QUALITY OF LIFE
Predictors of Quality of Life in Women with Heart Failure Mary S. Riedinger, RN, PhD,a,b Kathleen A. Dracup, RN, DNSc,b and Mary-Lynn Brecht, PhDb for the SOLVD Investigators Background: Two and one half million women have heart failure (HF). Yet little is known about quality of life (QOL) in this population and the factors influencing it. Given the importance of QOL as an outcome of care, we conducted a study to evaluate predictors of QOL in women with HF. Methods: Using baseline QOL data collected in the Studies of Left Ventricular Dysfunction (SOLVD) trials, we studied predictors of QOL in 691 women with HF. Univariate, bivariate, and multiple regression analyses were used. Potential predictors included age, education, tobacco use, social isolation, life stresses, comorbidity index, New York Heart Association (NYHA) class, HF symptoms, etiology, and medications. We measured global QOL and QOL dimensions of physical function, emotional distress, and social and general health. Results: Women were older (61 ⫾ 10.5 years), predominately Caucasian (75%), and their mean ejection fraction was 0.27 (⫾6.51). Variables with the strongest relationship to QOL included dyspnea, NYHA class, and life stresses. As dyspnea, life stresses, and NYHA class increased, QOL decreased. Additionally, smoking behavior and vasodilator use was associated with decreased QOL. Heart failure etiology of ischemic origin was associated with decreased social life satisfaction, and use of digitalis was predictive of increased social life satisfaction. Finally, increasing age was related to an increase in general life satisfaction. Conclusions: Symptom amelioration, which may improve functional ability, has the greatest potential for increasing QOL in women with HF. Programs to increase physical activity in women with HF should be developed and tested. Finally, clinicians may need to optimize HF medications in women. J Heart Lung Transplant 2000;19:598–608.
H
eart failure (HF) is one of the leading causes of morbidity and mortality in the United States; this signifies a major public health concern. An estimated 5 million people have HF; newly diagnosed cases occur at a rate of 400,000 per year.1 Increased
life expectancy combined with increasingly effective treatments for myocardial infarction will increase the number of patients with HF. The total estimated direct and indirect costs of HF are approximately $21 billion, accounting for costs incurred from hos-
From the Cedars-Sinai Medical Center,a Los Angeles, California, USA; and the University of California–Los Angeles School of Nursing,b Los Angeles, California, USA Submitted October 25, 1999; accepted February 23, 2000. A complete listing of the SOLVD investigators has been published (N Eng J Med 1992;327:685–91). This paper uses data supplied by the National Heart, Lung, and Blood Institute, NIH, DHHS. The views expressed in this paper are those of the
authors and do not necessarily reflect the views of the National Heart, Lung, and Blood Institute. Reprint requests: Mary S. Riedinger, RN, PhD, Cedars-Sinai Medical Center, 8631 West Third Street, Suite 800E, Los Angeles, CA 90048. E-mail:
[email protected]. Copyright © 2000 by the International Society for Heart and Lung Transplantation. 1053-2498/00/$–see front matter PII S1053-2498(00)00117-0
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pitalization, nursing homes, health care professionals, medications, lost productivity, morbidity, and mortality.1 Because of the number of individuals currently affected, the alarming rate of new cases each year; and the enormous burden this disease places on our health care system, survival, and quality of life (QOL), treatment of HF is an important health care issue. If an ultimate goal of health care includes improving QOL, then it is important to understand the effects that medical treatment has on QOL through its direct measurement. To date, much less information is available on women with HF than on men with HF. Most researchers focusing on HF have included only small samples of women, making it difficult to characterize women with HF or to compare them with men with HF. In the past, fewer women participated in clinical trials. For example, in 1 recent study, researchers evaluated the enrollment of women with HF into large clinical trials.2 In the past decade they found 10 HF trials. Combining the samples from all trials yielded 17,370 enrolled patients, with approximately 20% women. Nationally, if slightly more than one half of patients with HF are women, then women’s representation in clinical trials seems disproportionately small. Perhaps, a higher incidence of diastolic dysfunction in women disqualifies them from trials in which systolic dysfunction is the main entry criterion.2 Not only will improvement in QOL benefit patients, it may decrease health care costs related to readmissions. In 1 study of geriatric patients, researchers found that more functionally impaired patients or those who had more social or behavioral problems were more frequently readmitted to the hospital within 3 months; those more socially dysfunctional patients were readmitted more often resulting in increased health care costs.3 In another study of elderly HF patients, investigators determined that higher levels of QOL were related to fewer readmissions, which were associated with decreased health care costs.4 We conducted the current study to identify predictors of QOL in women with HF. Identifying predictors of QOL in women with HF will allow the development of studies to evaluate gender-specific treatment for HF with the goal of improving QOL in women. Not only will improved QOL benefit patients, it may result in decreased health care costs associated with poor outcomes. To meet this goal, we performed a secondary data analysis on QOL data collected during the Studies
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Model—Predictors of quality of life in women with heart failure (HF). ⫹ or ⫺ in parenthesis behind predictors indicates theorized (positive or negative) relationship to quality of life.
