Depression and perceptions about heart failure predict quality of life in patients with advanced heart failure

Depression and perceptions about heart failure predict quality of life in patients with advanced heart failure

Care of Patients with Chronic Heart Failure Depression and perceptions about heart failure predict quality of life in patients with advanced heart fa...

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Care of Patients with Chronic Heart Failure

Depression and perceptions about heart failure predict quality of life in patients with advanced heart failure Claire N. Hallas, PhD, BSca,*, Jo Wray, PhD, BSca, Panayiota Andreou, MSc, BScb, Nicholas R. Banner, MDa a

Royal Brompton & Harefield National Health Service Trust, Harefield, Middlesex, United Kingdom b Department of Psychology, University of Bath, Bath, United Kingdom

article info

abstract

Article history: Received 18 April 2009 Revised 13 December 2009 Accepted 22 December 2009 Online 8 April 2010

Background: Mood is an independent predictor of mortality and quality of life (QoL) for people with heart failure. However, the underlying belief systems involved in mood are unknown.

Keywords: Illness beliefs Depression Anxiety Quality of life Heart failure

Objective: We sought to identify psychological and clinical variables predicting mood and QoL for people diagnosed with heart failure (HF). Methods: One hundred and forty-six HF patients were assessed with standardized measures, to determine their beliefs about HF, coping styles, mood, and QoL. Results: Patients with more negative beliefs about the consequences of HF and with less perceived control over symptoms showed maladaptive coping styles such as denial and behavioral disengagement, and more severe levels of depression and anxiety. Depression also independently predicted QoL outcomes. Conclusions: Anxious and depressed patients have more negative beliefs about HF, leading to negative coping behaviors and poor QoL. Our evidence suggests that changing negative beliefs may improve the psychological well-being and QoL of patients, irrespective of disease severity. Cite this article: Hallas, C. N., Wray, J., Andreou, P., & Banner, N. R. (2011, MARCH/APRIL). Depression and perceptions about heart failure predict quality of life in patients with advanced heart failure. Heart & Lung, 40(2), 111-121. doi:10.1016/j.hrtlng.2009.12.008.

A large proportion of patients with advanced heart failure report poor quality of life (QoL) and significant emotional distress. Coping with debilitating symptoms such as breathlessness, fatigue, poor exercise capacity, nausea, and sleeping difficulties often leads

to depression, anxiety, panic, and low self-esteem. Recent evidence suggests that between 25% and 50% of heart-failure patients self-report anxiety, and 18% to 47% self-report depression, depending on their age, length of diagnosis, and comorbid conditions.1-4

* Corresponding author: Claire N. Hallas, PhD, BSc, now at the Department of Behavioral Medicine, College of Medicine and Health Sciences, Sultan Qaboos University, PO Box 35, Al Khoud, Postal Code 123, Muscat, Oman. E-mail address: [email protected] (C. N. Hallas). 0147-9563/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.hrtlng.2009.12.008

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Patients with negative mood states also report poorer QoL and greater functional impairment than nondepressed patients, even compared with patients of a higher New York Heart Association class, suggesting that interpretations of function, rather than clinical status, predict QoL outcomes.5-8 Although depression is generally less prevalent than anxiety, it exhibits a significant cardiotoxic effect on prognosis, morbidity, and mortality in heart-failure patients.9,10 For example, depressed individuals run an increased risk of mortality (36% vs. 16%) and an 87% increase in hospital readmissions (vs. 74%) compared with nondepressed individuals after controlling for baseline cardiac function,11,12 neuroticism, gender, and age.13 Furthermore, depressed patients with heart failure are twice as likely to suffer premature death, compared with their nondepressed peers, and their allcause mortality amounts to 12%, compared with 9% for nondepressed patients.14,15 Evidence indicates that depressed patients also have 4 pathophysiological changes that correspond to the pathogenesis of heart failure: neurohormonal activation, hypercoagulability, autonomic neurocardiac dysfunction, and cytokine release.16-18 These pathophysiological changes are thought to mediate the increased risk for cardiac events in people who are depressed, and their subsequent poor prognosis if such events occur.15,19 Developing an understanding of the etiology of mood disorders, and particularly depression, within the heart-failure population is therefore critical. A recent review20 concluded that psychosocial factors either promote health by moderating pathological processes, or promote heart failure by enhancing those processes. Depression, anxiety, and life stress diminish health, whereas social support and inclusion promote health, and subsequently mediate biological processes. Recent studies indicate that the coping styles of nondepressed and non-anxious heart-failure patients are more active (planning, problem-solving, using social support, positive thinking, and distraction), and that such patients have greater belief in their self-control and efficacy over their health management.2,21,22 On the other hand, anxious and depressed patients use maladaptive styles such as avoidance, behavioral disengagement, and denial23,24 as a consequence of believing negative outcomes about living with heart failure (e.g., the consequences of heart failure, the controllability of symptoms, and the cause of their illness25). More recent studies demonstrated the role of illness beliefs as a mediator of coping and mood disorders in heart-failure and other cardiac populations. Negative perceptions about perceived control over their illness and the consequential outcomes were related to impaired recovery, nonadherence to medications, greater anxiety and depression, and decreased satisfaction with QoL.26-29 To measure these beliefs, studies use the Illness Perceptions Questionnaire, derived from the self-regulatory model of chronic illness,30 which categorizes 9 key illness beliefs (symptoms’ identity, cause, illness coherence, personal and treatment

