Abstract

Background: current understanding of quality of life in heart failure is largely derived from clinical trials. Older people, women and those with co-morbidities are underrepresented in these. Little is known about factors predictive of quality of life amongst older people with heart failure recruited from community settings.

Objective: to identify factors predictive of quality of life amongst older people recruited from community settings.

Design: prospective questionnaire survey.

Setting: general practice surgeries located in four areas of the UK: Bradford, Barnsley, East Devon and West Hampshire.

Subjects: a total of 542 people aged >60 years with heart failure.

Methods: participants completed a postal questionnaire, which included a disease-specific measure (Kansas City Cardiomyopathy Questionnaire), a generic quality-of-life measure (SF-36) and sociodemographic information.

Results: a multiple linear regression analysis identified the following factors as predictive of decreased quality of life: being female, being in New York Heart Association (NYHA) functional class III or IV, showing evidence of depression, being in socioeconomic groups III–V and experiencing two or more co-morbidities. Older age was associated with decreased quality of life, as measured by a generic health-related quality-of-life tool (the SF-36 mental and physical health functioning scales) but not by a disease-specific tool (the Kansas City Cardiomyopathy Questionnaire).

Conclusion: findings from the study suggest that quality of life for older people with heart failure can be described as challenging and difficult, particularly for women, those in a high NYHA class, patients showing evidence of depression, patients in socioeconomic groups III–V, those experiencing two or more co-morbidities and the ‘oldest old’. Such information can help clinicians working with older people identify those at risk of reduced quality of life and target interventions appropriately.

Background

Heart failure is predominantly a ‘disease of older people’ [1], affecting approximately 7% of those aged 75–84 years and 15% of those aged 85 years and older [2]. In the UK, hospitalisation rates are higher for heart failure than for any other condition [3], and it is among the top five reasons for general practitioner (GP) consultations among men >75 years and among women >85 years [4]. Therefore, heart failure constitutes a major cause of morbidity and represents one of the most expensive conditions for the National Health Service to manage.

The primary goal of heart failure management is to maximise life expectancy and improve quality of life [5]. Maximising quality of life is particularly important, given that the prognosis in heart failure is worse than in either breast or prostate cancer [6]. Indeed, there is evidence that patients place a high priority on quality of life; for example, it has been found that many people with heart failure are willing to risk the possibility of drug-induced death for an improvement in quality of life [7]. Quality of life has also been identified as predictive of hospitalisation and mortality in heart failure [8–10].

However, few studies have reported the impact of heart failure upon quality of life within community samples. Indeed, current understanding of quality of life in heart failure is derived primarily from clinical trials in which older people, women and those with co-morbidities are underrepresented [11]. Therefore, these data cannot be extrapolated to community patients, about whom little is known [12].

One study did examine quality of life within a community-based sample of 426 patients with heart failure [12], identifying statistically significant impairments in all domains of quality of life. However, a generic measure of quality of life was used (the SF-36), which is not as sensitive as disease-specific measures [13]. The study identified that New York Heart Association (NYHA) classification was associated with quality of life and found a gender difference in quality-of-life impairment when compared with a disease-free population, with men worse affected. However, other factors predictive of quality of life were not explored.

Past research from non-community samples has identified possible associations between quality of life and NYHA functional class [14–18] and depression [19, 20]. Findings regarding the relationship between quality of life and gender have been inconclusive, although there is some evidence that women with heart failure experience poorer quality of life than men with heart failure [19, 21–24]. Older age has been found to be associated with better quality of life [18, 24–26]. Two studies have identified no association between quality of life and co-morbidities [18, 25], although this may reflect study design.

Little is known about which older people being cared for in the community are at risk of reduced quality of life. The study presented in this article therefore fills a gap in current knowledge by identifying factors predictive of quality of life amongst older people with heart failure recruited from community settings.

