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Psychometric Properties of the Symptom Status Questionnaire–Heart Failure

Heo, Seongkum PhD, RN; Moser, Debra K. DNSc, RN, FAAN; Pressler, Susan J. PhD, RN; Dunbar, Sandra B. DSN, RN; Mudd-Martin, Gia PhD, RN; Lennie, Terry A. PhD, RN

The Journal of Cardiovascular Nursing: March/April 2015 - Volume 30 - Issue 2 - p 136–144
doi: 10.1097/JCN.0000000000000102
ARTICLES
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Background and Purpose: Many patients with heart failure (HF) experience physical symptoms, poor health-related quality of life (HRQOL), and high rates of hospitalization. Physical symptoms are associated with HRQOL and are major antecedents of hospitalization. However, reliable and valid physical symptom instruments have not been established. Therefore, this study examined the psychometric properties of the Symptom Status Questionnaire–Heart Failure (SSQ-HF) in patients with HF.

Method: Data on symptoms using the SSQ-HF were collected from 249 patients (aged 61 years, 67% male, 45% in New York Heart Association functional class III/IV). Internal consistency reliability was assessed using Cronbach’s α. Item homogeneity was assessed using item-total and interitem correlations. Construct validity was assessed using factor analysis and testing hypotheses on known relationships. Data on depressive symptoms (Beck Depression Inventory II), HRQOL (Minnesota Living With Heart Failure Questionnaire), and event-free survival were collected to test known relationships.

Results: Internal consistency reliability was supported: Cronbach’s α was .80. Item-total correlation coefficients and interitem correlation coefficients were acceptable. Factor analysis supported the construct validity of the instrument. More severe symptoms were associated with more depressive symptoms, poorer HRQOL, and more risk for hospitalization, emergency department visit, or death, controlling for covariates.

Conclusions: The findings of this study support the reliability and validity of the SSQ-HF. Clinicians and researchers can use this instrument to assess physical symptoms in patients with HF.

Seongkum Heo, PhD, RN Assistant Professor, College of Nursing, University of Arkansas for Medical Sciences, Little Rock

Debra K. Moser, DNSc, RN, FAAN Professor and Gill Chair of Nursing, College of Nursing, University of Kentucky, Lexington.

Susan J. Pressler, PhD, RN Professor, School of Nursing, University of Michigan, Ann Arbor

Sandra B. Dunbar, DSN, RN Professor, School of Nursing, Emory University, Atlanta, Georgia

Gia Mudd-Martin, PhD, RN Assistant Professor, College of Nursing, University of Kentucky, Lexington

Terry A. Lennie, PhD, RN Professor, University of Kentucky, College of Nursing, Lexington.

Funding for this study came from an American Heart Association Postdoctoral Fellowship to Seongkum Heo; the National Institutes of Health (NIH) National Institute of Nursing Research (NINR) R01 NR009280 to Terry Lennie; the Philips Medical-AACN Research Award and Center grant NIH NINR 1P20NR010679 to Debra Moser; and, in part, by PHS grant M01 RR0039 from the General Clinical Research Center program, NINR, and PHS grant UL1 RR025008 from the Clinical and Translational Science Award program, NIH, National Center for Research Resources (D. Stephens), and the Atlanta Veterans Administration Medical Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINR or the NIH.

Correspondence Seongkum Heo, PhD, RN, College of Nursing, University of Arkansas for Medical Sciences, 4301 W Markham Street 529, Little Rock, AR 72205 (sheo@uams.edu).

Patients with heart failure (HF) have poorer health-related quality of life (HRQOL) than do healthy people and patients with other chronic diseases1,2 and higher rates of hospitalizations, which account for the high costs of HF.3,4 Physical symptoms can affect both HRQOL and hospitalization. Approximately 90% to 100% of HF patients experience dyspnea,5 and 91% have multiple physical symptoms.6 Physical symptoms are 1 of the factors most strongly associated with HRQOL.1,7,8 In addition, approximately 90% to 100% of HF patients who visit emergency departments or are hospitalized experience physical symptoms.5,9 Thus, improvement in physical symptoms in this population may lead to improvement in HRQOL and reduction of hospitalization rates. To manage and improve physical symptoms effectively, the first step is to assess physical symptoms using a reliable and valid instrument.