FIGURE 1
of Left Ventricular Dysfunction (SOLVD). In the SOLVD trials, QOL data were collected in patients with HF, randomized to either treatment with the angiotensin-converting enzyme (ACE) inhibitor enalapril or treatment with placebo.5,6 These data have not been analyzed separately; the SOLVD investigators aggregated these data with data collected from men and reported the aggregated results. Separate analysis of the women’s data would make a significant contribution to our knowledge of QOL in women with HF.
THEORETICAL FRAMEWORK We developed a conceptual model for the current study. We divided potential predictors or independent variables into 3 categories: personal characteristics, disease characteristics, and treatment characteristics. Personal characteristics included age, years of completed education, tobacco use, social isolation, and life stresses. The disease characteristics category consisted of comorbidity index, HF severity, HF symptoms, and HF etiology. Finally, treatment characteristics included use of HF medications including vasodilators, diuretics, other inotropics, beta-blockers, and digitalis (see Figure 1). The dependent variables included global QOL
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and its dimensions. Global QOL included current life situation and general life satisfaction; the QOL dimensions consisted of physical functioning (vigor, activities of daily living [ADL], health interference with normal activities), emotional distress (anxiety, depression), social health (social function, satisfaction with social life, intimacy), and perceived health (general health rating). Based on this conceptual framework, we developed a hypothesis and stated it in general empirical terms: personal, disease, and treatment characteristics are predictive of QOL in women with HF.
METHODS SOLVD Trials The proposed study was a secondary analysis of QOL data in the SOLVD trials study. The SOLVD studies were comprised of 2 distinct sections: (1) a heart failure registry and (2) 2 double-blinded, placebo-controlled, randomized trials of enalapril in patients with either overt or covert HF. Quality of life data were collected in patients with HF, randomized to either treatment with the angiotensinconverting enzyme (ACE) inhibitor enalapril or to treatment with placebo, at baseline, 4 to 6 weeks, 1 year, and 2 years after admission into the study.6 Clinical data were collected at various intervals throughout the study. We analyzed QOL baseline data on 764 women. However, only 691 women had data for all independent variables and, therefore, constitute the study sample. We compared the sociodemographic and clinical characteristics of the 691 women in this study with those women not included (n ⫽ 73). We found no obvious differences between the included and excluded subjects, with the exception of education. Those not included had completed almost 2 more years of education than those subjects that were included.
Instruments Charlson Comorbidity Index The Charlson Comorbidity Index (CCI) was developed for use in longitudinal research to predict risk of 1-year mortality based on presence of comorbid disease.7 The original CCI considered the following to be significant comorbid conditions: myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, connective tissue disease, ulcer disease, mild liver disease, diabetes with and without end-organ damage, hemiplegia, moderate or severe renal damage, any tumor, cancer,
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leukemia, lymphoma, moderate or severe liver disease, metastatic solid tumor, and acquired immune deficiency syndrome. We used a modified version of the CCI. The SOLVD investigators collected most of the information on comorbid conditions listed in the index. However, 4 conditions were not formally documented as to their presence or absence at the baseline evaluation. In the current analysis, we assumed that those patients with hemiplegia or severe diabetes mellitus with end-organ damage were excluded from the SOLVD randomization trial. Peptic ulcer and peripheral vascular diseases also were not considered in the index. These last 2 conditions were excluded from the SOLVD analysis because the data were not collected. Therefore, the CCI was a modified version. New York Heart Association Classification Disease severity is a measure of the complexity or degree of HF. Heart failure severity was measured using the New York Heart Association’s (NYHA) classification score (a measure of functional capacity).8 Symptoms Scale The Symptoms Scale was used to determine the degree of chest pain, dyspnea, and dizziness that patients had experienced during the month prior to the baseline evaluation period. These 3 symptoms were used in this study as predictors of QOL. This scale has been used previously in the Cardiac Arrhythmia Suppression Trial. SOLVD Battery The SOLVD QOL battery consisted of items taken from several instruments; these items were compiled to create a comprehensive battery that addressed most of the dimensions of QOL of HF patients. The battery included items from the Profile of Mood States Inventory (POMS), the Functional Status Questionnaire (FSQ), the RAND Medical Outcome Study Instrument (MOS), the Beta Blocker Heart Attack Trial questionnaire, and the Ladder of Life (see Table I). The psychometric properties of the POMS,9 –11 FSQ,12–15 MOS,16,17 Symptoms Scale,18 and the Ladder of Life19,20 have been extensively tested and are considered valid and reliable instruments.10 –20 Although some of the items used in the SOLVD battery have undergone psychometric testing elsewhere, we subjected the items used in this study to additional testing for internal consistency with this patient sample.
