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European Journal of Heart Failure 2002 4(3):337-343; doi:10.1016/S1388-9842(02)00006-5
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© 2002 European Society of Cardiology

Self-rating of quality of life provides additional prognostic information in heart failure. Insights into the EPICAL study

François Allaa,*, Serge Briançona, Francis Guillemina, Yves Juillièreb, Paul-Michel Mertèsb, Jean-Pierre Villemotb, Faiez Zannadb,c and for the EPICAL Investigatorsb,c

a Department of Epidemiology University Hospital, Nancy, France
b Department of Cardiology University Hospital, Nancy, France
c CIC, INSERM-University Hospital Nancy, France

* Corresponding author. Service d'épidémiologie et évaluation cliniques, CHU de Nancy, 54035 Nancy, France. Tel.: +33-3838-52163; fax: +33-3838-51205. E-mail address: f.alla{at}chu-nancy.fr


   Abstract
Top
 Abstract
1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Acknowledgments
 References
 
Background: The relationship between quality of life (QoL) and survival have been poorly investigated. The aim of this study was to determine the value of QoL score as a prognostic factor in a prospective cohort of patients with advanced chronic heart failure (CHF).

Methods: QoL assessment was performed with a generic questionnaire: the Duke Health Profile (DHP) and a disease-specific instrument: the Minnesota Living With Heart Failure Questionnaire (LIhFE), in a sample of 108 patients registered in the EPICAL program (hospitalised patients with severe CHF defined by a NYHA grade III/IV, oedema or hypotension, and LVEF <30%). Prognostic value of general, physical, mental and social dimensions on survival and hospital-free survival were tested in a Cox model.

Results: One-year survival rate was 76%, 1-year hospital-free survival 38%. QoL was significantly associated with outcomes: for both questionnaires, a 10-point decrement in baseline score was associated with a 23–36% increase in the risk of death or hospitalisation for heart failure. For hospital-free survival, this relationship remained significant after adjustment for others prognostic factors.

Conclusion: QoL score is a predictive factor of survival and an independent predictive factor of hospital-free survival in patients with advanced CHF. This assessment may provide additional information for clinical management and therapeutic decisions.

Key Words: Quality of life • Heart failure • Prognosis

Received May 29, 2001; Revised August 29, 2001; Accepted October 23, 2001


   1. Introduction
Top
 Abstract
 1. Introduction
2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Acknowledgments
 References
 
In spite of a low overall survival rate, life expectancy for congestive heart failure (CHF) patients is increasing steadily with recent progress in therapeutic management: Improving Health-Related Quality of life (HR-QoL) has become an important objective for treatment [1]. However, quality of life is a multidimensional concept, based on the patient's own perception of his health which integrates not only the functional or physical dimensions of the disease, but also psychological and social dimensions [2]. These functions are not generally taken into account by functional aptitude indicators commonly measured by the practitioner.

Several tools, generic or disease-specific, have been validated in heart failure patients and are used to evaluate changes in quality of life linked to disease or to treatment [3]. Generic tools are usable whatever the disease or the population. The most commonly used in heart failure are the Nottingham health profile [4], the Sickness Impact Profile [5], the MOS 36-Item Short Form [6] and the Duke Health Profile [7]. Disease-specific instruments such as the Chronic Heart failure Questionnaire [8], the Yale Scale [9], the Quality of Life Questionnaire in Severe Heart Failure [10], the Minnesota Living with Heart Failure Questionnaire [11] are used in heart failure.

Many studies have utilised the HR-QoL score as an outcome in clinical trials [12]. To our knowledge, few studies have utilised this measurement for predicting death [13–15] or hospitalisation [13,16]. These studies suggested a relationship between poor baseline HR-QoL and poor outcome.

The aim of this study was to assess the value of HR-QoL as a prognostic factor of survival and of hospital-free survival in a prospective cohort of patients with advanced CHF.


   2. Patients and methods
Top
 Abstract
 1. Introduction
 2. Patients and methods
3. Results
 4. Discussion
 5. Conclusion
 Acknowledgments
 References
 
2.1. Sample population
Our study is based on a sample of 108 patients registered in the EPICAL cohort, which has been previously described in detail [17]. A comprehensive registration of patients hospitalised with advanced CHF, aged 20–80 during the year 1994, living in the community of the Lorraine region in France (2.3 million inhabitants) was performed prospectively by trained research nurses and medical doctors. Advanced CHF was defined as hospital admission in the presence of NYHA class III or IV symptoms, radiological and/or clinical signs of pulmonary congestion and/or signs of peripheral oedema, left ventricular ejection fraction <30% or cardio-thoracic ratio >60%.

For the purpose of this study, patients who were still alive 1 month after discharge from their primary hospitalisation and who had at this time a measurement of HR-QoL were retained.