Several instruments have been used to assess physical symptoms in patients with HF: the Memorial Symptom Assessment Scale (MSAS),10 the MSAS-Short Form,11 the MSAS-HF,12 the Heart Failure Somatic Perception Scale,13,14 the Dyspnea-Fatigue Index,15 and the Kansas City Cardiomyopathy Questionnaire (KCCQ).16 The MSAS was developed to assess multidimensional information on 32 physical and psychological symptoms with some additional open-ended questions for other symptoms, and the reliability and validity of this instrument have been supported in cancer patients.17,18 The MSAS-HF is the modified version of the MSAS to use for patients with HF and includes 32 physical and psychological symptoms.12 In patients with HF, number of symptoms, symptom prevalence, and symptom burden were associated with HRQOL.10–12 However, these instruments assess not only common HF symptoms but also psychological and other physical symptoms. Thus, it is difficult to evaluate the unique effects of common HF symptoms on patient outcomes using these instruments. The Heart Failure Somatic Perception Scale is used to assess the presence and severity of 18 common physical signs, symptoms, and the effects of dyspnea on daily activities in patients with HF.14 The Dyspnea-Fatigue Index has also been used to evaluate symptoms in patients with HF.15 It consists of 3 questions that assess only dyspnea and fatigue related to functional status.8,15 It is difficult to assess physical symptoms independently from functional impairment in the former and latter instruments, and the latter instrument does not cover some common HF symptoms. The Symptom and Symptom Stability subscales of the Kansas City Cardiomyopathy Questionnaire are used to measure the frequency, burden, and stability of physical symptoms but cover only swelling, fatigue, and dyspnea and do not include assessment of severity of symptoms.16 Thus, a reliable and valid physical symptom instrument is needed to assess and manage important common physical symptoms in patients with HF and examine their unique effects on patient outcomes.

Therefore, we developed the Symptom Status Questionnaire–Heart Failure (SSQ-HF), which is a modified version of the MSAS-HF.12 The authors of the current study selected the most common HF-related physical symptoms reported in the literature.6,12 The following items were selected because they have been noted to occur most frequently, with greatest severity, and cause the most distress among patients with HF: dyspnea during the day time, dyspnea when lying down, fatigue, chest pain, edema, sleeping difficulty, and dizziness or loss of balance.6,12 As in the MSAS-HF, the SSQ-HF measures the frequency, severity, and distress of each physical symptom.12

The current study was conducted to test the reliability and validity of the SSQ-HF. The first aim was to test the reliability and item homogeneity of the SSQ-HF using Cronbach’s α, item-total correlations, and interitem correlations. The second aim was to test construct validity using factor analysis and examine the hypothesized relationships between physical symptoms as measured by the SSQ-HF and depressive symptoms, HRQOL, and event-free survival. Depressive symptoms have been associated with physical symptoms in patients with HF. For instance, 2 recent studies have shown that higher levels of depressive symptoms were predictive of poor physical symptom status.8,19 Another study demonstrated a positive association between number of depressive symptoms and number of physical symptoms.11 Event-free survival was defined as the time from enrollment to first hospitalization, emergency department visit, or death. Most patients who were admitted to hospitals had physical symptoms.5 Therefore, hypothesis 1 was that the depressed group would have more severe physical symptoms than the nondepressed group would, controlling for age,8 gender,20 body mass index (BMI),21 comorbidities,22 and use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs), β-blockers, and diuretics.23,24 Hypothesis 2 was that physical symptoms would be associated with HRQOL, controlling for age,1 gender,25 comorbidities,26 New York Heart Association (NYHA) functional class,26 and depressive symptoms.27 Hypothesis 3 was that physical symptoms would predict event-free survival, controlling for age, gender, BMI,28 comorbidities,29 NYHA functional class,30 left ventricular ejection fraction (LVEF),30 medications (ACEIs or ARBs, β-blockers, and diuretics),31–33 and depressive symptoms.34

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Methods

Design, Setting, and Sample

This was a prospective, observational study.27,35 Patients who met the following inclusion criteria were enrolled: (1) had a confirmed diagnosis of HF with preserved or nonpreserved systolic function, (2) stable doses of cardiac-related medications for 3 months or for 2 clinic visits, and (3) ability to speak and read English. Patients were excluded if they had any of the following: (1) HF originating from valvular heart disease, rheumatic disease, or pregnancy, (2) myocardial infarction or stroke within the past 3 months, or (3) other comorbid conditions that could considerably affect morbidity and mortality. Patients with severe cognitive impairment or psychiatric problems as determined by referring physicians or through medical record review were excluded because of potential difficulties with data collection. Sample size was calculated based on the recommendations (at least 10–15 subjects per item) by Nunnally and Bernstein36 and Pett and colleagues.37