control, timeline of acute-chronic and cyclical, consequences, and the emotional representation of illness) that affect coping and emotional responses, along with a feedback system ultimately influencing behavioral and QoL outcomes (Figure 1). However, to date, studies of heart-failure populations have not measured the direct contributions of these underlying illness beliefs to affective (mood) and QoL outcomes. Our objective was to measure the significance of heart-failure (HF) beliefs and their relationship to coping behaviors, anxiety and depression, and generic and health-related QoL outcomes. We used the selfregulatory model as a basis for measuring illness beliefs and developing our study hypotheses:

(1) Anxious and depressed patients will exhibit significantly more negative beliefs about HF, compared with nonanxious and nondepressed patients; (2) Anxious and depressed patients will exhibit significantly more negative coping styles, compared with nondepressed and nonanxious patients; (3) Depression will be the greatest significant predictor of QoL outcomes; and (4) Clinical measures of cardiac function will not significantly predict QoL or affective outcomes.

Methods Design and Sample A cross-sectional cohort study was conducted, collating standardized, psychometric questionnaires, and medical information. Patients were recruited from an outpatient tertiary cardiothoracic hospital clinic in the United Kingdom. Patients were excluded if they were not able or willing to give informed, written consent or were unable to comprehend English well enough to complete tests and were below age 18 years (nonparticipants were not significantly different from participants in terms of left-ventricular ejection fraction, peak oxygen uptake on treadmill testing, duration of heart failure, and number of medications taken). Two hundred and eighty-four patients were identified as eligible for inclusion, of whom 146 patients gave informed consent to participate and completed the study questionnaires, for a response rate of 51% (see Table 1 for patient demographics).

Procedure Ethics approval for the study was given by the United Kingdom Central Office for Research Ethics Committee,

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ILLNESS REPRESENTATION (beliefs) Label & Symptoms Time-line (acute/chronic & cyclical) Controllability (personal & treatment) Cause (internal/external) Consequences (e.g. pain, death) Illness coherence (understanding) Emotional representations (affect)

COPING APPRAISALS & BEHAVIOURS

THE SELF

THREAT! Symptoms Event – e.g. heart attack Treatment effects

age, personal, social & environmental experiences

Illness beliefs Affective states

OUTCOME APPRAISALS Well Stressed Ill

EMOTIONAL (affective) STATES Anxiety Depression Panic; Fear Anger Rumination

Figure 1 e Common-sense model of chronic illness. The self-regulatory model is a parallel processing model whereby cognitions (i.e., beliefs) and emotions are interrelated and feed back to moderate coping appraisals, mood, and outcomes.

Table 1 e Patient demographics Number of participants Age (y) (mean  SD) Diagnosis (y) (mean  SD) Gender Married or living as married Employed Ethnicity Comorbid conditions* Dilated cardiomyopathy Nonischemic heart disease Congenital condition LVEF (mean  SD) MVO2 (mean  SD)

146 48.6 (SD, 9.45) 5.1 (SD, 6.2) 120 (82%) male 102 (70%) 31 (21%) 129 (88%) Caucasian 45 (30%) 54 (40%) 85 (58%) 3 (2%) 38.2% (SD, 15.1) 14.8 mL/min/kg (SD, 5.2)

LVEF, left-ventricular ejection fraction; MVO2, mean peak oxygen uptake. * Diabetes, asthma, and cerebral ischemic attack.

and eligible patients were identified at outpatient clinics. All patients received the questionnaire, together with an information sheet and a consent form, and were required to give informed, written consent to participate. All questionnaires were returned in a reply-paid envelope to the study coordinator.

