF, and 6MWT].¹⁵

Measurement of quality of life

This study is retrospective, utilizing data from patients identified through the HF-PAC programme. The programme employs EQ-5D, MLHFQ for HR-QoL questionnaires, and EQ-5D VAS for PSRQoL as standard tools to evaluate the QoL of HFrEF patients. Therefore, our utilization of these specific instruments was primarily driven by their established use within the national programme, allowing for a consistent and systematic assessment. EQ-5D scores were converted to a utility value, reflecting the quality and utility of life.^16,17 MLHFQ scores provided insights into the physical, emotional, and social dimensions of HR-QoL in HF patients.¹⁸ For ease of comparison and better understanding, utility scores were multiplied by 100 to align with the scale of EQ-5D VAS scores (Supporting Information, Methods S1).

Statistics

In this study, patients' demographic and health-related information was reported using means and standard deviations (SDs), or medians and interquartile ranges, and categorical variables as percentages. HR-QoL measures such as EQ-5D, MLHFQ, and EQ-5D VAS and the utility scores were presented as means and SDs in each dimension. Pearson's correlations were used to assess the correlation between the HR-QoL questionnaires. Multivariate regression analyses were performed to examine the determinants of the varied measurement tools. To ensure the robustness of the regression analysis, two sensitivity analyses were conducted, including the exclusion of outliers, stepwise regression, and forward selection methods. The results were consistent across both sensitivity analyses. Besides, we also performed subgroup analyses to examine the association between the determinants and the individual dimensions of the tools. All statistical analyses were performed using the Statistical Package for Social Sciences (SPSS) Version 22.0 (IBM Corporation, Armonk, NY, USA). Statistical significance was considered when P < 0.05 was two-tailed.

Results

Figure 1 shows that a total of 134 patients were included in this study for final analysis. Among them, 70.1% (n = 94) were male, and 38.1% (n = 51) were current smokers. The mean age of the patients was 62.2 ± 13.6 years, with a mean LVEF of 29.2 ± 7.0%. Most patients presented with major comorbidities, including hypertension (61.9%), CAD (45.5%), diabetes (43.3%), CKD (27.6%), hyperlipidaemia (23.1%), and atrial fibrillation (20.1%). The highest prescription percentage of guideline-directed medical therapy was ARNIs or ACE-Is/ARBs (85.8%), followed by MRAs (79.9%), and beta-blockers (75.4%). The lowest was SGLT2is (20.1%). Other medications, including diuretics and statins, were used by over half of the patients (Table 1 and Supporting Information, Table S1).

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Table 1 Basic characteristics of the patients

	Total (N = 134)
Age, mean ± SD	6.2 ± 13.6 (median: 65.0)
Age ≥ 65 years old, n (%)	68 (50.7)
Gender (male), n (%)	94 (70.1)
Current smoker, n (%)	51 (38.1)
BMI (kg/m2), mean ± SD	25.2 ± 5.2
Underweight (<18.5), n (%)	11 (8.2)
Normal weight (18.5 ≤ BMI < 24), n (%)	48 (35.8)
Overweight (24 ≤ BMI < 27), n (%)	31 (23.1)
Obese (BMI ≥ 27), n (%)	44 (32.8)
Systolic blood pressure (mmHg), mean ± SD	127.4 ± 23.2
Diastolic blood pressure (mmHg), mean ± SD	80.8 ± 17.4
Heart rate (b.p.m.), mean ± SD	86.2 ± 16.7
Pulse pressure (mmHg), mean ± SD	46.6 ± 16.5
<35, n (%)	35 (26.1)
35–45, n (%)	30 (22.4)
46–55, n (%)	25 (18.7)
>55, n (%)	44 (32.8)
NYHA Fc
II, n (%)	82 (61.2)
III, n (%)	50 (37.3)
IV, n (%)	2 (1.5)
LVEF (%), mean ± SD	29.2 ± 7.0
LVEF ≤ 40%, n (%)	134 (100.0)
Comorbidities
Hypertension, n (%)	83 (61.9)
CAD, n (%)	61 (45.5)
DM, n (%)	58 (43.3)
CKD, n (%)	37 (27.6)
Hyperlipidaemia, n (%)	31 (23.1)
Atrial fibrillation, n (%)	27 (20.1)
COPD, n (%)	12 (9.0)
Cancer with chemotherapy, n (%)	12 (9.0)
VHD, n (%)	6 (4.5)
DCM, n (%)	5 (3.7)
LBBB/RBBB, n (%)	2 (1.5)
Cardiovascular medication
ARNIs, n (%)	67 (50.0)
ACE-Is/ARBs, n (%)	48 (35.8)
Beta-blockers, n (%)	101 (75.4)
MRAs, n (%)	107 (79.9)
SGLT2is, n (%)	27 (20.1)
Diuretics, n (%)	76 (56.7)
Ivabradine, n (%)	30 (22.4)
Hydralazine, n (%)	13 (9.7)
Nitrate, n (%)	14 (10.4)
Digoxin, n (%)	22 (16.4)
Anticoagulation, n (%)	85 (63.4)
Statin, n (%)	77 (57.5)
6 min walk test (m), mean ± SD	217.5 ± 104.6
Grade I (<300), n (%)	106 (79.1)
Grade II (300–374.9), n (%)	17 (12.7)
Grade III (375–449.5), n (%)	10 (7.5)
Grade IV (≥450), n (%)	1 (0.7)

