Secondary Logo

Journal Logo

The Associations of Diagnoses of Fatigue and Depression With Use of Medical Services in Patients With Heart Failure

Heo, Seongkum, PhD, RN; McSweeney, Jean, PhD, RN; Tsai, Pao-Feng, PhD, RN; Ounpraseuth, Songthip, PhD; Moser, Debra K., PhD, RN, FAAN; Kim, JinShil, PhD, RN

Journal of Cardiovascular Nursing: July/August 2019 - Volume 34 - Issue 4 - p 289–296
doi: 10.1097/JCN.0000000000000574
Heart Failure
Free

Background: Fatigue and depression based on self-report and diagnosis are prevalent in patients with heart failure and adversely affect high rates of hospitalization and emergency department visits, which can impact use of medical services. The relationships of fatigue and depression to use of medical services in patients with preserved and reduced left ventricular ejection fraction (LVEF) may differ.

Purpose: We examined the associations of diagnoses of fatigue and depression with use of medical services in patients with preserved and reduced LVEF, controlling for covariates.

Methods: Data were collected on fatigue, depression, covariates, and use of medical services. Patients (N = 582) were divided into 2 groups based on LVEF (<40%, reduced LVEF; ≥40%, preserved LVEF). Multiple linear regression analyses were used to analyze the data.

Results: A diagnosis of fatigue was a significant factor associated with more use of medical services in the total sample (β = .18, P < .001, R2 = 54%) and patients with reduced LVEF (β = .13, P = .008, R2 = 54%) and also preserved LVEF (β = .21, P < .001, R2 = 54%), controlling for all covariates, but a diagnosis of depression was not.

Conclusions: This study demonstrates the important roles of a diagnosis of fatigue in use of medical services. Thus, fatigue needs to be assessed, diagnosed, and managed effectively.

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

Jean McSweeney, PhD, RN Professor and Associate Dean for Research, College of Nursing, University of Arkansas for Medical Sciences, Little Rock.

Pao-Feng Tsai, PhD, RN Professor, College of Nursing, University of Arkansas for Medical Sciences, Little Rock.

Songthip Ounpraseuth, PhD Associate Professor, College of Public Health, University of Arkansas for Medical Sciences, Little Rock.

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

JinShil Kim, PhD, RN Professor, College of Nursing, Gachon University, Incheon, South Korea.

The project described was supported by the Translational Research Institute grant UL1TR000039 through the National Institutes of Health's National Center for Research Resources and National Center for Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The authors have no conflicts of interest to disclose.

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

Online date: May 16, 2019

Heart failure (HF) is a high-cost clinical condition. Costs associated with HF in the United States have increased from $23 billion in 2002 to $31 billion in 2012,1,2 and they are estimated to reach approximately $70 billion by 2030.2 Use of medical services has been associated with high costs in this population. In the United States, 1 factor contributing to total medical costs the most was inpatient costs (47%).3 In Europe, the main sources of total costs in patients with HF were hospitalization (39%) and outpatient care (20%).4 In the study, approximately 31% of patients were admitted to hospitals unexpectedly, and 53% visited emergency departments at least once.4 In addition, all-cause or HF-related hospitalization was significantly associated with higher costs.4

Two factors that may affect use of medical services and costs through their effects on hospitalization are fatigue and depression, which are the most common and burdensome physical and psychological symptoms in patients with HF.5–8 Approximately 80% to 94% of patients with HF experience fatigue5,6 and reported that fatigue was one of the worst HF symptoms and less improved over time.9 Approximately 30% to 50% of patients with HF have depression or depressive symptoms7,8 (hereafter the term “depression” will be used for both depression and depressive symptoms). Both fatigue and depression based on self-report have been known to adversely affect hospitalization and mortality. Fatigue based on self-report and diagnosis has been significantly associated with a higher number of hospitalizations and higher mortality risk scores.10,11 In addition, depression based on self-report and diagnosis has been associated with shorter time to hospitalization, emergency department visit, or mortality,12 or a higher number of hospitalizations.11 Both in the United States and Europe, hospitalization has been the largest contributor to medical costs in HF.3,4 Thus, both fatigue and depression can impact high use of medical services. However, the relationships, especially based on diagnosis, have not been examined controlling for typical covariates of high hospitalization, which can lead to high costs. Diagnosis of fatigue or depression can be identified by clinicians easily through medical records for effective management. Thus, it is valuable to examine the relationships of fatigue and depression based on diagnoses to use of medical services.