Psychological Measures

generated to enhance its psychometric properties. The questionnaire contains 9 subscales, and 69 items in total measure perceptions of HF identity (symptoms), consequences, timeline-acute/chronic, timelinecyclical, emotional representations of HF, illness coherence (understanding), treatment control, personal control, and causes. The items are rated on a 5-point Likert scale except for the identity subscale, which is rated as a yes/no response. This subscale can be amended to reflect variations in symptoms within study samples, and so the item “sore eyes” was replaced with “shortness of breath.” Cronbach’s a values ranged between .82 and .93.

Hospital Anxiety and Depression (HADS) The Hospital Anxiety and Depression (HADS) questionnaire contains 14 items, consisting of 2 subscales (7 items each) that measure anxiety and depression. Scores of 8 to 10 on the anxiety and depression subscales indicate a borderline clinical status, and scores of 11 are regarded as psychiatric (or case) levels of anxiety and depression, which were shown as comparable to clinical diagnostic interviews.33 A review of over 700 HADS studies34 supports its 2-factor structure (mean Cronbach’s a, .83 for the anxiety subscale, and .82 for depression) and validity with HF populations.35 In our study, Cronbach’s a for the anxiety subscale was .85, and for the depression scale, it was .83.

Illness Perceptions Questionnaire-Revised The initial Illness Perceptions Questionnaire-Revised (IPQ-R)31 was derived from the self-regulatory model of chronic illness,30 and the revised version32 was

COPE This 64-item questionnaire36 measures positive and negative coping strategies. Its 15 subscales are based

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on summing individual items measured on a 5-point Likert scale. The COPE was widely used within various chronically ill populations, and has documented reliability and validity.37 Cronbach’s a values ranged between .84 and .91.

World Health Organization Quality of Life Brief Assessment Generic QoL was measured with the 30-item World Health Organization Quality of Life Brief Assessment (WHOQOL-BREF),38 which is an abbreviated version of the WHOQOL-100 questionnaire. For the purpose of our study, only the 4 main domains of WHOQOL were assessed: physical, psychological, social, and environmental QoL. Scores can range from 4 to 20, with higher scores indicating greater QoL. Cronbach’s a values ranged between .81 and .87, except for the social QoL scale (Cronbach’s a, .52).

Minnesota Living With Heart Failure Questionnaire Heart failure-specific QoL was assessed using the 21-item Minnesota Living With Heart Failure Questionnaire (MLHF).39 It focuses on the effects of HF on physical, psychological, and socioeconomic aspects of patients’ lives. Each item is scored on a 6-point scale. Assessment is quantified by summing the scores (higher scores indicate greater impairment). Cronbach’s a was .84.

Clinical Data Collection Information on the severity of participants’ HF and functional capacity was collected from medical records and clinical databases. Severity of HF was assessed using the measurement of left-ventricular ejection fraction (LVEF), according to either multiple gated acquisition scan or echocardiography (n ¼ 130). Cardiopulmonary treadmill exercise test results (using the modified protocol of Bruce40) were used as an index of functional capacity, as measured using mean peak oxygen uptake (MVO2) (n ¼ 116). The multiple gated acquisition scan, echo, and exercise tests were routinely requested by the consultant cardiologist at patients’ outpatient appointments. The test results closest in time to the administration of the questionnaire were used in our study, to maximize their relevance to current function (no greater than 8 weeks from that date).

Statistical Analyses Statistical analyses were performed using the SPSS for Windows (version 15.0, SPSS, Inc., Chicago, IL). Relationships between normally distributed variables were assessed using the Pearson correlation coefficient and Spearman r correlations (for nonparametric data). Diagnostic group differences in HADS data were assessed using 1-way analyses of variance (ANOVAs). Hierarchical linear stepwise regression analyses were performed to evaluate relationships between

Table 2 e Means and standard deviations for questionnaire subscales and total scores Questionnaire scale Mean (SD) WHOQOL physical subscale WHOQOL psychological subscale WHOQOL social subscale WHOQOL environmental subscale WHOQOL global score MLHF total score IPQ identity scale IPQ timeline, chronic/acute IPQ-R timeline, cyclical IPQ-R consequences IPQ-R personal control IPQ-R treatment control IPQ-R emotional representations IPQ-R illness coherence HADS anxiety HADS depression

10.76 (2.50) 12.48 (2.59) 12.68 (3.40) 13.40 (2.79) 13.40 (2.73) 56.93 (26.46) 6.98 (3.19) 26.23 (3.74) 11.60 (3.96) 24.81 (4.13) 18.71 (5.05) 16.80 (3.75) 20.36 (6.03) 19.02 (5.17) 8.43 (4.82) 7.58 (4.33)