HR-QoL measurement-HR-QoL questionnaires were administered at home by a nurse. Data collection was performed 1 month after hospital discharge, with a generic questionnaire: the Duke Health Profile (DHP) [7] and a disease-specific instrument: the Minnesota Living With Heart Failure Questionnaire (LIhFE) [11].

In this study we used dimensions common to both questionnaires.

The DHP is a generic, 17-item patient self-assessment, including a general score and nine dimensions, among which three were retained for the purpose of this analysis: physical (5 items), mental (5 items) and social (5 items).

The LIhFE is a 21-item patient self-assessment measurement which was developed to evaluate the response to treatment in heart failure. It measures patients’ perceptions concerning the effect of CHF on their daily life [11]. We selected a general score and three out of four Rector's dimensions [18]: physical (eight items), mental (five items) and social (two items) [19].

Both questionnaires had been previously translated, trans-culturally adapted and validated in French [19].

General physical, social and mental scores were calculated according to the authors’ recommendations but were all standardised from 0 (poorest HR-QoL) to 100 (best HR-QoL).

2.2. Outcome
During follow-up, death and hospitalisation were reported from hospitals, general practitioners and administrative records. We retained the first hospitalisation with decompensated HF using the same decompensation definition as for index hospitalisation.

Hospital-free survival outcome included death and first hospitalisation with decompensated HF after the index hospitalisation.

2.3. Adjustment factors
Adjustment prognostic factors were collected during the index hospitalisation: socio-demographic (age, sex, occupation, academic level, autonomy — whether or not the subject was assisted by relatives or lived in an institution), clinical (cardiovascular risk factors, body mass index), etiological (aetiology, duration of causal disease), serious co-morbidities (history or presence of cancer, cirrhosis, chronic broncho-pneumonopathy, arthritis, cerebral vascular disease), electrocardiographic (heart rate, rhythm, conduction), biological (serum potassium, creatinine and sodium), therapeutic (angiotensin–converting–enzyme inhibitor prescription), hemodynamic (left ventricular ejection fraction), and hospital use (index hospitalisation duration, previous hospitalisations for decompensated HF) variables.

Adjustment factors consisted of the variables independently associated with survival rate in this sample [20]: heart rate, serum creatinine, serum sodium, previous hospitalisations for decompensated HF, age, autonomy, serious co-morbidities, aetiology and duration of causal disease. The other factors were tested in a forward stepwise Cox multivariate analysis; neither of them had an independent relationship with survival or hospital-free survival.

2.4. Statistical analysis
After sample description and its survival characteristics, we explored the prognostic value of HR-QoL scores.

Qualitative variables were expressed as percentage, quantitative variables and HR-QoL scores as mean±standard deviation of the mean. Survival rates were estimated using the Kaplan–Meier method.

Prognostic values of HR-QoL scores for survival and hospital-free survival, respectively, were tested in eight separate Cox models with the Wald test [21], firstly in a univariate analysis and secondly in an adjusted analysis for other prognostic factors. The relationship between HR-QoL scores and outcomes were expressed by the relative-risk or the percentage increase in the risk [(RR–1)x100].

For all HR-QoL scores, relative risks of death or hospitalisation were calculated for a 10-point decrement in the score. Their departure from linearity were tested using a Chi-square test (the difference between trend test and log rank test was compared to 0 [22]). All analyses were performed using BMDP© software [23].

All patients gave informed consent. Data analysis was anonymous, and the CNIL (Commission Nationale Informatique et Liberté – the French national computer data commission) gave approval for data collection and data processing.


   3. Results
Top
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
4. Discussion
 5. Conclusion
 Acknowledgments
 References
 
One hundred and eight patients were included (77% men, mean age 64±year, 42% ischemic aetiology, mean LVEF 21.6±0.5%, 42% prior decompensation). HR-QoL was poor, the mean scores were 64/100 for the LIhFE and 54/100 for the DHP and ranged, respectively, from 11 to 100 and from 13 to 93 (Table 1).


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  		Table 1 HR-QoL scores 1 month after hospital discharge

 
HR-QoL scores were not correlated with other factors, in particular factors previously shown to be related to disease severity (heart rate, serum sodium, ejection fraction, age, sex, aetiology). However, patients with renal failure had a lower general DHP score than patients without renal failure (36 vs. 51, P=0.006) and patients with previous decompensation had lower LIhFE and DHP general scores than patients without previous decompensation (respectively 46 vs. 63, P=0.002; 45 vs. 51, P=0.05).

Average follow-up was 18 months [12–24 months]. During follow-up, we registered 25 deaths and 54 hospitalisations for heart failure. The 1-year survival rate was 76% and the 1-year hospital-free survival rate was 52% (Fig. 1).


Figure 1
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  		Fig. 1 Kaplan–Meier survival and hospital-free survival (hospitalisation for CHF).