Institutional review board approval for the study was obtained from appropriate institutions. Patients were enrolled from outpatient clinics in academic health centers or community hospitals in 3 Southern and Midwestern cities in the United States. Signed informed consent was obtained from all subjects. Data on physical and depressive symptoms, demographic and clinical characteristics, and HRQOL were collected at patients’ houses, clinics, or places that patients wanted to meet a research team member. Data on event-free survival were collected using monthly telephone calls with patients and verified using medical record review when possible. The mean (SD) follow-up time for the sample was 571 (347) days. When necessary, clinical characteristics were confirmed using medical record reviews by research team members.

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Measures

Symptoms referred to patients’ perceptions of physical HF symptoms and were measured by the SSQ-HF. The instrument consists of 7 questions used to assess the presence, frequency, severity, and distress of common physical HF symptoms, including daytime dyspnea, dyspnea when lying down, fatigue, chest pain, edema, difficulty sleeping, and dizziness or loss of balance. Patients are asked to indicate the presence of each symptom during the past 4 weeks. If no symptom, the score is 0. If a patient has experienced a symptom, the patient is asked about the frequency, severity, and distress of the symptom. Responses on frequency range from 1 (less than once per week) to 4 (nearly daily); severity responses range from 1 (slight) to 4 (very much); and distress responses range from 0 (not at all) to 4 (very much). The total score for each physical symptom is calculated by summing the ratings for the symptom; scores range from 0 to 12. The total score on the instrument is calculated by summing the total scores of all the symptoms. Possible total scores range from 0 to 84, with higher scores indicating more severe symptoms.

Depressive symptoms were defined as cognitive, affective, and somatic depressive mood and were assessed using the Beck Depression Inventory II.38,39 The instrument consists of 21 items with 4 response options. Scores from 14 to 19 indicate mild depression; scores from 20 to 28, moderate depression; and scores from 29 to 63, severe depression.39 In the current study, patients were divided into 2 groups using a cut point of 14 (the depressed group, with a score ≥14, vs the nondepressed group, with a score <14); this cut point indicates at least mild depressive symptoms and has been used as a cut point for depressive symptoms.40 Internal consistency reliability has been supported in psychiatric outpatients.39 Validity has been supported by hypothesized relationships between the scores on this instrument and scores on other depression instruments in patients with HF41 and by factor analysis in college students.38 Cronbach’s α in the current study was .91.

Health-related quality of life was defined as an individual’s perception of how his/her clinical condition or treatment affected various aspects of daily life42 and was measured by the Minnesota Living With Heart Failure Questionnaire.43 This instrument was selected because it is the most commonly used to measure HRQOL in patients with HF, has well-established reliability and validity,43 and has been shown to be predictive of hospitalization and death in patients with HF.44,45 It consists of 21 items with 6 response options from 0 (no impact) to 5 (very much impact). The total score is calculated by summing all of the ratings; possible total scores range from 0 to 105, with higher scores indicating poorer HRQOL. Cronbach’s α values in several studies have been greater than .70, indicating acceptable internal-consistency reliability.43,46,47 In the current study, Cronbach’s α was .93. Construct validity has been supported by significant relationships to NYHA functional class and symptoms.46,47

Event-free survival was defined as the time from enrollment in the study to any first event of hospitalization, emergency department visit, or death due to any cause. A research team member called each patient by telephone monthly to collect data on hospitalization and emergency department visits. In addition, patients were given a hospitalization diary so that they could record hospitalizations before they forgot. Patients were asked to record information on hospitalizations and emergency department visits, including admission date, discharge date, hospital name, and the reason(s) for the events. If patients were admitted to recruitment hospitals, their hospitalization data were verified through medical record review. If patients were admitted to hospitals or visited emergency departments other than the recruitment sites, discharge notes from the hospitals, when available, were used to collect data on hospitalization and emergency department visit in addition to patient interviews. A trained research associate clarified, organized, and verified hospitalization data.