WHOQOL, World Health Organization Quality of Life Brief Assessment Scale; MLHF, Minnesota Living With Heart Failure Questionnaire; IPQ-R, Illness Perception Questionnaire-Revised; HADS, Hospital Anxiety and Depression Scale.

independent and dependent variables (QoL and HADS). Our analyses involved stepwise hierarchical regressions, because our study was based on theoretically driven hypotheses. Variables were selected for the regression after correlational analyses between HADS, COPE, IPQ, QoL, and clinical measures, to determine where significant relationships existed. Only significant relationships were entered into each separate regression analysis (7 in total: HAD anxiety, HAD depression, WHOQOL social scale, WHOQOL psychological scale, WHOQOL physical scale, WHOQOL environmental scale, and MLHF scale). For anxiety and depression regression analyses, only COPE, IPQ-R, and clinical data were analyzed together. Variables for each regression analysis were entered in blocks (hierarchical levels), as organized and specified through theoretical selfregulatory model (SRM) systems. Therefore, clinical variables were entered into block 1 (related to personal/ environmental variables in the SRM), coping (and HADS for QoL analyses, as the emotional appraisal system in the SRM) was entered into block 2, and IPQ-R data were entered into block 3 (outcomes of the SRM). The significance level was set at .05 for all analyses, although where multiple correlations or comparisons occurred, the P value was adjusted by the Bonferroni correction test or reduced to .01, to lower the possibility of a type I error. Mean scores and standard deviations for scales are presented in Table 2.

Results Pearson correlations were performed to determine significant relationships between HF perceptions

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Table 3 e Pearson correlation coefficients between IPQ-R, COPE, and HADS HADA

HADD

HADA HADD ID TLC TLAC CONS PC TC IC ER

y

.669

ID

TLC y

.446 .330y

y

.295 .233y .341*

TLAC .112 .079 .331* .374y

CONS y

.377 .415y .153 .286 .455y

PC

TC y

.244 .354y .023 .098 .284 .207

IC y

.241 .199* .095 .070 .124 .479y .462y

.207* .117 .215 .369* .142 .089 .099 .223

ER

ESS y

.757 .565y .327* .210 .434y .372y .228 .295* .337*

.046 .107 .023 .153 .001 .019 .023 .060 .009 .141

VENT y

.503 .272y .153 .120 .023 .247* .108 .050 .016 .526y

DEN y

.368 .179 .114 .185 .041 .032 .118 .019 .272y .372y

BDIS .445y .351y .307y .169 .062 .024 .241* .315y .236* .293y

IPQ-R scales: ID, identity; TLC, timeline-cyclical; TLAC, timeline-acute/chronic; PC, personal control; TC, treatment control; IC, Illness coherence; ER, emotional representations; HADS scales: HADA, hospital anxiety subscale; HADD, hospital depression subscale. COPE scales: ESS, emotional social support; VENT, venting; DEN, denial; BDIS, behavioral disengagement. * Correlation is significant at the .05 level. y Correlation is significant at the .01 level.

(IPQ-R), mood state (HADS), coping behaviors (COPE), QoL domains (WHOQOL and MLHF), and clinical variables (MVO2 and LVEF). The MVO2 was significantly related to anxiety (r ¼ .229, P < .05), depression (r ¼ .358, P < .01), physical QoL (r ¼ .44, P < .01), psychological QoL (r ¼ .30, P < .01), social QoL (r ¼ .27, P < .05), environmental QoL (r ¼ .27, P < .02), and MLHF QoL (r ¼ .46, P < .01). The LVEF, time since diagnosis, and comorbid conditions were not related to HADS or QoL outcomes. Tables 3 and 4 present significant correlations.

QoL Analysis Five separate hierarchical linear stepwise regression analyses were undertaken for the 4 WHOQOL subscale domains and the MLHF scale. Significant variables in the analyses were identified through correlational analyses (Tables 3 and 4) and entered into 3 blocks relating to their relationship within the self-regulatory model: block 1, maximum MVO2; block 2, COPE and HADS; and block 3, IPQ-R variables. The IPQ-R emotional-representations subscale was not included, although it was significantly associated with QoL, insofar as it demonstrated high colinearity with HADS; it was more robust and appropriate to include HADS within the regression analyses. In all regression models, depression accounted for the greatest proportion of the predictive model (Table 5).