Methods

A total of 542 people aged >60 years were recruited from 16 GP surgeries in four areas of the UK: Bradford, Barnsley, East Devon and West Hampshire. These areas were selected to maximise demographic variability on key factors (rural/urban, presence/absence of heavy industry and socioeconomic status). A pragmatic means of identifying heart failure patients was adopted [27]. This involves searching initially for patients with a Read Code (Read Codes are a coded thesaurus of clinical terms, which enable UK clinicians to make effective use of computer systems) for heart failure and adding to this the group receiving regular prescriptions for loop diuretics and angiotensin-converting enzyme (ACE) inhibitors. (Diuretics and ACE inhibitors are the basis for the pharmacological treatment of patients with heart failure.) This patient list is then checked by a GP to ensure that all identified patients have had heart failure. The method does not attempt to further refine diagnostic accuracy (such as chest X-rays or echocardiograms), as the aim was to develop a procedure that was easily applicable in most general practice circumstances and reflected the diagnostic uncertainty GPs encounter when managing older heart failure patients.

Patients were considered eligible for recruitment into the study if they were >60 years, could speak English, did not have evidence of significant cognitive impairment and were NYHA class II–IV. Thirty-one participants were excluded from invitation to participation by GPs for other reasons including frailty and severe medical problems. The study was granted ethical approval by the Cardiff Multi‐Centre Research ethics committee, and all participants gave written informed consent to participate.

A total of 748 patients (48% of total eligible from all the practices) replied to say that they would take part in the study and were telephoned by a researcher. Patients reporting NYHA class I (130), declining further participation (11), unable to contact by telephone (17) and who had died (3) were excluded at this point. From the remaining 587 participants who initially consented to receive questionnaires, 92% were returned, resulting in a baseline sample size of 542 participants. Patients who were unwilling or unable to participate in the study were more likely to be female (46% of participants were female compared with 63% of non-participants; χ2 = 40.22, df = 1, P<0.001) and older (median age of participants = 77; median age of non-participants = 82; χ2 = 13.28, df = 3, P = 0.004) than participants. Data available for four practices also indicated that non-participants were less likely to be in NYHA class IV according to their GP (21% of non-participants being classified as NYHA class III or IV, compared with 8.3% of participants; χ2 = 13.28, df = 3, P = 0.004).

Patients participating in this ongoing study completed the following questionnaires every 3 months for 24 months: Kansas City Cardiomyopathy Questionnaire [28], SF-36, Geriatric Depression Scale (GDS) (five-item) [29], NYHA grade and a service use questionnaire. The questionnaires were administered by post. The current article is based upon questionnaire data collected at baseline (between August 2003 and April 2004). Whilst all participants reported NYHA class II–IV at the time of the telephone screening, by the time of completing the baseline questionnaire, 20 participants (3.7%) reported an improvement to NYHA class I. This change reflects the illness trajectory of heart failure, and these participants were included in the study.

Statistical methods

Factors predictive of quality of life were identified using multiple linear regression analysis. The variables detailed in the first section of Table 1 were the potential predictors. Quality-of-life outcomes were the Kansas City Cardiomyopathy Questionnaire overall summary score, the Kansas City Cardiomyopathy Questionnaire clinical summary score and the SF-36 physical functioning and mental health scales. Age and number of co-morbidities were entered categorically because they showed non-linear effects. Socioeconomic class and NYHA score were dichotomised because of small numbers in some categories. (Age norms were not applied to SF-36 scores because this does not allow the relationship with age to be examined). A unified linear regression model was applied to all the outcomes consisting of all the potential predictor variables except ‘living alone’ and ‘marital status’, which did not contribute to any model in the exploratory analyses. Gender, baseline NYHA, evidence of depression, age group, socioeconomic status and number of co-morbidities were therefore controlled for in all models. Residual plots were used to check the model assumptions of linearity, normality and constant variance, and the effect of outlying values was examined. For each model, individuals were excluded from the analysis if data on the quality-of-life outcome measure or any of the predictor variables were missing. Because of the large sample size, small effects would achieve statistical significance. The sample size used for the study was determined in relation to the planned longitudinal analyses for the study as a whole.