Data on death from any cause were collected using several methods, including medical record review and interviews with the patient’s family members or healthcare providers. The research team asked patients to provide 1 or 2 contact numbers to use if the team was unable to contact them after the patients gave written consent for the study. During monthly follow-ups, if the research team could not contact the patient, the team checked medical records to determine whether the patient had died. If the research team could not determine the patient’s death through medical record review, the team contacted 1 of the numbers that the patient provided to determine the patient’s event.

Data on sociodemographic characteristics (age, gender, and BMI) and clinical characteristics (comorbidities, NYHA functional class, LVEF, and medication) were collected using standard Sociodemographic and Clinical Questionnaires by patient interviews or in medical record review. New York Heart Association functional class was determined by trained research team members in in-depth face-to-face patient interviews.48 Comorbidities were assessed using the Charlson Comorbidity Index, which was included in the Clinical Questionnaire.49

Cronbach’s α was used to assess the internal consistency reliability of the SSQ-HF. An acceptable coefficient for Cronbach’s α is greater than .70.50 Item-total correlations and interitem correlations were used to assess item homogeneity.51 An acceptable coefficient for item-total correlations is greater than .30, indicating contribution of the item to the measure.51 Acceptable coefficients for interitem correlations are greater than .30 and less than .70.51 Items with coefficients of .30 or less mean lack of contribution of the item, and items with coefficients .70 or greater mean redundancy.

Common factor analysis was conducted because we assumed that variance in symptoms could be explained by the combination of the underlying common factors and the variance unique to symptoms.37 Factors were extracted based on the results of a scree plot, eigenvalues, and total variance.37 A loading score greater than 0.40 was used as a cut point.37

General linear model analysis and t test were used to test hypothesis 1, that the depressed group would have more severe symptoms than the nondepressed group would after controlling for age, gender, BMI, comorbidities, and medications. Hierarchical multiple regression analysis with enter method was used to test hypothesis 2, that physical symptoms would be associated with HRQOL after controlling for age, gender, comorbidities, NYHA functional class, and depressive symptoms. Cox regression analysis with enter method was used to test hypothesis 3, that physical symptoms would predict event-free survival after controlling for age, gender, BMI, comorbidities, NYHA functional class, LVEF, medications, and depressive symptoms. In Cox regression analysis, patients were divided into 2 groups based on their mean score on physical symptoms (a less severe physical symptom group, ≤24, and a more severe physical symptom group, >24). We used the mean because the data showed normal distribution.

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Results

Sociodemographic and Clinical Characteristics and Symptoms

A total of 249 patients with HF participated in the study (Table 1). More whites than nonwhites belonged to the more severe physical symptom group. The more severe symptom group also had a lower educational level, higher BMI, more comorbidities, more impaired functional status, and more depressive symptoms than did the less severe physical symptom group. There were 105 patients (42%) who experienced an adverse event during the follow-up period: 91 of the first events were hospitalizations, 8 were emergency department visits, and 6 were deaths. The mean (SD) score on the SSQ-HF was 24 (16).

TABLE 1

TABLE 1

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Reliability and Item Homogeneity

Cronbach’s α for the instrument was .80, indicating adequate internal consistency. In the item-total correlation analysis, the correlation coefficients of all the items were greater than .30, indicating adequate contribution of all items to the measure (Table 2). The results of the interitem correlations are presented in Table 2. At least half of the correlation coefficients between all individual items and all other items were greater than .30 and less than .70, and all the correlation coefficients were less than .70.

TABLE 2

TABLE 2

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Validity

In common factor analysis, the scree plot showed that the eigenvalue of 1 component was greater than 1.00, and 1 component was extracted and explained 46% of the total variance (Table 3). All of the items demonstrated moderate to strong loadings (>0.40), indicating acceptable construct validity.

TABLE 3

TABLE 3

Hypothesis 1 was supported: The depressed group had more severe physical symptoms than the nondepressed group did after controlling for age, gender, BMI, comorbidities, and medications (Table 4). Among the covariates, BMI and comorbidities were significantly associated with physical symptoms. Patients with depressive symptoms, greater BMI, and more comorbidities had more severe physical symptoms.

TABLE 4

TABLE 4

Hypothesis 2 was also supported: Physical symptoms were associated with HRQOL after controlling for age, gender, comorbidities, NYHA functional class, and depressive symptoms (Table 5). Among the covariates, age and depressive symptoms were associated with HRQOL. Patients with more severe physical symptoms, younger age, and depressive symptoms had poorer HRQOL.