Mood Analyses Forty-six participants (32%) reported “psychiatric” or clinical case levels of depression (scores >11), and 44 participants (30%) reported “psychiatric” or clinical case levels of anxiety (scores >11). An additional 36 participants (25%) reported “borderline” clinical depression (scores of 8 to 10), and 38 participants (26%) reported “borderline” clinical anxiety (scores of 8 to 10). No significant differences in anxiety and depression

were evident for people with comorbid conditions, or any differences in anxiety and depression between ischemic disease and dilated cardiomyopathy diagnostic groups.

Comparing Illness Beliefs and Coping Between HADS Diagnostic Category Groups One-way ANOVAs were performed to determine significant differences in independent variables between patients categorized with “noncase” (scores <8), “borderline” (scores of 8 to 10), or “case” (scores 11) levels of anxiety and depression on the HADS scale. Depressed patients reported significantly more negative illness perceptions (IPQ identity, F(2,140) ¼ 15.94, P < .001; IPQ timeline cyclical, F(2,141) ¼ 3.27, P < .05; IPQ consequences, F(2,142) ¼ 7.95, p < 0.0011; and IPQ personal control, F(2,142) ¼ 8.22, P < .0001) and maladaptive coping styles (denial, F(2,98) ¼ 3.56, P < .05; behavioral disengagement, F(2,93) ¼ 8.43, P < .0001; and venting emotions, F(2,97) ¼ 4.61, P < .05) than nondepressed patients. Anxious patients reported significantly more negative illness perceptions (IPQ identity, F(2,140) ¼ 15.91, P < .0001; IPQ timeline cyclical, F(2,142) ¼ 10.22, P < .001; IPQ consequences, F(2,143) ¼ 8.45; IPQ personal control, F(2,143) ¼ 5.29, P < .01; and IPQ treatment control, F(2,142) ¼ 5.38, P < .05) and maladaptive coping styles (denial, F(2,98) ¼ 7.24, P < .001; behavioral disengagement, F(2,93) ¼ 9.04, P < .0001; and venting emotions, F(2,97) ¼ 8.23, P < .0001) than nonanxious patients. Post hoc analyses (Tukey-Kramer test for unequal sample sizes) demonstrated significant differences between noncase, borderline, and case patients for the majority of scales; see Table 6).

Depression Hierarchical stepwise regression analysis was conducted to identify predictors of depression (see Tables

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Table 4 e Pearson correlations between QoL measures and other variables HADA HADD PQOL PSQOL SQOL EQOL MLHF

y

.514 .579y .358y .356y .575y

y

.638 .716y .433y .591y .647y

ID

TLC y

.450 .260y .095 .322y .494y

TLAC CONS y

.294 .164* .101 .325y .361y

.130 .008 .035 .116 .041

y

.372 .332y .238y .397y .481y

PC

TC y

IC

.361 .124 .133 .353y .191* .246y .185* .164* .088 .357y .222y .361y .215y .081 .102

ER y

.374 .523y .230y .428y .490y

ESS

VENT

DEN

BDIS

.039 .041 .239* .157 .042

.241* .291y .260y .299y .263y

.107 .161 .204* .210* .114

.416y .492y .222* .465y .320y

IPQ-R scales: ID, identity; TLC, timeline-cyclical; TLAC, timeline-acute/chronic; PC, personal control; TC, treatment control; IC, illness coherence; ER, emotional representations; HADS: HADA, hospital anxiety subscale; HADD, hospital depression subscale. WHOQOL scales: PQOL, physical quality of life; PSQOL, psychological quality of life; SQOL, social quality of life; EQOL, emotional quality of life. MHLF, Minnesota Living With Heart Failure Questionnaire. * Correlation is significant at the .05 level. y Correlation is significant at the .01 level.

3 and 4 for correlational analyses conducted included in the regression models). Entry block 1 (maximum MVO2), block 2 (COPE subscales, i.e., venting emotions and behavioral disengagement), and block 3 (IPQ-R variables, i.e., timeline-cyclical, perceived consequences, personal control, treatment control, and illness identity) were entered into the analysis. The IPQ-R timeline-cyclical (b ¼ .29, R2 ¼ .12, P < .01) accounted for the most significant proportion of the model. The IPQ-R consequences (b ¼ .24, P < .01), personal control (b ¼ .22, P < .05), and COPE behavioral disengagement (b ¼ .20, R2 ¼ .10, P < .05) accounted for the remaining significant proportion of variance in the final model (R2 ¼ .48, P < .01).

