Elsevier

International Journal of Cardiology

Volume 227, 15 January 2017, Pages 172-176
International Journal of Cardiology

Comparison of contemporaneous responses for EQ-5D-3L and Minnesota Living with Heart Failure; a case for disease specific multiattribute utility instrument in cardiovascular conditions

https://doi.org/10.1016/j.ijcard.2016.11.030Get rights and content

Abstract

Background

The EQ-5D-3L, a generic multi-attribute utility instrument (MAUI), is widely employed to assist in economic evaluations in health care. The EQ-5D-3L lacks sensitivity when used in conditions such as cardiovascular disease (CVD). Although there are number of CVD specific quality of life instruments, currently, there are no CVD specific MAUIs. The aim of this study is to investigate the discriminative ability and responsiveness of the EQ-5D-3L and the Minnesota Living with Heart Failure Questionnaire (MLHF), a CVD specific quality of life instrument in a group of heart failure patients.

Methods

The psychometric performance of the EQ-5D-3L and the MLHF was assessed using data from a randomised trial for a heart failure management intervention. The two instruments were compared for discrimination, responsiveness and agreement. The severity groups were defined using New York Heart Association functional classes.

Results

The effect sizes for severe classes were generally similar showing good discrimination. The MLHF recorded better responsiveness between the time points than the EQ-5D-3L which was indicated by higher effect sizes and standardised response means. The change in MLHF summary scores between the time points was significant (p < 0.005; paired t-test). The overall agreement between the two measures was low.

Conclusion

The low correlation indicates that the two classification systems cover different aspects of health space. Comparison of CVD specific instruments with other generic MAUIs such as EQ-5D-3L and AQOL-8D is recommended for further research.

Introduction

Globally 17.3 million deaths are attributable to cardiovascular disease (CVD). Direct and indirect cost burden of heart disease more than US $316 billion. [1] In Australia around 300,000 are living with the syndrome heart failure (HF) with 30,000 cases newly diagnosed each year [2]. HF is a debilitating chronic condition that reduces the quality of life of its sufferers and has a devastating effect on everyday life activities of the patients. Those affected often feel tired and fatigued and experience shortness of breath (ranging from New York Heart Association Class II to IV), paroxysmal nocturnal dyspnoea and peripheral oedema. A number of new, and often costly, innovations have been introduced to manage and improve health outcomes in patients diagnosed with CVD [3].

In order to assess the impact of these innovations on a patient's quality of life, numerous instruments are being utilised in clinical trials. Some examples include: Chronic HF Questionnaire [4]; Quality of life Questionnaire for Severe HF [5]; Kansas City Cardiomyopathy Questionnaire [6]; Left Ventricular Dysfunction Questionnaire [7]; and Minnesota Living with HF Questionnaire (MLHF) [8]. All of these instruments are considered to have better sensitivity than generic instruments such as the 36-item Short Form Health Survey (SF-36) to measure quality of life changes in HF by the clinicians [9], [10]. However, many of these specialised HF quality of life instruments are unsuitable for economic evaluations as they do not have associated utility algorithms [11]. Utility represents the relative preference for a given health state and is anchored between 0 and 1 death-full health scale. The utility for a health state can be attained by a preference elicitation technique used in health states valuations. The health states can only be defined using Multi Attribute Utility Instruments (MAUIs) which contain a descriptive system as well as scoring system. To date, cardiovascular research has had to rely on generic health utility instruments (known as multi-attribute utility instruments or MAUIs) such as the EuroQol Group 5-dimention 3-level (EQ-5D-3L) and Short Form 6-domain (SF-6D) questionnaires to measure health related utility outcomes. However, there is a growing consensus that generic MAUIs are not sufficiently sensitive to measure the quality of life in HF patients [12]. As both the EQ-5D-3L and the MLHF have been widely used in measuring quality of life they are generally considered valid and sensitive instruments for heart failure. Given a paucity of a cardiovascular specific MAUIs and limitations with using generic MAUIs in patients with specific disease conditions including HF, it warrants a direct comparison between a generic MAUI and a HF specific quality of life questionnaire such as the MLHF to determine the sensitivity and responsiveness of these instruments. During the past decade, there have been many efforts to compare quality of life instruments [13], [14], many of which have mainly focused on validity and sensitivity [15]. These studies have merely presented the correlation between the instrument scores. Some have used statistical tests such as ANOVA and t-tests to show the association between the different instruments. More recently, there have been attempts to compare instruments with regards to their relative discrimination and responsiveness [16]. That is, increasing underlying clinical severity associated with reduced quality of life should be captured by a quality of life instrument. A quality of life instrument should be able to differentiate between levels of clinical severity. Any quality of life score should decrease with increasing clinical severity of the condition, and vice versa. This discriminative ability is important in a quality of life instrument [17]. The responsiveness of an instrument capture changes in quality of life scores between time points. It is anticipated that a health intervention would influence quality of life. Hence, ability of an instrument to capture this change is important [16]. The aim of this study is to compare the EQ-5D, a generic MAUI, and the MLHF, a HF specific quality of life instrument, to determine the discriminative ability between clinical severity classes and the responsiveness between time points.

Section snippets

Methods

Data used in this study were part of a multicentre randomised controlled trial to compare the multidisciplinary chronic HF management delivered via a nurse-led outreach, home-based intervention with an outpatient or a specialised chronic HF clinic-based intervention. A detailed description of the rationale and design and primary results has been published previously [18], [19], [20]. The inclusion criteria included the moderate to severe symptoms of HF with NYHA functional classes II–III with

Results

There were 280 and 175 patients who responded to both the instruments at T1 and T2 respectively. More than 80% of the sample were above the age of 60 with more males (72.5%) than females (Table 1). More than 50% of the patients had more than 12 years of education. Majority of the sample (75%) were NYHA classes III and IV. At T1, the mean EQ-5D-3L utility score for the full sample was 0.71 and a MLHF summary score of 49.9. At T2, this was 0.71 and 33.4 for EQ-5D-3L utility and MLHF summary score

Discussion

This is the first study to directly compare the EQ-5D-3L and the MLHF scores in patients hospitalised with HF. Results of this study confirm that MLHF is a better measure of quality of life changes in HF patients than the EQ-5D-3L. The MLHF showed better discrimination and responsiveness across the spectrum of HF severity than the EQ-5D-3L. Our results suggest a disease specific MAUI arising from the MLHF could be more appropriate to measure utility change in HF patients than the EQ-5D-3L. This

Conclusion

In conclusion, we found significant differences between the EQ-5D-3L and the MLHF. The MLHF was more responsive to capture HF specific quality of life changes. Further comparisons of CVD specific instruments with other MAUIs including the EQ-5D-5L and the AQOL-8D which contain more levels and dimensions are recommended for further research.

Conflict of interest

All authors declare no potential conflict of interest.

Acknowledgments and funding source

The study was funded by a National Health and Medical Research Council of Australia program grant [no 519823]. SS is supported by the National Health and Medical Research Council of Australia (APP1044897). SK and JB are funded by the Centre for Research Excellence to Reduce Inequality in Heart Disease.

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    This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

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