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TABLE I QOL dimensions and instruments QOL Dimension Current life situation General life satisfaction Vigor ADL (basic and intermediate) Interference with activities Anxiety Depression Social activity Social life satisfaction Intimacy General health
Instrument Ladder of Life Beta Blocker in Heart Attack Trial Profile of Mood States Functional Status Questionnaire Beta Blocker in Heart Attack Trial Profile of Mood States Profile of Mood States Functional Status Questionnaire Functional Status Questionnaire Beta Blocker in Heart Attack Trial Medical Outcomes Study
ADL, activities of daily living; QOL, quality of life.
Reliability and Validity Testing of SOLVD Battery Reliability Reliability tests for internal consistency were performed on the multi-item scales used in the SOLVD battery. Every scale exceeded the 0.70 alpha coefficient level. Therefore, the SOLVD multi-item scales are considered reliable for group comparisons within this population. Discriminate validity of the SOLVD battery was tested using a correlation matrix to determine the degree of independence among constructs. Secondly, the ability of the constructs to differentiate among patients based on severity of HF was also tested. This latter method of discrimination has been performed in other populations with heart disease.18 Validity Calculating correlations determined that most SOLVD QOL battery constructs fell below the 0.50 level of discrimination. This level has been used by other investigators and is felt to be discriminative enough that constructs can be considered different from one another. Basic and intermediate ADL had a correlation of 0.58; basic ADL and intermediate ADL had correlations with social activities of 0.53 and 0.75, respectively. The correlation between anxiety and depression was 0.74. Between social and general life satisfaction, the correlation was 0.63. After grouping patients by NYHA classification, t-test analysis revealed significant differences among
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the NYHA severity groups for all QOL dimensions except anxiety and depression. This demonstrates that most dimensions in the SOLVD battery are able to discriminate severity of illness.
Data Analysis Testing the hypothesis involved 2 phases of analyses, which allowed examination of relationships from both univariate and multivariate prediction perspectives. In the first phase, we computed first order correlations to test the relationship of each potential predictor with the QOL dimensions, without controlling for other variables. In the second phase of analyses, we used regression procedures to test the hypothesis from a multivariate prediction prospective. All independent variables were considered as potential predictors in these samples. Because of the relatively small set of potential predictors and the likely inter-relationships among predictors, variables were not dropped based on non-significant first order correlations. We performed analyses for each dependent variable (global QOL [current life situation, general life satisfaction], and QOL dimensions [physical function, emotional distress, social health, and perceived health]). Power analysis was performed to determine if the sample size was large enough to detect relationships between variables. The sample for the primary analysis of 691 subjects was sufficient to detect a small effect with a power of 0.95. Missing data We ran frequencies to determine the amount of missing data. Depending on the data type and quantity of missing dependent variable data, we either eliminated data from the analyses or estimated (imputed) data. If a scale had less than 50% of the data missing and the items were similar with similar responses, then we imputed missing values. The subject’s average score for valid items was used in place of missing items. Statistical assumptions Several assumptions underlie the use of multiple linear regression; it is assumed that the independent variables are fixed and measured without error. Assumptions regarding the error terms include (1) that the mean errors for each observation (over many replications) is zero, (2) that errors associated with 1 observation are not correlated with errors associated with any other observation, (3) that the variance of errors for all values of the independent variables is constant (homoscedasticity), (4) that the
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errors are not correlated with the independent variables, and (5) that the errors are normally distributed.22 Except for measurement and specification errors, regression analysis is known to be robust in the presence of assumption violations. Specification errors occur when the model is not justified from a theoretical perspective. Examples of specification errors include (1) omitting relevant variables from the model, (2) including irrelevant variables in the model, or (3) specifying that the regression is linear when it is curvilinear. Normality, heteroscedasticity, and multicolinearity were tested using appropriate statistical techniques. Multicolinearity was assessed by regressing each independent variable on all the other independent variables.23 Any R2 value near 1.0 is indicative of high multicolinearity.23 A correlation matrix and regression analyses of the independent variables were made to determine the degree of multicolinearity between the independent variables. Additionally, plots were examined to evaluate linearity, specification error, and homoscedasticity. After review of the residual plots, the plots were determined free of abnormalities and formed a straight broad band, which the horizontal line divided equally. Each independent variable was regressed on all others to check for multicolinearity; most R2 were under 0.20. The highest R2 was 0.33 (for NYHA classification) but was not considered sufficient to invalidate overall regression results. Therefore, all statistical assumptions were reasonably met.