As per the HR-QoL measurement tools, Table 2 demonstrates that the total scores of the EQ-5D were 6.1 ± 1.6, and those for the MLHFQ were 21.8 ± 21.3. The utility scores derived from EQ-5D were 81.7 ± 27.0, while the PSRQoL score of EQ-5D VAS was 59.5 ± 14.6. In the EQ-5D questionnaire, pain (1.3 ± 0.5) had the highest scores, followed by anxiety/depression (1.2 ± 0.5). For the MLHFQ, the physical aspect had the highest score (11.6 ± 10.1), and the emotional aspect had the lowest (3.7 ± 5.9). Upon analysing the HR-QoL measurement tools, we observed significant correlations among them (Supporting Information, Table S2). Specifically, there were positive correlations between the EQ-5D and MLHFQ scores, suggesting a concordant assessment of HR-QoL aspects by these tools. In contrast, the EQ-5D VAS displayed an inverse correlation with both EQ-5D and MLHFQ. This indicates that higher scores on the EQ-5D and MLHFQ, reflecting poorer health or more severe symptoms, were associated with lower self-rated health status on the EQ-5D VAS.

Table 2 Quality of life assessment of identified patients

	Total (N = 134)
EQ-5D
Total, mean ± SD	6.1 ± 1.6
Mobility, mean ± SD	1.1 ± 0.3
Self-care, mean ± SD	1.1 ± 0.4
Activity, mean ± SD	1.1 ± 0.4
Pain, mean ± SD	1.3 ± 0.5
Anxiety/depression, mean ± SD	1.2 ± 0.5
EQ VAS, mean ± SD	59.6 ± 14.6
EQ-5D utility value (%), mean ± SD	81.7 ± 27.0
MLHFQ
Total, mean ± SD	21.8 ± 21.3
Physical, mean ± SD	11.6 ± 10.1
Emotional, mean ± SD	3.7 ± 5.9
Social, mean ± SD	3.8 ± 6.4

Table 3 summarizes the associations between demographic characteristics and different HR-QoL measurements (details in Supporting Information, Table S1). Only NYHA Fc was associated with all measurement tools. For EQ-5D, NYHA Fc was consistently associated with all dimensions. Furthermore, EQ-5D was sensitive to CAD, MRAs, and 6MWT. Subgroup analyses showed that CAD was associated with the sub-dimensions of activity, pain, and anxiety/depression; MRAs with anxiety/depression; and 6MWT with self-care and activity (Table 4 and Supporting Information, Table S3).

Table 3 Linear regression associations of three health-related quality of life tools with demographic characteristics, health-related factors, and functional capacity

	EQ-5D	EQ VAS	MLHFQ
β	SE	P value	β	SE	P value	β	SE	P value
Age	0.01	0.01	0.45	0.10	0.12	0.44	−0.34	0.16	0.04a
NYHA Fc	1.43	0.26	<0.01a	−8.40	2.66	<0.01a	19.67	3.48	<0.01a
6MWT	<0.01	<0.01	0.03a	0.02	0.02	0.32	<0.01	0.02	0.98
Comorbidities
CAD	−0.87	0.27	<0.01a	3.49	2.70	0.20	−5.33	3.52	0.13
CKD	0.34	0.30	0.26	−8.14	3.07	0.01a	3.61	4.02	0.37
Cardiovascular medication
MRAs	−0.89	0.34	0.01a	2.62	3.40	0.44	−3.42	4.44	0.44