TABLE 5

TABLE 5

Finally, hypothesis 3 was supported: Physical symptoms predicted event-free survival after controlling for age, gender, BMI, comorbidities, NYHA functional class, LVEF, medications, and depressive symptoms (Table 6). Among the covariates, comorbidities, LVEF, and ACEIs or ARBs predicted event-free survival. More severe physical symptoms, more comorbidities, lower LVEF, and not taking ACEIs or ARBs were associated with shorter event-free survival. The Figure shows the survival curves for prediction of event-free survival between less and more severe physical symptom groups.

TABLE 6

TABLE 6

FIGURE

FIGURE

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Discussion

This study supported the reliability and validity of the SSQ-HF. The findings of Cronbach’s α and item analyses showed acceptable internal consistency reliability and item homogeneity. All of the items contributed to the measure, and there was no redundancy among the items. Factor analysis and hypothesis tests strongly supported the construct validity of the instrument. Unlike the MSAS, the MSAS–short form, and the MSAS-HF,10–12 this instrument includes only common HF-related physical symptoms, but not psychological symptoms and other symptoms that are not common in this population. Thus, this instrument is relatively short compared with the instruments, but physical symptoms measured by this instrument were strongly associated with HRQOL and predicted event-free survival. Physical symptoms alone explained 56.4% of the variance in HRQOL in the current study. In this population, psychological symptoms, including depressive symptoms, are commonly included in studies and are measured by several reliable and valid instruments such as the Beck Depression Inventory II and the Patient Health Questionnaire-9.27,38,39,41 Thus, it is beneficial to provide a reliable and valid instrument to measure only physical symptoms. Compared with the Dyspnea-Fatigue Index,8,15 this instrument is relatively long but covers important common HF-related physical symptoms and can measure only physical symptoms separated from functional impairment. Functional impairment is commonly measured using NYHA functional class in HF studies and clinics.12,48 The SSQ-HF allows clinicians and researchers to assess the status of common physical symptoms and the unique effects on patient outcomes, including HRQOL and hospitalizations, among patients with HF.

The findings of the current study not only support the reliability and validity of this instrument but also demonstrate the importance of physical symptoms for patient outcomes. The findings of significant relationships between physical symptoms and depressive symptoms and between physical symptoms and HRQOL in the current study are consistent with the findings of previous studies.1,7,11,19 In patients with HF, poor HRQOL is an important issue and outcome.52 Some studies have examined physical symptoms after patients were hospitalized or visited emergency departments, and most hospitalized patients had common HF-related physical symptoms.5,9 For instance, 88% and 35% of patients who visited emergency departments had dyspnea and chest discomfort, respectively.9 In ambulatory and hospitalized HF patients, more than 90% had dyspnea.5 However, the predictive relationship of physical symptoms to hospitalization and mortality rates has rarely been examined in patients with HF. This study has shown that physical symptoms are a predictor of event-free survival. The findings of the relationships of physical symptoms to HRQOL and event-free survival imply that physical symptoms should be assessed and managed to improve HRQOL and event-free survival.

In this study, as expected, depressive symptoms are the strongest factor associated with physical symptoms. This finding was consistent with the findings of our previous and other stuies.8,11,19 Depressive symptoms measured by the Brief Symptom Invetory8,19 and the Geriatric Depression Scale–Short Form11 were associated with number of symptoms, overall symptom distress, and symptoms combined with functional impairment. These findings demonstrate that depressive symptoms are strongly associated with physical symptoms regardless of the instruments. Thus, depressive symptoms should be considered to manage physical symptoms properly.

Different types of interventions have been provided to improve depressive symptoms and physical symptoms, and the findings were inconsistent. Some interventions using a telehealth communication device, a small-group education program, including diet and exercise, or a comprehensive discharge program did not improve depressive symptoms.53–55 In contrast, a multidisciplinary comprehensive management program, including education about disease and symptom management, dietary counseling, adjustment of medications, and participation in an exercise program, improved depressive symptoms and reduced hospitalization rates.56 However, this study was not a randomized controlled trial. A home-based disease management program and a mindfulness-based psychoeducational program improved physical and depressive symptoms, but the effects were not profound.57,58 For instance, the home-based disease management program improved depressive symptoms, fatigue, and swelling but did not improve the prevalence of dyspnea.58 The mindfulness-based psychoeducational program improved physical symptoms and depressive symptoms, but the effects were not profound (P = .03 and .05, respectively).57 In the sessions of this study, mastering mindfulness skills and coping skills and group support were targeted. Various topics related to HF and its management, including diet, exercise, stress, communication, spirituality, health, and social support, were discussed during the sessions. The findings of these studies suggest that comprehensive management programs, including situational-specific education and counseling and psychological alternative therapy, may improve physical and depressive symptoms and, in turn, reduce hospitalization rates. However, further studies are needed to make more profound effects on physical and depressive symptoms.