Human Subjects This study was considered exempt from review by the Human Subject Protection Committee. The data were existing, and no patient identifying markers were in the data set. Therefore, patient confidentiality was maintained.
RESULTS Clinical and demographic characteristics of the sample are presented in Table II. The average age of the women was 61 years; their mean ejection fraction was 0.27. Most of the women were Caucasian (75%), more than one half of the women had a history of smoking and myocardial infarction, and most were symptomatic. The average CCI score for the group was 1.02 ⫾ 0.78 (range 0 to 4 points). One patient had a comorbidity index score of 4, and 24 patients had comorbidity index scores of 3. These 25 patients constituted 3.6% of the entire population. There-
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TABLE II Sociodemographic and clinical data Women (n ⴝ 691) Demographics
n
Age (years) Race African-American Asian Caucasian Hispanic Native American Other Education (years) Smoking Hx Smoking—at entry HF severity Ejection fraction (%) NYHA class Ischemic etiology Comorbidity Comorbidity score Hx of myocardial infarction Hx of diabetes mellitus Hx of COPD Hx of stroke CHF—symptomatic Symptoms Chest pain Dyspnea Dizziness Medications at baseline Taking vasodilators Taking diuretics Taking digitalis Taking other inotropics Taking beta–blockers
691
%
144 1 521 21 2 2
20.84 0.14 75.40 3.04 0.29 0.29
402 124
58.18 17.95
691 691 453
84.99
691 423
61.22
180 49 51 437
26.05 7.09 7.38 63.24
691 691 691
44.6 69.5 44.1
365 447 302 6 87
52.82 64.69 43.70 0.87 12.59
Mean
Std Dev
60.65
10.48
10.86
3.02
26.79 1.97
6.51 0.72
0.98
0.70
COPD, chronic obstructive pulmonary disease; Hx ⫽ history; n, sample size; NYHA, New York Heart Association; Std Dev, standard deviation.
fore, for analytical purposes, those patients with scores of 3 or 4 were combined with those patients with scores of 2. After combining the groups, the mean comorbidity score was 0.98 ⫾ 0.70. A t-test was performed to detect any differences between the original comorbidity scores and the aggregated scores and no significant differences were found (t ⫽ 0.71, p ⫽ n.s.). The women had a mean NYHA classification of 1.99 ⫾ 0.75. Because only 1.3% of patients had a NYHA class IV rating, those patients in class III and IV were combined. To test for significant differences between the original NYHA class means and
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the aggregated NYHA class means, a t-test was performed; no significant differences were found (t ⫽ 0.51, p ⫽ n.s.). Overall, women with HF had decreased QOL. Comparisons between QOL in this cohort of women with HF and normative groups of women are reported elsewhere (Riedinger et al., unpublished observations). Summarizing those findings, women with HF had significantly lower global QOL than the normative group of women. Where comparative data were available, women with HF had worse vigor, intermediate ADL, anxiety, depression, social activity, and general health ratings. Women with HF had scores consistent with normal populations for basic ADL dimensions. Yet, less than one half of the women with HF felt that they were healthy enough to perform normal activities.