Table 1.

Participant characteristics and outcome scores

Predictor variables n 
Gender  
    Male 293 
    Female 249 
Baseline NYHA  
    Ia and II 329 
    III and IV 211 
Depression as measured by the GDS-5  
    No evidence of depression 287 
    Evidence of depression 254 
Age groups  
    60–64 45 
    65–69 63 
    70–74 117 
    75–79 104 
    80–84 125 
    85+ 88 
Socioeconomic status  
    SE I and II 186 
    SE III–V 356 
Co-morbidities  
    0 and 1 168 
    2 162 
    3 115 
    4+ 97 
Marital status  
    Married 297 
    Widowed, divorced, single 245 
Household  
    Living alone 194 
    Living with others 348 
Quality-of-life outcome measuresb  
    Kansas overall summary score, 0–100 55 (36, 73) (539) 
    Kansas clinical summary score, 0–100 54 (38, 74) (539) 
    SF–36 physical functioning, 0–100 30 (15, 50) (533) 
    SF–36 mental health, 0–100 75 (60, 90) (536) 
Predictor variables n 
Gender  
    Male 293 
    Female 249 
Baseline NYHA  
    Ia and II 329 
    III and IV 211 
Depression as measured by the GDS-5  
    No evidence of depression 287 
    Evidence of depression 254 
Age groups  
    60–64 45 
    65–69 63 
    70–74 117 
    75–79 104 
    80–84 125 
    85+ 88 
Socioeconomic status  
    SE I and II 186 
    SE III–V 356 
Co-morbidities  
    0 and 1 168 
    2 162 
    3 115 
    4+ 97 
Marital status  
    Married 297 
    Widowed, divorced, single 245 
Household  
    Living alone 194 
    Living with others 348 
Quality-of-life outcome measuresb  
    Kansas overall summary score, 0–100 55 (36, 73) (539) 
    Kansas clinical summary score, 0–100 54 (38, 74) (539) 
    SF–36 physical functioning, 0–100 30 (15, 50) (533) 
    SF–36 mental health, 0–100 75 (60, 90) (536) 

GDS-5, Geriatric Depression Scale (5 item); NYHA, New York Heart Association.

a

This includes 20 participants who were NYHA stage I for reasons explained in the methods section.

b

Values are expressed as median (Q1, Q2) (n).

Results

Table 1 summarises participant characteristics, which were used as predictor variables and outcome scores. As can be seen, 54% of participants were males and 40% were classified as being in NYHA class III and IV. The majority were aged >70 years, with 39.3% aged >80 years; 55% were married and 46% lived alone. Just over half showed signs of depression and 69% were experiencing two or more co-morbidities in addition to their heart failure.

Factors predictive of quality of life at baseline

Table 2 shows the quality-of-life outcomes which were associated with each predictor variable at the 5% level of significance, when controlling for all potential confounders.

Table 2.