Caution is needed in generalizing the findings of the current study to races other than whites because most of the participants were white. With other races, symptoms and the effects on HRQOL and event-free survival may be different from those reported in the current study. Also necessitating caution in interpretation of the data is the use of symptom recall during the past 4 weeks. Although this may present a difficulty for patients with HF, gathering data across a longer timespan can provide valuable data on symptom frequency.

In conclusion, the outcomes of our study provide preliminary evidence that clinicians and researchers who work with patients with HF can use the SSQ-HF to assess common HF-related physical symptoms and evaluate their effects on patient outcomes. Patients can easily fill out the questionnaire within 5 minutes after a brief explanation facilitating use during clinic visits. This instrument shows the presence, frequency, severity, and distress of 7 physical symptoms. Thus, it does not take much time for clinicians to review but provides more information about symptoms than NYHA functional class does. Further studies are needed to assess changes in symptoms over time and to determine whether these changes are associated with changes in HRQOL and event-free survival.

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What’s New and Important

  • This study presents a symptom instrument that clinicians and researchers can use to assess common physical symptoms in patients with HF.
  • This study provides information about factors associating with physical symptoms in patients with HF.
  • This study provides information about the predictive effects of physical symptoms on event-free survival.
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REFERENCES

1. Heo S, Moser DK, Lennie TA, Zambroski CH, Chung ML. A comparison of health-related quality of life between older adults with heart failure and healthy older adults. Heart Lung. 2007; 36: 16–24.
2. Juenger J, Schellberg D, Kraemer S, et al. Health related quality of life in patients with congestive heart failure: comparison with other chronic diseases and relation to functional variables. Heart. 2002; 87: 235–241.
3. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012; 125: e2–e220.
4. Cowie MR, Fox KF, Wood DA, et al. Hospitalization of patients with heart failure: a population-based study. Eur Heart J. 2002; 23: 877–885.
5. Albert N, Trochelman K, Li J, Lin S. Signs and symptoms of heart failure: are you asking the right questions? Am J Crit Care. 2010; 19: 443–452.
6. Carlson B, Riegel B, Moser DK. Self-care abilities of patients with heart failure. Heart Lung. 2001; 30: 351–359.
7. Heo S, Moser DK, Riegel B, Hall LA, Christman N. Testing a published model of health-related quality of life in heart failure. J Card Fail. 2005; 11: 372–379.
8. Heo S, Doering LV, Widener J, Moser DK. Predictors and effect of physical symptom status on health-related quality of life in patients with heart failure. Am J Crit Care. 2008; 17: 124–132.
9. Welsh JD, Heiser RM, Schooler MP, et al. Characteristics and treatment of patients with heart failure in the emergency department. J Emerg Nurs. 2002; 28: 126–131.
10. Blinderman CD, Homel P, Billings JA, Portenoy RK, Tennstedt SL. Symptom distress and quality of life in patients with advanced congestive heart failure. J Pain Symptom Manage. 2008; 35: 594–603.
11. Bekelman DB, Havranek EP, Becker DM, et al. Symptoms, depression, and quality of life in patients with heart failure. J Card Fail. 2007; 13: 643–648.
12. Zambroski CH, Moser DK, Bhat G, Ziegler C. Impact of symptom prevalence and symptom burden on quality of life in patients with heart failure. Eur J Cardiovasc Nurs. 2005; 4: 198–206.
13. Jurgens CY, Fain JA, Riegel B. Psychometric testing of the heart failure somatic awareness scale. J Cardiovasc Nurs. 2006; 21: 95–102.
14. Altice NF, Madigan EA. Factors associated with delayed care-seeking in hospitalized patients with heart failure. Heart Lung. 2012; 41: 244–254.
15. Feinstein AR, Fisher MB, Pigeon JG. Changes in dyspnea-fatigue ratings as indicators of quality of life in the treatment of congestive heart failure. Am J Cardiol. 1989; 64: 50–55.