Anxiety Block 1 (maximum MVO2), block 2 (COPE subscales, i.e., venting, behavioral disengagement, and denial), and block 3 (IPQ-R variables, i.e., timeline-cyclical, perceived consequences, personal control, treatment control, illness coherence, and illness identity) were entered into the analysis. Results indicated that the COPE subscale venting of emotions (b ¼ .32, R2 ¼ .22, P < .001) accounted for the most significant proportion of the model. Behavioral disengagement (b ¼ .21, P < .05) and illness identity (b ¼ .21, P < .005) each contributed 11% to the final model. The IPQ-R timeline-cyclical (b ¼ .19, P < .05) accounted for a small proportion of the variance in the final model (R2 ¼ .56, P < .001).

Discussion Our objective was to investigate psychological and clinical variables related to QoL and affective (mood) outcomes for patients diagnosed with advanced HF, with particular interest in defining the role of illness/HF beliefs. Our first hypothesis proposed that anxious and depressed patients would have significantly more negative beliefs about HF compared with nonanxious and nondepressed patients. This hypothesis was supported, because depressed and anxious

patients exhibited significantly more negative perceptions about their symptoms, the consequences of HF, and the controllability of their disease. Anxious patients, in addition, demonstrated less illness coherence (understanding), and perceived greater uncertainty regarding the timeline and fluctuation of HF symptoms and less treatment control. Depressed patients only differed significantly from borderline “case” or subthreshold levels of depressed patients by exhibiting more negative emotional representations (e.g., fear) of HF. The variance in depression scores was significantly and independently predicted by illness beliefs, and specifically by the negative cyclical timeline and consequences of HF beliefs. Our second hypothesis proposed that anxious and depressed patients would have significantly greater negative coping styles compared with nondepressed and nonanxious patients. This hypothesis was supported, because depressed and anxious patients exhibited more negative styles, such as venting of emotions and behavioral disengagement. Anxious patients also reported significantly greater levels of denial than did nonanxious patients. Borderline depressed and anxious patients differed from noncase patients by exhibiting more denial, disengagement, and venting of emotions. These 3 coping styles were most frequently and significantly related to depression and anxiety, and the venting of emotions significantly predicted the greatest variance in anxiety scores. However, coping styles did not predict depression scores. Our third hypothesis stated that depression would be the greatest significant predictor of QoL outcomes. This hypothesis was supported, because depression contributed the greatest variance to all models of generic QoL (WHOQOL) and health-related QoL (MLHF) outcomes. Negative coping styles contributed to smaller variances. Our final hypothesis predicted that clinical measures of cardiac function would not significantly predict QoL or affective outcomes. This was only partially supported, because MVO2 levels (and not LVEF results) were significantly related to QoL outcomes, and contributed to all models. Levels of MVO2 accounted for a significantly larger proportion of the variance after depression for physical QoL and

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Table 5 e Stepwise regression analyses for WHOQOL domains and MLHF Dependent variable Psychological QoL Final model: R2 ¼ .66 Model 1: D ¼ .08 Model 2: R2 D ¼ .45 Model 3: R2 D ¼ .07 Model 4: R2 D ¼ .03 Social QoL Final model: R2 ¼ .34 Model 1: D ¼ .08 Model 2: R2 D ¼ .11 Model 3: R2 D ¼ .05 Model 4: R2 D ¼ .07 Models 5 and 6: D ¼ .03 Environmental QoL Final model: R2 ¼ .45 Model 1: D ¼ .06 Model 2: R2 D ¼ .28 Model 3: R2 D ¼ .09 Model 4: R2 D ¼ .03 Physical QoL Final model: R2 ¼ .62 Model 1: D ¼ .21 Model 2: R2 D ¼ .33 Model 3: R2 D ¼ .02 Model 4: R2 D ¼ .05 MLHF Final model: R2 ¼ .64 Model 1: D ¼ .22 Model 2: R2 D ¼ .37 Model 3: R2 D ¼ .03 Model 4: R2 D ¼ .02

Independent variable

b

T

P

Maximum VO2 Depression COPE behavioral disengagement Anxiety

.07 .45 .19 .24

1.06 5.05 2.59 2.58

ns P < .001 P < .05 P < .05

Maximum VO2 Depression COPE seeking emotional support COPE venting emotions IPQ-R treatment control

.22 .15 .35 .293 .189

2.30 1.49 3.53 2.94 2.07

P < .05 ns P < .001 P < .01 P < .05

Maximum VO2 Depression COPE behavioral disengagement IPQ-R consequences

.00 .36 .37 .19

.07 3.67 4.12 2.17

ns P < .01 P < .001 P < .05

Maximum VO2 Depression Anxiety IPQ-R illness identity

.25 .43 .10 .27

3.51 4.65 1.06 3.42

P < .01 P < .001 ns P < .01

Maximum VO2 Depression Anxiety IPQ-R illness identity

.24 .45 .17 .19

3.59 5.01 1.87 2.58

P < .01 P < .001 ns P < .05

ns, no significance.