Participant characteristics and quality-of-life outcomes

  Difference in quality of life associated with one-point increase in scale scores
 
  
Predictors Associated quality-of-life outcomes Estimate (SE) 95% CI P-value 
Female Kansas overall summary score −3.3 (1.38) −6.0, –0.6 0.016 
 Kansas clinical summary score −6.2 (1.44) −9.0, –3.4 <0.001 
 SF-36 physical functioning scale −7.2 (1.98) −11.1, –3.3 <0.001 
High baseline NYHA (III–IV) Kansas overall summary score −23.1 (1.42) −26.0, –20.4 <0.001 
 Kansas clinical summary score −22.1 (1.50) −25.0, –19.2 <0.001 
 SF-36 physical functioning scale −14.1 (2.07) −18.1, –10.0 <0.001 
 SF-36 mental health scale −7.1 (1.56) −10.1, –4.0 <0.001 
Evidence of depression Kansas overall summary score −15.6 (1.46) −18.5, –12.7 <0.001 
 Kansas clinical summary score −13.1 (1.53) −16.1, –10.1 <0.001 
 SF-36 physical functioning scale −10.2 (2.10) −14.3, –6.0 <0.001 
 SF-36 mental health scale −15.7 (1.59) −18.8, –12.6 <0.001 
Low socioeconomic groups (III–V) Kansas overall summary score −2.7 (1.4) −5.4, 0.1 0.057 
 SF-36 mental health scale −3.6 (1.5) −6.6, –0.6 0.017 
Age groupa     
    65–69 SF-36 physical functioning scale 3.3 (4.16) −4.9, 11.4 <0.001 
    70–74  −3.2 (3.76) −10.6, 4.2  
    75–79  −9.4 (3.81) −16.9, –1.9  
    80–84  −9.1 (3.72) −16.4, –1.8  
    85+  −12.1 (3.95) −19.9, –4.3  
    65–69 SF-36 mental health scale 1.7 (3.17) −4.5, 8.0 0.040 
    70–74  1.9 (2.84) −3.7, 7.5  
    75–79  0.2 (2.89) −5.5, 5.8  
    80–84  5.0 (2.81) −0.5, 10.5  
    85+  6.7 (2.98) 0.8, 12.5  
Co-morbiditiesb     
    2 Kansas overall summary score −4.2 (1.67) −7.5, –0.9 <0.001 
    3  −5.9 (1.89) −9.7, –2.2  
    4+  −9.6 (2.04) −13.6, –5.6  
    2 Kansas clinical summary score −5.6 (1.75) −9.0, –2.2 <0.001 
    3  −9.6 (1.98) −13.5, –5.7  
    4+  −11.7 (2.14) −15.9, –7.5  
    2 SF-36 physical functioning scale −1.7 (2.40) −6.4, 3.1 <0.001 
    3  −8.5 (2.73) −13.9, –3.2  
    4+  −10.8 (2.94) −16.6, –5.0  
    2 SF-36 mental health scale −0.0 (1.82) −3.6, 3.6 <0.001 
    3  −0.7 (2.06) −4.7, 3.4  
    4+  −9.0 (2.22) −13.4, –4.7  
  Difference in quality of life associated with one-point increase in scale scores
 
  
Predictors Associated quality-of-life outcomes Estimate (SE) 95% CI P-value 
Female Kansas overall summary score −3.3 (1.38) −6.0, –0.6 0.016 
 Kansas clinical summary score −6.2 (1.44) −9.0, –3.4 <0.001 
 SF-36 physical functioning scale −7.2 (1.98) −11.1, –3.3 <0.001 
High baseline NYHA (III–IV) Kansas overall summary score −23.1 (1.42) −26.0, –20.4 <0.001 
 Kansas clinical summary score −22.1 (1.50) −25.0, –19.2 <0.001 
 SF-36 physical functioning scale −14.1 (2.07) −18.1, –10.0 <0.001 
 SF-36 mental health scale −7.1 (1.56) −10.1, –4.0 <0.001 
Evidence of depression Kansas overall summary score −15.6 (1.46) −18.5, –12.7 <0.001 
 Kansas clinical summary score −13.1 (1.53) −16.1, –10.1 <0.001 
 SF-36 physical functioning scale −10.2 (2.10) −14.3, –6.0 <0.001 
 SF-36 mental health scale −15.7 (1.59) −18.8, –12.6 <0.001 
Low socioeconomic groups (III–V) Kansas overall summary score −2.7 (1.4) −5.4, 0.1 0.057 
 SF-36 mental health scale −3.6 (1.5) −6.6, –0.6 0.017 
Age groupa     
    65–69 SF-36 physical functioning scale 3.3 (4.16) −4.9, 11.4 <0.001 
    70–74  −3.2 (3.76) −10.6, 4.2  
    75–79  −9.4 (3.81) −16.9, –1.9  
    80–84  −9.1 (3.72) −16.4, –1.8  
    85+  −12.1 (3.95) −19.9, –4.3  
    65–69 SF-36 mental health scale 1.7 (3.17) −4.5, 8.0 0.040 
    70–74  1.9 (2.84) −3.7, 7.5  
    75–79  0.2 (2.89) −5.5, 5.8  
    80–84  5.0 (2.81) −0.5, 10.5  
    85+  6.7 (2.98) 0.8, 12.5  
Co-morbiditiesb     
    2 Kansas overall summary score −4.2 (1.67) −7.5, –0.9 <0.001 
    3  −5.9 (1.89) −9.7, –2.2  
    4+  −9.6 (2.04) −13.6, –5.6  
    2 Kansas clinical summary score −5.6 (1.75) −9.0, –2.2 <0.001 
    3  −9.6 (1.98) −13.5, –5.7  
    4+  −11.7 (2.14) −15.9, –7.5  
    2 SF-36 physical functioning scale −1.7 (2.40) −6.4, 3.1 <0.001 
    3  −8.5 (2.73) −13.9, –3.2  
    4+  −10.8 (2.94) −16.6, –5.0  
    2 SF-36 mental health scale −0.0 (1.82) −3.6, 3.6 <0.001 
    3  −0.7 (2.06) −4.7, 3.4  
    4+  −9.0 (2.22) −13.4, –4.7  