Table 4 Linear regression associations of the EuroQol-5 dimension sub-item with demographic characteristics, health-related factors, and functional capacity

	Mobility	Self-care	Activity	Pain	Anxiety/depression
β	SE	P value	β	SE	P value	β	SE	P value	β	SE	P value	β	SE	P value
NYHA Fc	0.26	0.06	<0.01a	0.25	0.08	<0.01a	0.31	0.07	<0.01a	0.35	0.10	<0.01a	0.26	0.10	0.01a
6MWT	<0.01	<0.01	0.06	<0.01	<0.01	0.02a	<0.01	<0.01	<0.01a	<0.01	<0.01	0.87	<0.01	<0.01	0.78
Comorbidities
CAD	−0.09	0.06	0.13	−0.03	0.08	0.73	−0.18	0.07	0.02a	−0.35	0.10	<0.01a	−0.23	0.10	0.03a
Cardiovascular medication
MRAs	−0.14	0.07	0.06	−0.13	0.10	0.21	−0.16	0.09	0.08	−0.14	0.13	0.28	−0.32	0.13	0.02a

Similarly, NYHA Fc was associated with all components of MLHFQ (physical, emotional, and social components), whereas age was associated with emotional and social components. For EQ-5D VAS, CKD emerged as another significant determinant of HR-QoL (Table 5 and Supporting Information, Table S4).

Table 5 Linear regression associations of the Minnesota Living with Heart Failure Questionnaire sub-item with demographic characteristics, health-related factors, and functional capacity

	Physical	Emotional	Social
β	SE	P value	β	SE	P value	β	SE	P value
NYHA Fc	9.99	1.59	<0.01a	3.88	1.07	<0.01a	4.50	1.05	<0.01a
Age	−0.11	0.07	0.13	−0.11	0.05	0.03a	−0.13	0.05	0.01a

Discussion

This study utilized three different measurement tools to assess the HR-QoL of patients with HFrEF in a regional population. Interestingly, all tools in our study were sensitive to NYHA Fc, but the detection of other determinants was tool specific: CAD, MRAs, and 6MWT were detected by EQ-5D, age was picked up by MLHFQ, and CKD was captured by EQ-5D VAS. These findings, unique to each tool, offer valuable insights into understanding the determinants of HR-QoL in HFrEF patients and have substantial implications for clinical practice. Our results reinforce the need for using multiple HR-QoL measurement tools to capture a comprehensive view of patient health status, thereby aiding in the design of tailored patient-centred interventions. Intriguingly, our study revealed that EQ-5D-derived utility scores, calculated using the British EQ-5D time trade-off (TTO) tariff, were considerably higher than the PSRQoL scores obtained from the EQ-5D VAS. This discrepancy indicates that the current method of calculating EQ-5D-derived utility scores may not accurately reflect the HR-QoL of Taiwanese HFrEF patients. Consequently, our findings underscore the potential need for recalibrating the quality weights assigned to EQ-5D health states in this specific population, a crucial step to enhance the applicability of EQ-5D in accurately measuring HR-QoL in Taiwanese HFrEF patients.

This study illuminates the considerable influence of emotional aspects, such as pain and anxiety/depression, on HR-QoL in HFrEF patients, which is consistent with the previous study.¹⁹ Emotional and social dimensions were found to have more associated determinants than mobility, self-care, and physical aspects. These findings highlight the necessity of integrated mental health support in HFrEF care. While mental health in HF patients has been the focus of past research,^19,20 the management of psychological distress and pain, which can significantly affect HR-QoL with a prevalence rate of up to 84.5%,²¹ is often overlooked. In line with a study conducted in Inner Mongolia,²² our study reports high scores for pain and anxiety/depression in the EQ-5D, implying an urgent need for improved psychological and pain management for HFrEF patients. We may advocate for an expansion of the HF-PAC programme to enhance not only symptom control but also mental resilience and well-being.