MLHF outcomes, indicating that functional capacity was more successful in predicting physical QoL outcomes compared with LVEF, which is a measure of HF severity. Our results support the conclusions of previous research that depression is the predominant factor in QoL, and that cardiac function measured by MVO2 (demonstrating exercise capacity) exerts only a partial impact on outcomes.41 These results are consistent with previous studies indicating that perceived control, maladaptive coping strategies, and uncertainty are all important factors that combine to predict poor QoL outcomes and depression in cardiac populations.42-46 Our study, however, highlighted the role of other significant illness beliefs, not previously indicated, that are associated with both anxiety and depression and QoL outcomes. Negative emotional representations of HF (e.g., fear or anger), perceptions of reduced control over treatment with perceptions of negative consequences of HF (e.g., psychological, financial, and social), and perceptions of a more uncertain, fluctuating timeline for living with HF were also significant beliefs associated with the severity of depressed mood. These data also fit into existing knowledge on the psychological models of anxiety47 and depression.48,49 Both models indicate that negative life events (or

critical incidents such as illness episodes) can activate previously held negative beliefs or assumptions that were learned or developed through previous life experiences. These beliefs shape patients’ appraisals of a health threat and of whether they have the psychological resources to cope with the challenge. Individuals who are prone to the activation of negative belief systems are those with negative personality types. In particular, a type D (i.e., distressed) personality (a tendency toward negative affectivity and inhibition of self-expression in social situations) was shown to result in “toxic” outcomes. The type D personality was shown to be an independent significant predictor of total cardiac mortality, adjusting for sociodemographics and disease severity (LVEF) in chronic HF patients.50,51 Studies indicate that type D patients demonstrate a significant reduction in their endothelial progenitor cells (CD34þ/KDRþ), with prognostic results similar to those of clinically depressed patients. Therefore, patients with a tendency toward premorbid negative beliefs and coping styles mediated via a distressed personality, or with low positive affect, are more likely to develop depression and hold negative illness beliefs after a diagnosis of HF.52-54 The perceptual and conceptual processes that interact to produce anxious thinking are developed

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Table 6 e One-way ANOVAs comparing IPQ and COPE data according to HADS diagnostic groups Subscale

HADS depression HADS anxiety NC

IPQ-R identity IPQ-R timeline-cyclical IPQ-R consequences IPQ-R personal control IPQ-R treatment control IPQ-R illness coherence IPQ-R emotional representations COPE venting emotions COPE denial COPE behavioral disengagement

6.51 (2.98)* 11.11 (3.85) 23.69 (3.81)* 20.47 (4.51)* 17.72 (2.99) 19.96 (4.27) 17.63 (4.42)* 8.07 (2.66)* 5.80 (2.24) 5.80 (2.16)*

BL 7.97 (2.76) 12.95 (2.93)y 25.22 (3.09) 18.71 (4.19) 16.44 (2.98) 17.88 (3.72) 21.03 (4.88)y 8.22 (2.71) 7.48 (3.20)y 7.70 (2.77)y

C 8.15 (2.96) 12.34 (3.98) 26.40 (2.70) 16.69 (5.17) 16.50 (4.09) 18.65 (5.68) 24.09 (4.67)z 10.14 (2.96) 6.32 (2.48) 8.09 (2.93)

NC 5.82 (2.63)* 10.32 (3.22)* 23.62 (4.01)* 20.62 (4.64)* 17.85 (3.28)* 20.52 (3.54)* 16.11 (3.56)* 7.61 (2.41)* 5.31 (1.87)* 5.75 (2.10)*

BL

C y

7.81 (2.44) 13.13 (3.33)y 24.43 (3.27) 18.16 (3.88)y 17.53 (3.02) 18.13 (4.64)y 21.19 (3.80) 8.28 (2.70) 6.85 (2.83) 6.85 (2.41)

8.80 (3.18) 12.89 (4.07) 26.31 (2.46)z 18.03 (5.24) 15.87 (3.24) 18.10 (5.33) 24.19 (4.50) 10.12 (2.90)z 7.33 (2.87) 8.22 (3.00)

HADS diagnostic categories: NC, noncase/normal; BL, borderline/subthreshold; C, case/psychiatric scores. * Significant difference between NC scores (scores <8) and C scores (scores >11). y Significant difference between NC scores and BL scores (scores of 8 to 10). z Significant difference between BL scores and C scores.