 
3.1. Survival
In univariate analysis, general, physical and mental dimensions of the LIhFE and general and physical dimensions of DHP were significantly associated with survival. A 10-point decrement in LIhFE and DHP global scores were associated respectively with a 23% and 27% increase in the risk of death. This relationship disappeared after adjustment for other prognostic factors (Table 2).


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  		Table 2 Relationship between HR-QoL dimensions and survival

 
3.2. Hospital-free survival
In univariate analysis, general, physical, mental and social dimensions of the LIhFE as well as general and physical dimension of the DHP were significantly associated with HF hospitalisations. A 10-point decrement in LIhFE and DHP global scores was associated respectively with a 31% and 36% increase in the risk of death or hospitalisation. This relationship remained significant even after adjustment (Table 3).


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  		Table 3 Relationship between HR-QoL dimensions and hospital-free survival (hospitalisation for CHF)

 
The relationship between LIhFE mental dimension and the two endpoints was not linear: patients with median scores (between 40 and 60) had the poorest survival rate and hospital-free survival as compared with patients with the highest scores (>60) or the lowest scores (<40).


   4. Discussion
Top
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
5. Conclusion
 Acknowledgments
 References
 
In this prospective cohort study, we show that HR-QoL score is a predictive factor of death and is an independent predictive factor of HF hospitalisation in patients with advanced CHF: A 10-point decrement in baseline HR-QoL score was associated with a 23–36% increase of 1 year mortality or re-hospitalisation. To our knowledge, this study is the first focused on severe CHF.

In accordance with previous studies [11], baseline HR-QoL scores were poor.

HR-QoL scores have been frequently reported to be significant predictive factors of survival and/or hospitalisation in various diseases, but few studies have been done on cardiovascular diseases [24–26]. Although HR-QoL measurements are occasionally used for assessing treatment effects in CHF [1], to our knowledge, only four studies have investigated the relationship between HR-QoL and outcome in patients with CHF [13–16]. Our results are consistent with those of previously published articles, which related poor HR-QoL to poor outcome in various cardiovascular diseases.

In the V-HeFT II study a low univariate relation was shown between HR-QoL score and mortality [15]. In a large patient population with an ejection fraction <0.35 (SOLVD cohort [13]), HR-QoL was an independent predictive factor of mortality and CHF-related hospitalisation. Another study showed that baseline LIhFE scores were lower in hospitalised patients vs. non-hospitalised patients during a 6-month follow-up [16]. In a population of moderate CHF patients, physical and emotional HR-QoL scores could predict death, but this relationship disappeared after adjustment for depressed mood [14].

4.1. HR-QoL and functional capacity
Previous studies have shown that exercise capacity in the laboratory is not representative of exercise capacity in normal daily activities [27]. HR-QoL measurement may supply a better evaluation of the impact of CHF in daily life [28]: it takes into account the importance attributed by the patient to his physical limitations [11], their repercussions on his mental and social life and on his capacity to cope with CHF.

Wilson et al. have shown that there was no relationship between the LIhFE score and peak exercise VO2 [29]. Rector et al. established a correlation between changes in exercise time and peak oxygen consumption and changes in HR-QoL scores during a 3-month follow-up. However, this correlation was weak and some patients had substantial improvement in their exercise times without improving on the LIhFE general score [18]. Several studies have established that HR-QoL measurement provide supplementary information to functional capacity in CHF [13] and in other cardiovascular diseases [14,30].

Our results confirm that self-rating of QoL in severe CHF, provides additional prognostic information as compared with objective known measures of disease severity [20]. Indeed, self-rating of QoL integrates not only the objective functional or physical aspect of the disease, but also the patient's own perception of his health and psycho-social aspects. These aspects, related to patient evaluation, are poorly explained by objective evaluations. This finding must be taken into account by practitioners and clinical therapists. We suggest that these simple measurements could be used in addition to other functional measurements for routine clinical management and therapeutic decisions as well as for risk stratification in clinical trials.

4.2. Relationship between mental dimension of HR-QoL and survival
In our study, the mental dimension of LIhFE was a prognostic factor of survival and hospitalisation. This finding is inconsistent with previous studies. In the majority of studies in CHF [13] and other cardiovascular diseases [24], mental dimensions of HR-QoL questionnaires were not related to survival. However, in a study including 62 males with CHF, the emotional dimension of LIhFE was significantly lower in hospitalised patients vs. non-hospitalised patients during a 6-month follow-up [16]. In the same way, the relationship between depression and survival was not clearly defined and results were controversial [31].