Predictors of QOL in HF Results of the multiple regression analyses are presented in Table III. Summaries of variables that accounted for explained variance in QOL for women with HF are presented below by independent variable category. Personal characteristics We found a positive relationship between age and general life satisfaction; that is, as age increased, general life satisfaction increased. As the years of completed education increased, the perception that health interfered with normal activities decreased. Tobacco use was associated with decrease in vigor and general health. An increase in the number of life stresses was related to an increase in anxiety and depression. Disease characteristics Comorbidity was not significantly associated with QOL. New York Heart Association classification was predictive of the global QOL measure of current life situation and 3 of the 10 QOL dimensions (vigor, intermediate ADL, and social activity); as NYHA classification increased QOL ratings decreased. An increase in dyspnea was predictive of decreased QOL for all the QOL measures, increased chest pain was predictive of decreased general health ratings, and finally, dizziness was not significantly predictive of any of the QOL measures. Etiology of ischemic origin was associated with decreased social life satisfaction.
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TABLE III Multiple regression data Quality of Life Global Current life situation Dyspnea Vasodilators NYHA class General life satisfaction Dyspnea Age Dimensions Physical function Vigor Dyspnea NYHA class Smoking behavior Basic ADL Dyspnea Intermediate ADL Dyspnea NYHA class Vasodilators Interference Activity Dyspnea Education Emotional distress Anxiety Dyspnea Life stresses Depression Life stresses Dyspnea Social health Social activity NYHA class Dyspnea Vasodilators Social Life Satisfaction Dyspnea Etiology Digitalis Perceived health General health Dyspnea Smoking behavior Chest pain
Women (n ⴝ 691) Beta Coeff 0.23 ⫺0.63 ⫺0.45 ⫺0.17 ⫺0.02 Beta Coeff
0.72 ⫺1.79 ⫺1.17 1.68 2.08 ⫺6.98 ⫺4.55 ⫺0.11 ⫺0.06 ⫺0.72 1.04 1.47 ⫺0.81 ⫺10.63 3.43 ⫺10.11 ⫺0.16 0.56 ⫺0.40 ⫺0.12 0.24 ⫺0.10
R2
p
0.24
0.0001 0.003 0.04 0.02 0.0001 0.0001 0.02
0.23
R2
p
0.17
0.0002 0.002 0.006 0.04 0.0001 0.0002 0.0001 0.0001 0.0001 0.001 0.0002 0.01 0.02
0.18 0.43
0.17
0.20 0.19
0.27
0.24
0.25
0.0001 0.004 0.008 0.0001 0.009 0.002 0.0001 0.0001 0.0003 0.001 0.0001 0.0001 0.004 0.005 0.0001 0.003 0.003 0.008
Treatment characteristics Use of vasodilators at baseline predicted decreased current life situation, intermediate ADL, and social activity. Digitalis use at baseline was predictive of increased social life satisfaction.
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FIGURE 2 Revised model—Predictors of quality of life in women with heart failure (HF). ⫹ or ⫺ in parenthesis behind predictors indicates theorized (positive or negative) relationship to quality of life. The hypothesis that personal, disease, and treatment characteristics predict QOL was accepted. See Figure 2 for a revised model based on the findings.
DISCUSSION Predictors will be discussed by category. Due to the relationships among the predictors, it is easier to discuss the findings by predictor, rather than by QOL dimension. Younger patients experienced more mood disturbance, specifically anxiety, and depression than older patients, whose expectations may be more consistent with their condition. This is consistent with findings in the cancer literature. In 1 metaanalysis of 58 studies of various types of cancer, psychological sequelae of cancer diagnosis was evaluated.24 Researchers found that younger patients had higher levels of anxiety, depression, and general psychological distress than older patients. This may be due to the inability of younger patients to adapt to the stress of serious illness and the possibility of death.25 In another study of patients awaiting heart transplantation, researchers found older age to be predictive of increased QOL.26 In yet another study of QOL in a normative sample of 4,600 subjects, investigators found that as the subjects aged, they
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experienced less anger, anxiety, and depression (although depression increased around age 60 that was related to marital status).27 Additionally, younger women with HF were less satisfied with their general and social life. Their current life situation ratings were also lower than older patients. However, younger subjects were able to function at higher intermediate ADL and social activity levels than were older subjects. Those women with less education had more anxiety than women with more education. These results are consistent with a study performed on 400 men and women in Athens, Greece.28 Researchers found that regardless of age level and gender, the higher the educational levels the lower the anxiety score. Although these findings support the current findings of this study, temporal and cultural influences must be considered when comparing this study (which was performed more than 30 years ago in Greece) with the current study. More years of education was also found to be associated with basic ADL and general health ratings; as years of education increased, basic ADL and general health scores improved. Additionally, more education was related to the