The prognostic value of quality of life in heart failure: insights from the RECOLFACA registry: a commentary

This invited commentary refers to ‘Quality of life is an independent predictor of mortality in patients with heart failure: a prospective cohort study from the Colombian heart failure registry (RECOLFACA)’, by J.E. Gómez-Mesa et al., https://doi.org/10.1093/eurjcn/zvae117. Improving health-related quality of life (HRQoL) is one of the main goals of heart failure (HF) management.1 Health-related quality of life is a concept that, while lacking a universally agreed-upon definition,2 generally refers to an individual’s self-perceived impact of how a medical condition, its symptoms, and treatments affect their overall well-being. This concept encompasses multiple domains, including physical, psychological, spiritual, and social aspects.3 As a patient-reported outcome measure (PROM), HRQoL offers valuable insights into how treatments affect patients from their perspective, complementing clinical endpoints such as mortality and hospitalization rates.2,4 In the context of HF, assessing HRQoL is particularly important, as it serves as a significant outcome measure, capturing the broader impact of HF on patients’ daily lives. This focus on HRQoL allows for a more comprehensive understanding of treatment effectiveness, beyond traditional clinical markers, by addressing how HF shapes a patient’s quality of life (QoL).4,5 Quality of life (QoL) was found an independent predictor of all-cause mortality in the study of Gómez-Mesa et al.,6 with data from the Colombia registry (RECOLFACA). Quality of life was measured with the EuroQoL-5D (EQ-5D) questionnaire, and two independent predictors of mortality were obtained: the visual analogue scale and the utility score. The study included 2514 patients from 60 different centres in Colombia, showing a negative correlation relation of QoL and survival, even after adjusting for socioeconomic factors and severity of disease symptoms. The results of this study are in line with previous trials such as PARADIGM-HF7 and the G-CHF,8 with the later one being the largest study until now known to investigate the relation between QoL and mortality in HF. One important issue that should be considered from Gómez-Mesa et al.6 was the exclusion of patients that had limited access to healthcare facilities or lack of family/social support. Including these patients may significantly alter the results, as they are likely to experience worst QoL. Social support is a well-known associated factor in determining QoL.9 Social support is a multi-faced concept and one of the key social determinants of health that positively influences disease-related outcomes in HF.10 The relationship between social support and QoL in patients with HF is complex and multidimensional. Social support influences QoL through various mechanisms, including its impact on mental health, self-management, and access to healthcare resources. These interactions create a dynamic process where strong social networks not only provide emotional and practical assistance but also improve self-care behaviours, reduce psychological distress, and facilitate access to timely medical care, ultimately enhancing QoL.11 Limited access to high-quality health care is often associated with delayed or insufficient preventive care and disease management. This lack of timely medical intervention exacerbates health disparities and contributes to poorer outcomes, particularly for patients with HF who require consistent care and monitoring to manage their condition effectively.10,11 The study of Gómez-Mesa et al. adds valuable information from South America about HF and QoL to the already existing information mostly coming from Europe and North America.7,8 Furthermore, this study used a generic instrument to measure HRQoL. Generic tools provide an overall evaluation of health status and, on the other hand, disease-specific tools that evaluate particular aspects of HRQoL relevant to the specific condition.4,12 As indicated in a recent review, several studies have employed both generic and disease-specific tools simultaneously, to investigate differences in HRQoL.13 These studies consistently report similar findings across various variables such as age, sex, New York Heart Association functional class, ejection fraction, and geographic location. Taking that into consideration, along with the findings from Gómez-Mesa et al.6 which align with the existing literature,7,8 the next step in future research is to evaluate the practical implications of the EQ-5D. Moving to more patient-centred approaches in the management of HF, PROMs and particularly HRQoL assessment tools have become increasingly important as endpoints in clinical trials. They are commonly used to support therapeutic decision-making, evaluate the effectiveness of treatment, and guide future patient care.4,5 These measure not only informs therapeutic choices and disease management but also provides valuable prognostic information.5,12 The question is which tools are the more appropriate. As noted by Savarese et al.,5 there are advantages to using non-disease-specific tools compared with disease-specific. Non-disease-specific can be particularly beneficial when evaluating HF interventions that improve symptoms such as dyspnoea, but may also lead to significant complications. These broader tools can capture the overall impact of treatments, including complications that may be overlooked by disease-specific tools. Thus, the next step is to evaluate the clinical utility of the EQ-5D as a screening tool for QoL deficits and to assess the impact of treating these deficits on clinical outcomes. Investigating these aspects will not only broaden the applicability of the EQ-5D but will also address the concern raised by the authors6 regarding the need for freely accessible tools such as EQ-5D, particularly in studies conducted in low-income countries. No funding was received. There are no new data associated with this article.