We suggest that these inconsistent results, in particular previously published negative results, may be explained by a non-linear relationship between the mental dimension of HR-QoL and survival. In our study, median mental heath had a negative impact compared with good mental health or poor mental health. In this context, the codification of this variable is very important: for example, in our sample, if we used a continuous codification for mental heath, the result was not significant. A test of linearity of the relationship between HR-QoL dimensions and survival is therefore necessary. The explanation of this non-linear relationship is not unequivocal. One can hypothesise that subjects with very low levels of mental health behave differently and especially have a way of life leading to less decompensations. It can also be related to the complexity of the relations of mental health status with capacity of self-evaluation, functional capacity, mood disturbance and well-being.

Murberg et al. [14] indicated that emotional HR-QoL score (assessed with a scale constituted by the LIhFE and Quality of Life Questionnaire in Severe Heart Failure) predicted death, but this prognostic value disappeared after adjustment for depressed mood [14]. According to this author, depressed mood is a major independent factor of death and the relation between HR-QoL and survival is better accounted for as a result of depression. As previously shown by the same author [32], depression is a determinant of HR-QoL. However, depressed mood can also be the result of a patient's subjective evaluation of disease severity. In coronary disease, an association between depression and a poorer HR-QoL or functional status is commonly related [33,34]. The relationship between depression and cardiovascular disease may have a physiological mechanism: one possible explanation is the altered sympathetic/parasympathetic ratio tone linked to depression [35].

We propose incorporating HR-QoL measurement and depression scale into future studies to improve our knowledge of their inter-relationships and to explore the complex non-linear relations between emotional distress and survival.

The relationship between mental dimension, depression and prognosis, in accordance with similar findings in patients with coronary disease [35], may modify our therapeutic interventions which are based only on physiopathological aspects of the disease.

CHF treatment may be improved by the addition of interventions acting on emotional distress, whether medical (antidepressant drugs) or not (psychological rehabilitation).

The recent ‘Sertraline for treatment of major depression after acute myocardial infarction’ (the SADHAT trial) inaugurates a series of studies intended to address the impact of antidepressant therapy on cardiovascular risks [36].

We suggest that future objectives in CHF research include a development and validation of tools to assess emotional distress in CHF, an estimation of the relevance of depression assessment in CHF patients, as well as an evaluation of specific drug therapy.

4.3. Generic and disease-specific tools
The prognostic value of our specific score was not very different from the generic one. According to some authors, specific instruments may be more responsive to change than generic instruments [2]. But, this has little relevance in prognostic evaluation. Death may be caused by the severity of disease (evaluated with specific tools) and influenced by co-morbid states. However, specific measurement seems best for predicting hospital-free survival, which is more disease-related than survival.

4.4. Limitations
Due to the limited number of deaths, this study was underpowered for testing the relationship between HR-QoL and survival. This fact may explain the non-significant relationship between HR-QoL and survival rate in adjusted models.


   5. Conclusion
Top
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
Acknowledgments
 References
 
This report provides additional arguments in favour of the usefulness of HR-QoL measurements in addition to traditional observer measures in patients with advanced CHF as a non-invasive assessment; the score obtained is a predictive factor of death and is an independent predictive factor of hospitalisation for HF; this prediction is clinically meaningful.

This study also describes the impact of the patient's negative mental perception of his disease on outcome. These findings must be confirmed and may be useful for further research.

HR-QoL assessment may provide additional information for routine clinical management and therapeutic decisions as well as for risk stratification in clinical trials.


   Acknowledgments
Top
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Acknowledgments
References
 
This study was funded by a PHRC grant (Programme Hospitalier de Recherche Clinique) from the Health Ministry (France) and by the Association de Recherche et d'Information Scientifique en Cardiologie (ARISC-Nancy, France). Steering Committee: Prof. E. Aliot (Nancy), Dr Ch. Breton (St Max), Prof. S. Briançon (Nancy), Prof. Y. Juillière (Vandoeuvre), Dr K. Khalifé (Metz), Dr P.M. Mertès (Vandoeuvre), Dr J.L. Neimann (Metz), Prof. J.P. Villemot (Vandoeuvre), Dr F. Zannad (Nancy).


   References
Top
 Abstract
 1. Introduction
 2. Patients and methods
 3. Results
 4. Discussion
 5. Conclusion
 Acknowledgments
 References
 

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M. A.J. Visschedijk, B. M.J. Uitdehaag, M. Klein, E. van der Ploeg, E. H. Collette, L. Vleugels, L. E.M.A. Pfennings, E. L.J. Hoogervorst, H. M. van der Ploeg, and C. H. Polman
Value of health-related quality of life to predict disability course in multiple sclerosis
Neurology, December 14, 2004; 63(11): 2046 - 2050.
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G. E. Soto, P. Jones, W. S. Weintraub, H. M. Krumholz, and J. A. Spertus
Prognostic Value of Health Status in Patients With Heart Failure After Acute Myocardial Infarction
Circulation, August 3, 2004; 110(5): 546 - 551.
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