NYHA, New York Heart Association.

a

In relation to <65 years.

b

In relation to 0 and 1.

Gender

Gender showed a significant association with all quality-of-life outcomes, apart from the SF-36 mental health scale. For example, women had an estimated 6.2 point lower score on the Kansas clinical summary score (95% CI 3.4, 9.0).

Baseline NYHA

NYHA score at baseline was significantly associated with all outcomes. For example, people reporting NYHA score 3 or 4 at baseline had an estimated 23.1 point lower score on the Kansas overall summary score than those reporting NYHA score 2 (95% CI 20.4, 26.0).

Evidence of depression

Evidence of depression (as measured by the GDS-5) was significantly associated with all outcomes. For example, people reporting depression scored an estimated 15.6 points lower on the Kansas overall summary score (95% CI 12.7, 18.5).

Socioeconomic group

Socioeconomic group was significantly associated with the Kansas overall summary score and the SF-36 mental health scale but not with the SF-36 physical functioning scale. For example, people from socioeconomic groups III–V reported a 3.6 point lower score on the SF-36 mental health scale when compared with those from groups I and II (95% CI 0.6, 6.6).

Age group

Age group was not significantly associated with quality of life as measured by the Kansas overall summary score or the Kansas clinical summary score. However, significant associations were identified for the SF-36 physical and mental functioning scales. For example, people aged >85 years reported a 12.1 point lower score on the SF-36 physical functioning scale when compared with those <65 years (95% CI 4.3, 19.9).

Co-morbidities

Number of co-morbidities showed a significant association with all outcomes. For example, people reporting four or more co-morbidities had an estimated 11.7 point lower score on the Kansas clinical summary score (95% CI) than those with 0 or 1.

Discussion

Maximising quality of life for older people with heart failure has been identified as a key challenge for clinicians involved in the care of older people [1]. However, more information about this patient group is needed in order for this to be achieved. Both older people and people living in the community have been excluded from most heart failure research [11]. The current study provides new data about factors predictive of the quality of life of older people with heart failure recruited from community settings.

The following factors were identified as being independently associated with reduced quality of life amongst heart failure patients >60 years: being female, having a higher NYHA score, showing evidence of depression, being in socioeconomic groups III–V and having two or more co‐morbid conditions. Increasing age was associated with poorer generic quality of life but not with heart failure-specific quality of life.

Being female was associated with reduced scores on all measures of quality of life apart from the SF-36 mental health scale. As noted in the introduction, past research has proved inconclusive regarding the relationship between gender and quality of life, although some studies have identified lower quality of life among women [19, 21–24]. However, this research has typically been conducted with clinical trial samples in which women and older people are underrepresented or excluded. The findings from this study support the conclusion that heart failure does impact more upon quality of life for older women than for older men and that this relationship is independent of marital status and age.