In addition to fatigue and depression, some demographic characteristics (eg, age and gender)7,13 and some clinical characteristics (eg, body mass index, comorbidities, and New York Heart Association functional class)14,15 are significantly associated with HF symptoms and/or depression. Some demographic characteristics (eg, age, gender, and ethnicity),16,17 clinical characteristics (eg, comorbidities, left ventricular ejection fraction [LVEF], and medications),14,17,18 vital signs (eg, blood pressure [BP] and heart rate),16 and laboratory tests (eg, creatinine, sodium, hemoglobin, and troponin-1)17,18 are associated with hospitalization or mortality. Thus, these factors can impact use of medical services through their adverse effects on HF symptoms, depression, and/or hospitalizations or mortality (Figure).

FIGURE

FIGURE

Therefore, the purpose of this study was to examine the associations of diagnoses of fatigue and depression with use of medical services in patients with HF, controlling for traditional covariates and also numbers of hospitalizations and emergency department visits and length of stay, which are the strongest contributors to high costs.

Back to Top | Article Outline

Methods

Study Design, Setting, and Procedure

The study design, setting, and procedure have been reported elsewhere.11 This was a cross-sectional, secondary analysis study using data from the Enterprise Data Warehouse of a university in the southern region of the United States. The study was approved by the university's institutional review board. The Enterprise Data Warehouse team determined patients with HF as their primary or secondary diagnosis based on International Classification of Diseases, Ninth Revision codes (428–428.9) between January 1, 2010, and December 31, 2012. Then, the team retrieved data on all the study variables of those patients with HF. The research team received 3 years of data on use of medical services based on Current Procedural Terminology codes, fatigue (780.71 and 780.79) and depression (296.2–296.36 and 311), based on International Classification of Diseases, Ninth Revision codes, demographic and clinical characteristics, vital signs, and laboratory tests from the medical record. The research conformed to the provisions of the Declaration of Helsinki as revised in Brazil in 2013.

Back to Top | Article Outline

Measures

Use of medical services was assessed by counting the number of medical services used for the 3-year period based on Current Procedural Terminology codes with no consideration of weight. Use of medical services included codes of evaluation and management services, surgery services, radiology services, pathology and laboratory services, and medicine services. Use of medical services did not include number of hospitalizations, emergency department visits, and length of stay. Because there were multiple data for body mass index, vital signs, laboratory tests, and clinical characteristics, a statistician who was one of the research team members calculated the mean of each of the variables for the 3-year period, and the means were used for data analyses. Hospitalizations and emergency department visits were assessed by counting numbers of hospitalizations and emergency department visits for the 3-year period, respectively. Length of stay was assessed by counting number of nights during the hospitalizations and emergency department visits for the 3-year period.

Back to Top | Article Outline

Data Analysis

Initially, sample characteristics were summarized using means and standard deviations for continuous variables and frequencies and percentages for categorical variables. t Test analyses were used to compare sample characteristics between patients with HF with reduced and preserved LVEF and between patients with HF with and without fatigue or depression. Multiple regression analyses with Enter method (all variables, including fatigue and depression, were entered into each model simultaneously) were used to determine factors associated with the number of all medical services used, including evaluation and management services, surgery services, radiology services, pathology and laboratory services, and medicine services, controlling for covariates, in the total sample and also 2 subgroups of patients with preserved LVEF and with reduced LVEF. Although hospitalization rates between patients with HF with preserved and reduced LVEF did not differ, factors associated with hospitalization rates in the groups differed.19 Inpatient costs considerably contributed to high costs, which implies the connection between hospitalization and more use of medical services. Thus, factors associated with use of medical services between patients with HF with preserved and reduced LVEF may differ. Thus, the analyses were conducted in the total sample and in the 2 subgroups. Two-tailed tests were used, and a P < .05 was set up as significant. All data analyses were conducted using Statistical Package for Social Sciences (version 24).20

Back to Top | Article Outline

Results

Sample Characteristics

Demographic and clinical characteristics, vital signs, and laboratory tests are presented in Table 1. In the total sample, the mean age was 63.2 (±14.4) years, and approximately half of them were male (54.5%) and white (51.2%) (Table 1). In the sample, 48.5% had HF with reduced LVEF, 45.4% had a diagnosis of fatigue, and 26.3% had a diagnosis of depression. Patients with reduced LVEF were younger and had lower body mass index and systolic BP, lower levels of blood creatinine and sodium, higher levels of blood hemoglobin, less use of medical services, fewer number of hospitalizations, and shorter lengths of stay than patients with HF with preserved LVEF. In addition, they were more frequently male and had fewer comorbidities and diagnosis of depression than their counterparts. Patients with a diagnosis of fatigue were older and had lower body mass index, lower systolic and diastolic BP, lower levels of blood monocytes and neutrophils, more use of medical services, more frequent hospitalizations, longer lengths of stay, and more frequent emergency department visits than patients without a diagnosis of fatigue. In addition, they were more frequently female and had more comorbidities and diagnosis of depression than their counterparts. Patients with a diagnosis of depression had lower systolic and diastolic BP, higher LVEF, more use of medical services, more frequent hospitalizations, longer lengths of stay, and more frequent emergency department visits than patients without a diagnosis of depression. In addition, they were more likely female and of white race and had cancer and fatigue than their counterparts.