16. 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: 1245–1255.
17. Portenoy RK, Thaler HT, Kornblith AB, et al. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur J Cancer. 1994; 30A: 1326–1336.
18. Browall M, Sarenmalm EK, Nasic S, Wengstrom Y, Gaston-Johansson F. Validity and reliability of the Swedish Version of the Memorial Symptom Assessment Scale (MSAS): an instrument for the evaluation of symptom prevalence, characteristics, and distress. J Pain Symptom Manage. 2013; 46: 131–141.
19. De Jong M, Moser DK, Chung ML. Predictors of health status for heart failure patients. Prog Cardiovasc Nurs. 2005; 20: 155–162.
20. Barsheshet A, Brenyo A, Goldenberg I, Moss AJ. Sex-related differences in patients’ responses to heart failure therapy. Nat Rev Cardiol. 2012; 9: 234–242.
21. Dickstein K, Cohen-Solal A, Filippatos G, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2008: the Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2008 of the European Society of Cardiology: developed in collaboration with the Heart Failure Association of the ESC (HFA) and endorsed by the European Society of Intensive Care Medicine (ESICM). Eur Heart J. 2008; 29: 2388–2442.
22. Dei Cas L, Metra M, Nodari S, Dei Cas A, Gheorghiade M. Prevention and management of chronic heart failure in patients at risk. Am J Cardiol. 2003; 91: 10F–17F.
23. Hunt SA, Abraham WT, Chin MH, et al. 2009 Focused update incorporated into the ACC/AHA 2005 guidelines for the diagnosis and management of heart failure in adults a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines developed in collaboration with the International Society for Heart and Lung Transplantation. J Am Coll Cardiol. 2009; 53: e1–e90.
24. Lindenfeld J, Albert NM, Boehmer JP, et al. Executive summary: HFSA 2010 comprehensive heart failure practice guideline. J Card Fail. 2010; 16: 475–510.
25. Riedinger MS, Dracup KA, Brecht ML, Padilla G, Sarna L, Ganz PA. Quality of life in patients with heart failure: do gender differences exist? Heart Lung. 2001; 30: 105–116.
26. Heo S, Moser DK, Chung ML, Lennie TA. Social status, health-related quality of life, and event-free survival in patients with heart failure. Eur J Cardiovasc Nurs. 2012; 11: 141–149.
27. Heo S, Moser DK, Pressler SJ, et al. Dose-dependent relationship of physical and depressive symptoms with health-related quality of life in patients with heart failure. Eur J Cardiovasc Nurs. 2013; 12: 454–460.
28. Haass M, Kitzman DW, Anand IS, et al. Body mass index and adverse cardiovascular outcomes in heart failure patients with preserved ejection fraction: results from the Irbesartan in Heart Failure With Preserved Ejection Fraction (I-PRESERVE) trial. Circ Heart Fail. 2011; 4: 324–331.
29. Braunstein JB, Anderson GF, Gerstenblith G, et al. Noncardiac comorbidity increases preventable hospitalizations and mortality among Medicare beneficiaries with chronic heart failure. J Am Col Cardiol. 2003; 42: 1226–1233.
30. Feola M, Aspromonte N, Milani L, et al. Plasma brain natriuretic peptide predicts short-term clinical outcome in heart failure patients with restrictive filling pattern. J Card Fail. 2008; 14: 420–425.
31. Gomez-Soto FM, Romero SP, Bernal JA, et al. Mortality and morbidity of non-systolic heart failure treated with angiotensin-converting enzyme inhibitors: a propensity-adjusted case-control study. Int J Cardiol. 2010; 139: 276–282.
32. Shibata MC, Flather MD, Wang D. Systematic review of the impact of beta blockers on mortality and hospital admissions in heart failure. Eur J Heart Fail. 2001; 3: 351–357.
33. Ahmed A, Husain A, Love TE, et al. Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods. Eur Heart J. 2006; 27: 1431–1439.
34. Faris R, Purcell H, Henein MY, Coats AJ. Clinical depression is common and significantly associated with reduced survival in patients with non-ischaemic heart failure. Eur J Heart Fail. 2002; 4: 541–551.
35. Song EK, Moser DK, Frazier SK, Heo S, Chung ML, Lennie TA. Depressive symptoms affect the relationship of n-terminal pro b-type natriuretic peptide to cardiac event-free survival in patients with heart failure. J Card Fail. 2010; 16: 572–578.