That NYHA functional class was associated with quality of life confirms previous research which has found associations between higher NYHA scores and reduced quality of life [12, 14–18]. Rates of depression as identified by the GDS-5 were high, with just over half of the patients showing evidence of depression. However, this prevalence is similar to that reported in a recent study of hospital outpatients who were also in NYHA stages II–IV [30]. Only 11.3% of participants reported depression as a co-morbidity, which is likely to reflect the well-known under-treatment of depression within the older population, as well as the fact that a questionnaire self-report measure is no substitute for a clinical diagnosis. Depression was predictive of reduced quality of life, which again has been reported in previous studies [19, 20]. No previous research could be identified that examined the association between quality of life in heart failure and socioeconomic status, although education level was found to be predictive of quality of life in one study conducted with an older Chinese sample [18]. Indeed, education level may be contributing to the association identified in this study, given its close association with socioeconomic status.

Previous research has identified older age as predictive of better quality of life, although these results have been identified in heart failure samples of all ages [18, 24, 26] or samples aged >50 years [25]. This study identified that, within a sample of people aged >60 years with heart failure, increasing age was associated with poorer general quality of life but not with heart failure-specific quality of life. This finding lends support to the conclusion that age-related quality-of-life changes, including general frailty, are likely to compound older people’s experience of quality-of-life changes resulting from heart failure. It also highlights the importance of using both generic and disease-specific measures to assess the quality of life of older people with heart failure.

People reporting four or more co-morbidities were particularly at risk for reduced quality of life. This finding does not correspond to previous research, although, again, other study samples were unrepresentative of an older community population with heart failure [18, 25]. Indeed, previous research has identified co-morbidities as predictive of hospitalisation amongst patients with heart failure [31], indicating that it is likely to influence quality of life. Given the high prevalence of co-morbidities experienced by older patients with heart failure, the need for multi-disciplinary care for older people living in the community is apparent. In particular, this study underscores the importance of cardiologists working together with geriatricians and primary care physicians by providing evidence that managing heart failure symptoms in isolation from consideration of co-morbid conditions is unlikely to lead to significant improvements in quality of life.

Despite providing important and novel data, certain study limitations must be acknowledged. First, participation rates were relatively low, particularly amongst the very old, women and those in the highest NYHA groups, reflecting the difficulties of trying to involve often very ill older people in research. The conclusions drawn must therefore be considered within the context of the underrepresentation of these groups within the sample. Second, heart failure was not diagnosed according to objective criteria. However, this decision was made to reflect the situation in primary care where significant difficulties in making an ‘objective’ diagnosis [32] have been identified. Indeed, the difficulties of identifying patients with heart failure from primary care are well known. The European Society of Cardiology gives guidelines for diagnosing heart failure [33], but in practice these guidelines are not necessarily followed and general practitioners are often not confident in making the diagnosis, even when echocardiograms are available to them [32]. Furthermore, a high proportion of older patients receive no investigations for heart failure [34]. This method may have resulted in a small number of patients without heart failure to be incorrectly identified, but GPs checked the lists of patients produced by the search, and potential participants were screened for symptoms of breathlessness and agreed they had been diagnosed with a heart condition. Finally, while the Kansas City Cardiomyopathy Questionnaire has been used in a number of large international studies [35] and no evidence found of differences in performance between countries, a formal UK validation has not been undertaken.

Findings from the project suggest that quality of life for older people with heart failure can be described as challenging and difficult, particularly for women, those in a high NYHA class, patients showing evidence of depression, patients in socioeconomic groups III–V, those experiencing two or more co-morbidities and the ‘oldest old’. This information can help clinicians working with older people identify those at risk of reduced quality of life and target interventions appropriately.

Key points

  • Little is known about factors predictive of quality of life amongst older people with heart failure recruited from community settings.