This study identified age, NYHA Fc, 6MWT, CAD, CKD, and MRAs as factors significantly associated with HR-QoL scores in HFrEF patients. Notably, all tested questionnaires demonstrated a significant association with NYHA Fc, indicative of clinical symptoms. The EQ-5D, sensitive to 6MWT, CAD, and MRA determinants, may exhibit superior applicability and sensitivity among Taiwanese HFrEF patients compared with other scales. Contrary to previous studies,^22–25 long 6MWT and MRA usage were linked to improved HR-QoL, while patients with a CAD history recorded better scores. This discrepancy could stem from varying individual experiences, illness awareness, and adherence to treatment and lifestyle changes. Patients with a CAD history may have benefitted from interventions like smoking cessation, cardiopulmonary rehabilitation, and CAD case management programmes, enhancing their HR-QoL. Besides, consistent with previous findings,²⁶ our study underscored CKD's significant association with HF patients' HR-QoL.

This study's results align with international findings regarding the use of the disease-specific HR-QoL questionnaire for HF, MLHFQ. For instance, the average score in Australia stands at 28.9 (SD 23.5),²⁷ in Arabia, it is at 24.84 ± 18.25,²⁸ and in Taiwan, it ranges between 16.5 ± 12.9 (NYHA Fc II) and 25.3 ± 19.2 (NYHA Fc III),²⁹ similar to our findings. However, our study reported a higher utility score (0.817 ± 0.27) derived from EQ-5D compared with those recorded in Australia (0.6619, SD 0.27) and the United States (0.7 ± 0.24).^27,30 This discrepancy could be attributed to Taiwan's well-established HF case management system, bolstered by national policies and research funding.¹³ Additionally, our findings resonated with those of two other studies conducted among Asian ethnic groups, underscoring the potential influence of ethnic and cultural factors on the utility score.^22,23

The differences between the EQ-5D-derived utility and PSRQoL may impact cost-effectiveness analyses and value-based healthcare policy development. This discrepancy could be due to EQ-5D's ceiling effect and the lack of individualization of different settings.^31,32 The insensitivity to patient perspectives may lead to cost-effectiveness evaluation discrepancies in various interventions. Given the growing emphasis on patient-centred care in value-based healthcare policies, it is critical to integrate patient preferences and experiences into decision-making processes. The PSRQoL offers a more intuitive reflection of an individual's overall HR-QoL because self-reported measures may be influenced by subjective factors not fully captured by standardized questionnaires like the EQ-5D. Future research should consider using PSRQoL alongside HR-QoL questionnaires such as EQ-5D and MLHFQ to diversify assessment and focus on developing disease-specific questionnaires tailored to HFrEF patients' unique needs and experiences. This would ensure that cost-effectiveness analyses and value-based healthcare policies are better informed by patient preferences, leading to improved patient-centred care and outcomes.

There are several limitations to this study. First, this study's cross-sectional design was not likely to allow us to establish causal relationships between variables and HR-QoL. Longitudinal studies would be needed to determine the causal effects and trajectories in HR-QoL over time. Second, our sample was predominantly composed of acute decompensated HFrEF patients at a single medical centre in Taiwan, which may limit the generalizability of our findings to patients with HF and mildly impaired or preserved ejection fractions and other populations with different cultural, social, and healthcare backgrounds. Third, although the study used multiple questionnaires, the psychometric properties of these questionnaires, including their validity and reliability, may vary across different cultural contexts, potentially affecting the results. Finally, assessing all potential factors that could influence patients' HR-QoL in this study was not possible. Future research should address these limitations and continue to investigate the complex interplay between various factors affecting the HR-QoL of HFrEF patients.

Conclusions

In conclusion, our findings elucidate the nuanced diversity and inherent disparities between various HR-QoL measurement tools and the PSRQoL in patients with HFrEF within the HF-PAC programme in a Taiwanese context. The use of multiple HR-QoL measurement tools in assessing HR-QoL is key to uncovering varied determinants and thus providing comprehensive patient-centred care. Particularly, EQ-5D appears to be a highly sensitive tool that captures various determinants in Taiwanese HFrEF patients. This study further identified several key factors—including NYHA Fc, age, 6MWT, CAD, CKD, and MRAs—that are associated with HR-QoL, offering valuable insights that can aid clinicians in developing patient-focused interventions. Lastly, the study provides evidence of the need for recalibration of EQ-5D-derived utility scores to better reflect HR-QoL in Taiwanese HFrEF patients, thereby strengthening patient-centred care within the HF-PAC programme.

Acknowledgements

We thank Chi Mei Medical Center for the assistance and financial support.

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Funding

This study was supported by Chi Mei Medical Center and funded for English editing and submission.

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