TABLE 1

TABLE 1

Back to Top | Article Outline

Associations of Fatigue and Depression With Use of Medical Services

A diagnosis of fatigue was a significant factor associated with more use of medical services in the total sample (β = .18, P < .001), patients with reduced LVEF (β = .13, P = .008), and also patients with preserved LVEF (β = .21, P < .001), controlling for all covariates. However, a diagnosis of depression was not a significant factor associated with more use of medical services (Table 2). In the total sample, all the variables explained 54% of the variance in use of medical services. In patients with reduced LVEF, the model explained 54% of the variance in use of medical services. In patients with preserved LVEF, the model explained 54% of the variance in use of medical services.

TABLE 2

TABLE 2

Back to Top | Article Outline

Discussion

The findings of this study demonstrate the important role of a diagnosis of fatigue in use of medical services in patients with HF with reduced and also preserved LVEF. A diagnosis of fatigue was significantly associated with more use of medical services, even controlling for all typical covariates of demographic and clinical characteristics, vital signs, and laboratory tests, and also hospitalization, emergency department visit, and length of stay, which are major contributors for use of medical services in patients with HF. The β coefficients, which indicate the strength of the effects of individual predictor variable on the outcome variable21 of fatigue in the total sample and patients with preserved LVEF were comparable with those of cancer, length of stay, or emergency department visit and comparable with or slightly lower than those of hospitalization. Factors associated with use of medical services in patients with preserved LVEF and reduced LVEF were very similar, and each model explained a very similar amount of variance in use of medical services. Thus, to decrease use of medical services, a diagnosis of fatigue should be considered in patients with HF with both preserved and reduced LVEF. On the other hand, depression was not associated with use of medical services maybe because of the strong relationships of several independent variables, including fatigue, hospitalization, emergency department visits, length of stay, and cancer, to use of medical services.

In the literature, fatigue is one of the most common and distressing HF symptoms, and up to 80% to 90% of patients with HF reported that they experienced fatigue.5,6,22 In the current study, approximately half of the patients (45.4%) had a diagnosis of fatigue. Both self-reported and a diagnosis of fatigue have been associated with high hospitalization rates. Several studies have shown that self-reported fatigue or increased self-reported fatigue was significantly associated with high rates of hospitalization or mortality in this population.10,11,23 In addition, a diagnosis of fatigue also has been associated with a greater number of hospitalizations, controlling for typical covariates that were included in the current study except hospitalization, emergency department visits, and length of stay.11 In both Europe and the United States, hospitalization or inpatient costs have been associated with high costs.3,4 In the current study, as expected, all hospitalization, emergency department visits, and length of stay were associated with higher use of medical services in both patients with preserved and reduced LVEF, controlling for all typical covariates. More importantly, a diagnosis of fatigue was also associated with higher use of medical services in both patients with preserved and reduced LVEF, controlling for all typical demographic and clinical characteristics and laboratory tests and all hospitalization, emergency department visits, and length of stay. These findings imply that improvement in fatigue may reduce use of medical services. To improve fatigue, we need to assess and manage fatigue in both patients with preserved and reduced LVEF to reduce hospitalization rates, use of medical services, and, in turn, costs.

In addition, further research needs to be conducted to know whether prevalence of fatigue based on diagnosis and self-report matches each other. Because the current study was a secondary analysis, it was impossible to collect data on self-reported fatigue. The relationship between fatigue based on diagnosis and self-report rarely has been examined in patients with HF. However, mismatch among a diagnosis of depression, self-reported depressive symptoms, and the treatment has been well known. For example, the prevalence rate of depression based on diagnosis in medical records was 23.4%, prescription of antidepressants was 33%, and depressive symptoms based on self-report was 43.1%.24 The findings of the current study support the previous findings, with the prevalence rate of depression based on diagnosis being 26.3% and that of prescription of antidepressants being 41.4%. There may be a possibility that prevalence of fatigue based on self-report and diagnosis differs; thus, it may be meaningful to examine the prevalence of fatigue based on self-report and also diagnosis in patients with HF at the same time. If a diagnosis of fatigue is recorded in the medical record of the patient, clinicians can be involved in the management of fatigue to reduce hospitalization and use of medical services.