  • Amongst 542 people aged >60 years with heart failure, the following factors were predictive of reduced quality of life: being female, evidence of depression, being in NYHA class III and IV, having two or more co-morbidities and being in socioeconomic groups III–V.

  • Older age was associated with reduced quality of life as measured by a generic quality-of-life tool (SF-36) but not by a disease-specific quality-of-life tool (Kansas City Cardiomyopathy Questionnaire).

Funding

Department of Health (programme to support research to support the National Service Framework for Older People).

Conflicts of interest

None.

Acknowledgements

We thank all GP practices and participants involved in the study.

References

1.
Jones
AM
, O’Connell JE, Gray CS. Living and dying with congestive heart failure: addressing the needs of older congestive heart failure patients.
Age Ageing
 
2003
;
32
:
566
–8.
2.
British Heart Foundation Statistics Database. http://www.heartstats.org (May 2005).
3.
Department of Health. National Service Framework: Coronary Heart Disease, HMSO, London,
2000
.
4.
Murphy
NF
, Simpson CR, McAlister FA et al. National survey of the prevalence, incidence, primary care burden, and treatment of heart failure in Scotland.
Heart
 
2004
;
90
:
1129
–36.
5.
National Collaborating Centre for Chronic Conditions. Chronic heart failure. National clinical guideline for diagnosis and management in primary and secondary care. London: National Institute for Clinical Excellence (NICE),
2003
.
6.
Sanderson
S.
ACE inhibitors in the treatment of chronic heart failure: effective and cost-effective.
Bandolier
 
1994
; 1. http://www.jr2.ox.ac.uk/bandolier/band8/b8-1.html (21 December
2005
, date last accessed).
7.
Rector
TS
, Tschumperlin LK, Kubo SH et al. Use of the living with heart failure questionnaire to ascertain patients’ perspectives on improvement in quality of life versus risk of drug-induced death.
J Card Fail
 
1995
;
1
:
201
–6.
8.
Bennett
SJ
, Pressler ML, Hays L, Firestine LA, Huster GA. Psychosocial variables and hospitalization in persons with chronic heart failure.
Prog Cardiovasc Nurs
 
1997
;
12
:
4
–11.
9.
Konstam
V
, Salem D, Pouler H et al. Baseline quality of life as a predictor of mortality and hospitalisation in
5
,025 patients with congestive heart failure.
Am J Cardiol
 
1996
;
78
:
890
–5.
10.
Alla
F
, Briancon S, Guillemin F et al. Self-rated quality of life provides additional prognostic information in heart failure. Insights into the EPICAL study.
Eur J Heart Fail
 
2002
;
4
:
337
–43.
11.
Sharpe
N
, Doughty R. Epidemiology of heart failure and ventricular dysfunction.
Lancet
 
1998
; 352 (Suppl.
1
):
3
–7.
12.
Hobbs
FDR
, Kenkre JE, Roalfe AK, Davis RC, Hare R, Davies MK. Impact of heart failure and left ventricular systolic dysfunction on quality of life.
Eur Heart J
 
2002
;
23
:
1867
–76.
13.
Bennett
SJ
, Oldridge NB, Eckert GJ et al. Comparison of quality of life measures in heart failure.
Nurs Res
 
2003
;
53
:
207
–16.
14.
Carels
RA.
The association between disease severity, functional status, depression and daily quality of life in congestive heart failure patients.
Qual Life Res
 
2004
;
13
:
63
–72.
15.
Sullival
M
, Levy WC, Russo JE, Spertus JA. Depression and health status in patients with advanced heart failure: a prospective study in tertiary care.
J Card Fail
 
2004
;
10
:
390
–6.
16.
Yu
DS
, Lee DT, Woo J. Health-related quality of life in elderly Chinese patients with heart failure.
Res Nurs Health
 