36. Nunnally JC, Bernstein IH. Psychometric Theory. New York, NY: McGraw-Hill; 1994.
37. Pett MA, Lackey NR, Sullivan JJ. Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research. Thousand Oaks, CA: Sage Publications; 2003.
38. Storch EA, Roberti JW, Roth DA. Factor structure, concurrent validity, and internal consistency of the beck depression inventory-second edition in a sample of college students. Depress Anxiety. 2004; 19: 187–189.
39. Beck AT, Steer RA, Ball R, Ranieri W. Comparison of beck depression inventories-IA and -II in psychiatric outpatients. J Pers Assess. 1996; 67: 588–597.
40. Frasure-Smith N, Lesperance F, Irwin MR, Talajic M, Pollock BG. The relationships among heart rate variability, inflammatory markers and depression in coronary heart disease patients. Brain Behav Immun. 2009; 23: 1140–1147.
41. Hammash MH, Hall LA, Lennie TA, et al. Psychometrics of the PHQ-9 as a measure of depressive symptoms in patients with heart failure. Eur J Cardiovasc Nurs. 2013; 12: 446–453.
42. Scott LD. Caregiving and care receiving among a technologically dependent heart failure population. ANS Adv Nurs Sci. 2000; 23: 82–97.
43. Rector TS, Cohn JN. Assessment of patient outcome with the Minnesota Living With Heart Failure Questionnaire: reliability and validity during a randomized, double-blind, placebo-controlled trial of pimobendan. Pimobendan Multicenter Research Group. Am Heart J. 1992; 124: 1017–1025.
44. Alla F, Briancon S, Guillemin F, et al. Self-rating of quality of life provides additional prognostic information in heart failure: insights into the epical study. Eur J Heart Fail. 2002; 4: 337–343.
45. Moser DK, Yamokoski L, Sun JL, et al. Improvement in health-related quality of life after hospitalization predicts event-free survival in patients with advanced heart failure. J Card Fail. 2009; 15: 763–769.
46. Heo S, Moser DK, Riegel B, Hall LA, Christman N. Testing the psychometric properties of the Minnesota Living With Heart Failure Questionnaire. Nurs Res. 2005; 54: 265–272.
47. Riegel B, Moser DK, Glaser D, et al. The Minnesota Living With Heart Failure Questionnaire: sensitivity to differences and responsiveness to intervention intensity in a clinical population. Nurs Res. 2002; 51: 209–218.
48. Bennett JA, Riegel B, Bittner V, Nichols J. Validity and reliability of the NYHA classes for measuring research outcomes in patients with cardiac disease. Heart Lung. 2002; 31: 262–270.
49. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987; 40: 373–383.
50. Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to Their Development and Use. New York, NY: Oxford University Press; 2001.
51. Ferketich S. Focus on psychometrics: aspects of item analysis. Res Nurs Health. 1991; 14: 165–168.
52. Lewis EF, Johnson PA, Johnson W, Collins C, Griffin L, Stevenson LW. Preferences for quality of life or survival expressed by patients with heart failure. J Heart Lung Transplant. 2001; 20: 1016–1024.
53. LaFramboise LM, Todero CM, Zimmerman L, Agrawal S. Comparison of health buddy with traditional approaches to heart failure management. Fam Community Health. 2003; 26: 275–288.
54. Miche E, Herrmann G, Wirtz U, Laki H, Barth M, Radzewitz A. Effects of education, self-care instruction and physical exercise on patients with chronic heart failure. Z Kardiol. 2003; 92: 985–993.
55. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow-up of hospitalized elders: a randomized clinical trial. JAMA. 1999; 281: 613–620.
56. Holst DP, Kaye D, Richardson M, et al. Improved outcomes from a comprehensive management system for heart failure. Eur J Heart Fail. 2001; 3: 619–625.
57. Sullivan MJ, Wood L, Terry J, et al. The support, education, and research in chronic heart failure study (search): a mindfulness-based psychoeducational intervention improves depression and clinical symptoms in patients with chronic heart failure. Am Heart J. 2009; 157: 84–90.
58. Todero CM, LaFramboise LM, Zimmerman LM. Symptom status and quality-of-life outcomes of home-based disease management program for heart failure patients. Outcomes Manag. 2002; 6: 161–168.
Keywords:

heart failure; instrument; symptoms

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