perception that health interfered less with normal activities. These findings are consistent with a study that examined the effect of education on quality of life.27 After evaluating national samples from 2 surveys conducted in 1990 and 1995, researchers determined that subjects with more education had lower levels of physical (aches, pains, and malaise) and emotional (anger, anxiety, and depression) distress. They further determined that education reduces distress through increased personal control. Work, whether conducted in or out of the home, that was non-routine and allowed the individual to develop as a person decreased distress. These same researchers found that education had a larger effect on women’s distress levels than it had on men’s levels. Smoking at study entry was predictive of worse general health ratings, anxiety, and depression. These findings are consistent with other studies that have demonstrated an association between nicotine dependence and increased depression and anxiety.29 –31 A history of smoking or smoking at study entry was not associated with dyspnea. Those patients with a history of chronic obstructive pulmonary disease (COPD) had a significantly higher incidence of dyspnea. However, because the number of patients with COPD was so small, it is doubtful that
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COPD had an overall affect on dyspnea for the entire group. Comorbidity was not a predictor of QOL for the HF women. This is most likely related to the fact the SOLVD exclusionary criteria were comprehensive; therefore, most patients with comorbid conditions were excluded at the beginning of the study. New York Heart Association classification was the second most predictive variable across QOL dimensions. (Dyspnea was the most predictive variable.) An increase in NYHA classification was associated with a decrease in current life situation ratings, general life satisfaction, vigor, basic and intermediate ADL scores, social activity, social life satisfaction, intimacy, general health, and an increase in the perception that health interfered with normal activities. As a reflection of physical functioning, one would expect NYHA classification to be predictive of any dimension with a physical functioning component, such as vigor, basic and intermediate ADL scores, and social activity. The NYHA classification was not predictive of depression. This finding is in contradiction to other previously documented findings in 134 patients with advanced HF, where NYHA classification was predictive of depression.32 The difference may be gender related because the sample was comprised mostly of men. In general, patients with higher NYHA classifications experience increased incidence of depression, related to the inability to perform physical activities, as increased physical activity decreases depression.33 It is unclear at this time why NYHA classification was not predictive of depression in the study women; women may not be as negatively affected as men are by a reduction in physical functioning. Perhaps men are more sensitive to reduced physical activity levels than are women, who may live more sedentary lives. This discrepancy may also be related to the discriminate ability of the POMS instrument. This finding requires further exploration. Of all the predictors, symptoms of HF (particularly dyspnea) were the most predictive of decreased QOL. Increased incidence of dyspnea was associated with decreased QOL for all measured aspects of QOL. Because of the fact that only 7% of the women had a history of COPD, the incidence of dyspnea is most likely related to HF, which is an expected symptom. Increased incidence of chest pain was associated with worse general health ratings. The effects of 5 types of HF medications were tested as predictors of QOL in women with HF: (1)
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vasodilators, (2) diuretics, (3) digitalis, (4) other inotropics, and (5) beta-blockers. Despite the potential benefit from taking these medications, side effects may be associated with their use.34 Potential side effects of vasodilators and diuretics include dizziness, which affects QOL. The HF patients taking vasodilators had a significantly higher incidence of dizziness than those not taking vasodilators. However, we found no difference in the incidence of dizziness with relation to use of diuretics. Vasodilator medication use was predictive of decreased scores of current life situation, intermediate ADL, and social activity. The fact that digitalis predicted increased social life satisfaction may be spurious and requires further investigation. Taking other medications such as diuretics, betablockers, and other inotropic medications at study baseline was not associated with any QOL dimensions. Consistently, research has shown that HF medications, particularly ACE inhibitors, prolong survival, yet they have little or no effect on QOL. This result may also be spurious.
SUMMARY Of the explained variance, dyspnea was significantly related to all QOL dimensions. Additionally, NYHA classification, which is a measure of functional capacity, was also significantly related to QOL. Based on the current findings, improving functional ability and decreasing HF symptoms have the greatest potential for increasing QOL in this population of women with HF.