2004
;
27
:
332
–44.
17.
Jeunger
J
, Schellberg D, Kramer S, Haunstetter A, Zugck C, Herzog W, Haass M. Health related quality of life in patients with congestive heart failure: comparison with other chronic diseases and relation to functional variables.
Heart
 
2002
;
87
:
235
–41.
18.
Riedinger
MS
, Dracup KA, Brecht ML. Predictors of quality of life in women with heart failure.
J Heart Lung Transplant
 
2000
;
19
:
598
–608.
19.
Cline
CM
, Willenheimer RB, Erhardt LR, Wiklund I, Israelsson BY. Health-related quality of life in elderly patients with heart failure.
Scand Cardiovasc J
 
1999
;
33
:
278
–85.
20.
Rumsfield
JS
, Havranek E, Masoudi FA et al. Depressive symptoms are the strongest predictors of short-term declines in health status in patients with heart failure.
J Am Coll Cardiol
 ;
42
:
1811
–7.
21.
Riegel
B
, Moser DK, Carlson B et al. Gender differences in quality of life are minimal in patients with heart failure.
J Card Fail
 
2003
;
9
:
42
–8.
22.
Friedman
M.
Gender differences in the health related quality of life of older adults with heart failure.
Heart Lung
 
2003
;
32
:
320
–7.
23.
Stomberg
A
, Martensson J. Gender differences in patients with heart failure.
Eur J Cardiovasc Nurs
 
2003
;
2
:
7
–18.
24.
Hou
N
, Chui MA, Eckert GJ, Oldridge NB, Murray MD, Bennett SJ. Relationship of age and sex to health-related quality of life in patients with heart failure.
Am J Crit Care
 
2004
; 13 (
2
):
153
–61.
25.
Clark
DO
, Wu W, Weiner M, Murray M. Correlates of health-related quality of life among lower-income, urban adults with congestive heart failure.
Heart Lung
 
2003
;
32
:
391
–401.
26.
Masoudi
FA
, Rumlsfield JS, Havranek EP et al. Age, functional capacity, and health-related quality of life in patients with heart failure.
J Card Fail
 
2004
;
10
:
368
–73.
27.
Seamark
DA
, Ryan M, Smallwood N, Gilbert J. Deaths from heart failure in general practice: implications for palliative care.
Palliat Med
 
2002
;
16
:
495
–8.
28.
Green
CP
, Porter CB, Bresnahan DR, Spertus JA. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure.
J Am Coll Cardiol
 
2000
;
35
:
1245
–55.
29.
Rinaldi
P
, Mecocci P, Benedetti C et al. Validation of the five-item geriatric depression scale in elderly patients in three different settings.
J Am Geriatr Soc
 
2003
;
51
:
694
–8.
30.
Gottlieb
SS
, Khatta M, Friedmann E et al. The influence of age, gender, and race on the prevalence of depression in heart failure patients.
J Am Coll Cardiol
 
2004
;
43
:
1542
–9.
31.
Brown
AM
, Cleland JGF. Influence of concomitant disease on patterns of hospitalization in patients with heart failure discharged from Scottish hospitals in
1995
.
Eur Heart J
  1998;
19
:
1063
–9.
32.
Ahmet
F
, Hungin APS, Murphy JJ. Barriers to accurate diagnosis and effective management of heart failure in primary care: qualitative study.
BMJ
 
2003
;
326
:
196
–201.
33.
Cleland
JGF
, Erdmann E, Ferrari R et al. Guidelines for the diagnosis of heart failure.
Eur Heart J
 
1995
;
16
:
741
–51.
34.
Hood
S
, Taylor S, Roeves A, Crook AM, Cohen J, Kaddoura S et al. Are there age and sex difference in the investigation and treatment of heart failure? A population-based study.
Br J Gen Prac
 
2000
;
50
:
559
–63.
35.
Soto
GE
, Jones P, Weintrub WS, Krumholz HM, Spertus JA. Prognostic value of health status in patients with heart failure after acute myocardial infarction.
Circulation
 
2004
;
110
:
546
–51.

Comments

0 Comments