through the selective processing of beliefs about the perceived severity of a threat, an overestimation of vulnerability and an inability to cope, and the negative consequential costs and outcomes of HF.47 The combination of risk and cost is modulated by coping appraisals and behaviors. Depressed mood is often a comorbidity of anxiety (r ¼ .66 in our study), and depression can develop via persistent, severe levels of anxiety rendering the individual physically and emotionally incapacitated, leading to the development of negative beliefs about personal loss and failure, and subsequent emotional outcomes of worthlessness and hopelessness.48 Beliefs that are developed through living with HF relate to loss of control (over symptoms, role, function, and well-being), and perceptions of unpredictable, fluctuating symptoms and disease course increase the patient’s anxiety, depression, and negative coping (e.g., behavioral disengagement or avoidance), which are maintained through endless safety-seeking behaviors, selective cognitive processing, and biased cognitive focus49 (Figure 2). Even where “rational” thoughts occur in relation to poor prognosis and advanced disease, measures of cardiac function and severity of disease clearly do not predict QoL and emotional well-being. Depressed and anxious patients have a significantly poorer QoL, which could be improved by changes to their negative belief systems.

Limitations The cross-sectional design of this study provided an assessment of QoL, but did not indicate the impact of changing patterns of beliefs in relation to fluctuating symptoms, hospital admissions, and treatment changes. In addition, the direction or causation between beliefs and outcome variables is also difficult to determine, and conclusions based on self-reported data must be treated with caution. Generalizations

from our data must also be formulated cautiously due to the context of our sample, i.e., mainly Caucasian, male, endstage patients from a Western, Englishspeaking culture.

Implications for Care and Conclusions Overall, this study identified a combination of negative HF beliefs and avoidant coping styles, to provide a greater understanding of the psychological variables that underpin depression and its impact on QoL for patients with advanced HF. This study indicates that patients with more negative beliefs about the consequences and controllability of HF are more vulnerable to depression and consequently a more negative QoL. These distressed patients may also amplify symptoms that are used as markers for the advancement of their condition, such as dyspnea, insomnia, low energy, fatigue, poor appetite, and diminished memory, even without the associated physiological symptomatology. Personality traits (e.g., type D) and psychiatric illnesses such as depression and anxiety may also alter perceptions of somatic symptoms that are associated with HF. The impact of distress and of the associated illness beliefs upon treatment outcomes, functional performance, and symptom-reporting deserves further assessment. The need to screen for distress in all patients with serious symptomatic HF appears necessary. Psychological interventions should strive to decrease negative mood, increase positive mood and coping strategies, and develop patients’ perceptions of control over living with HF symptoms and their disease course.55-57 Enabling patients to increase their acceptance of living with HF by facilitating a problem-solving approach to manage fluctuating symptoms and the uncertainty surrounding their prognosis is vital for managing depressed thinking and negative illness beliefs.58 It is no longer

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Threatening stimuli (heart failure) (e.g. thoughts of death and being disabled, reduced exercise capacity, losing job, loss of role)

Threat appraisal (I can’t cope; I’ll definitely die from this) Prevents disconfirmation of beliefs increases symptoms

(Probabilitya X Awfulnessb) ------------------------------(Coping & Rescuec)

Arousal

(“How can I get better? I don’t know how to?”)

ANXIETY

Safety-seeking behaviours:

Physiological changes:

Over-activity leads to fatigue Avoidance of going out alone Checking pulse obsessively Insists on more outpatient checks with doctor

++ +fatigue, Palpitations Sweating Shaking Weight loss/gain

I’m useless, I’m worthless, I can’t control my health, what’s the point?

DEPRESSION The Negative Triad: Self, Others, The World

No one can help me, no one cares about me, The World is a cruel place

Figure 2 e Combined cognitive model of anxiety and depression applied to heart failure. aBeliefs related to likeliness, frequency, and understanding of threat parallel the self-regulatory model’s illness coherence beliefs, timeline, and symptom identity beliefs. bBeliefs about consequences of threat parallel the selfregulatory model’s beliefs about consequences. cBeliefs about control and coping resources parallel the self-regulatory model’s control beliefs and coping appraisal feedback process (see Figure 1).

acceptable to treat patients’ clinical condition alone, but to recognize that psychological well-being is fundamental to QoL outcomes, clinical prognoses, and ultimately patient mortality.

Acknowledgments The authors thank Ana Sanchez for collecting questionnaire data. The authors acknowledge financial

support from the Royal Brompton and Harefield National Health Service Trust in completing this study.

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