Limitations Secondary data analyses are associated with several limitations. First, we are limited to studying the available data and the variables contained therein. Secondly, we are unaware of any data coding or entry errors. All data was taken at face value and considered accurate; this may not be the case in all instances. Additionally, readers should consider a conservative approach when interpreting marginally significant test statistics. When many statistical tests are performed, the potential is greater for producing significant findings when none truly exist. We note several additional limitations to this study. For the most part, the independent variables accounted for a small amount of the explained variance in the QOL dimensions. We found the highest amount of explained variance (43%) in intermediate ADL. Therefore, other determinants of QOL, not measured in this study, are predictive of the unexplained variance. These other determi-
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nants may include social support,35 exercise patterns,36 spirituality,37 and personal control.27 There are other possible limitations to this study. Results may not be generalizable to the broader HF population not participating in clinical trials because there may be something unique about this sample that is not known at this time. This study included a predominately Caucasian sample, thus, findings may not apply to other populations. Future studies need to incorporate other ethnic groups to determine the effects of ethnicity and culture on QOL in HF. Because this study is a secondary data analysis, we were limited to studying the available data and the variables contained therein; other more appropriate or sensitive measures may exist for use in future studies. Additionally, this study was also limited to the SOLVD investigators definition of QOL. Other QOL dimensions may be important in this patient population. This includes sexual functioning, which has been shown to decrease in the HF population.38 Another limitation was the evaluation of only baseline data. Longitudinal analyses may have provided additional information regarding QOL in women with HF. Baseline data were used to optimize the sample size.
Implications and Significance Data from this study suggest that physical functioning is predictive of QOL in women with HF. Programs to increase physical activity may improve the overall QOL in women with HF. Additionally, clinicians may not be optimizing HF failure medications in women. Digitalis has been shown to decrease some symptoms of HF and increase physical functioning and quality of life.39 Digitalis, in this study, was predictive of increased social life satisfaction, yet, only 44% of the women with HF were taking digitalis at baseline. The SOLVD data were collected during a period that preceded publications on the benefits of ACE inhibitors in HF. Although, in the years after the SOLVD trials, many publications have reported that ACE inhibitor therapy improves survival in HF patients, recent studies have demonstrated that ACE inhibitor treatment is underused in the HF population.8,40 – 42 Additionally, today’s HF patients who receive ACE inhibitor treatment may be prescribed inadequate dosages.43,44 Although appropriate dosing of ACE inhibitors has increased over time, in 1 study at an academic medical center, optimal dosing increased from 24% in 1990 to 62% in 1995; however, dosing remains inadequate.44 Vasodilator usage in the women with HF at baseline was only 53%;
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diuretic usage was 65%. Current optimal treatment would include the use of digitalis, ACE inhibitors, diuretics, and beta-blockers in appropriate dosages.45,46 Improvement in symptom control may improve QOL in women with HF. Symptom amelioration may result in increased physical functioning, which would improve QOL and potentially improve survival rates, as evidenced by results found in the SOLVD trials and other studies.47,48 It is important that clinicians continue to measure QOL using relevant, psychometrically tested instruments that address all aspects of QOL. The results from the current study pose new questions that can be answered only with further research. Although, most aspects of the model developed for this study were supported, most of the determinants were predictive of only a small amount of the variance of QOL. Therefore, future studies should incorporate additional predictors, based on theory that might account for additional variance. Future research should include exploration of not only physical but also psychological ramifications of the decreasing ability to perform intermediate ADL. Women, as homemakers and caregivers, have traditionally performed activities such as shopping and keeping house. The effect of HF on their ability to continue these tasks may have profound effects on their feelings of self-esteem, which impacts QOL.49 Additionally, studies evaluating aspects of functional capacity and women’s ability to perform household chores will be important to the development and testing of appropriate interventions. In summary, women with HF, as evidenced by these data, tend to have a poor QOL. Heart failure imposes a great impact on QOL, particularly on functional abilities. As one investigator found, a substantial number of patients with HF are willing to accept the risk of drug-induced death for improved QOL.50 Some patients with HF may adapt more readily to their conditions than others.51 Moreover, HF symptoms, particularly dyspnea, impact all measured aspects of QOL. REFERENCES 1. American Heart Association. Statistics. http://americanheart.org/statistics/07other.html. 1999 2. Lindenfeld J, Krause-Steinrauf J, Salerno J. Where are all the women with heart failure? J Am Coll Cardiol 1997;30(6): 1417–9. 3. Di Iorio A, Longo AL, Mitidieri Costanza A, et al. Characteristics of geriatric patients related to early and late readmissions to hospital. Aging 1998;10(4):339 – 46. 4. Candlish P, Watts P, Redman S, Whyte P, Lowe J. Elderly
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