Despite high rates of prescriptions of HF medications (prescription of angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers and β-blockers, ≥80%), the prevalence of self-reported fatigue (85%) and hospitalization rates (62% within 1 year) still remain high.25 Although fatigue is a common HF symptom, and many patients with HF report fatigue, many of them did not recognize fatigue as an HF symptom or concern, which can lead to delayed treatment seeking.22,26 In addition, fatigue compared with dyspnea was less improved during hospitalization and also after discharge.9 Thus, more effective strategies are needed for patients and clinicians to assess and manage fatigue appropriately. Clinicians can help patients with HF assess HF symptoms, including fatigue, during the patients' regular outpatient clinic visits or hospital admissions using a reliable and valid instrument and then diagnose fatigue adequately. For instance, the Symptom Status Questionnaire-Heart Failure14 is a reliable and valid instrument assessing 7 common HF symptoms, including fatigue,14 and approximately less than 5 minutes are needed to fill it out.

Because fatigue has been very common in patients with HF and, compared with dyspnea, was less improved, more comprehensive and effective interventions are needed to improve fatigue. In the current study, although depression was not associated with use of medical services, depression and fatigue are commonly associated with each other14,27–30 Thus, to manage fatigue effectively, it may be better to manage fatigue and depression using more comprehensive interventions. For instance, some additional treatment components that can deal with both fatigue and depression, such as meditation combined with self-management, may be beneficial. For example, in a HF study,31 meditation combined with a psychoeducational component prevented worsening of HF symptoms and reduced depression at 6 months. In addition, mindful, compassionate meditation combined with self-management showed promising outcomes of reducing both HF symptoms and depressive symptoms.32 Meditation interventions also improved fatigue and/or depression in patients with breast cancer.33,34 In addition, a collaborative symptom and psychosocial care program provided by a team of a nurse, a social worker, and a cardiologist also improved depression and fatigue.35

This study has some limitations. Diagnoses of fatigue and depression and use of medical services were included if documented at any time of the 3-year period based on medical records. Thus, the cause-and-effect relationships could not be examined. In addition, rates of depression based on diagnosis and depressive symptoms based on questionnaires have differed.24 Thus, rates of depression based on diagnosis might differ from actual rates of depression. In addition, a diagnosis of fatigue also might not reflect the actual fatigue status if healthcare providers did not record the diagnosis to the medical records. Some somatic/affective symptoms of depression may have overlapped with fatigue, which could impact the relationships. However, the sample represents both genders and different races very well, which expands the generalizability. In addition, the findings of this study demonstrate the important roles of fatigue in use of medical services, controlling for all typical covariates of medical service uses.

Back to Top | Article Outline

Conclusions

This study demonstrates the important roles of a diagnosis of fatigue in use of medical services. Thus, fatigue needs to be assessed, diagnosed, and managed effectively. Further studies are needed to develop and test comprehensive interventions, which focus on both fatigue and depression because depression can impact fatigue, to improve these symptoms and, in turn, to reduce use of medical services in patients with HF.

Back to Top | Article Outline

What's New and Important

  • A diagnosis of fatigue was associated with higher use of medical services in patients with HF with preserved and also reduced LVEF, controlling for a variety of typical covariates.
  • A diagnosis of depression was not associated with higher use of medical services in any of the 2 subgroups, controlling for a variety of typical covariates.
  • Factors associated with use of medical services in both subgroups were very similar, including a diagnosis of fatigue, a diagnosis of cancer, a higher level of albumin, a lower level of hemoglobin, more frequent hospitalizations and emergency department visits, and longer lengths of stay.
Back to Top | Article Outline

REFERENCES

1. American Heart Association. 2002 Heart and Stroke Statistical Update. Dallas, TX: American Heart Association; 2001.
2. Benjamin EJ, Virani SS, Callaway CW. Heart disease and stroke statistics—2018 update: a report from the American Heart Association. Circulation. 2018;137:e67–e492.
3. Echouffo-Tcheugui JB, Bishu KG, Fonarow GC, Egede LE. Trends in health care expenditure among US adults with heart failure: the Medical Expenditure Panel Survey 2002–2011. Am Heart J. 2017;186:63–72.
4. Farre N, Vela E, Cleries M, et al. Medical resource use and expenditure in patients with chronic heart failure: a population-based analysis of 88 195 patients. Eur J Heart Fail. 2016;18:1132–1140.
5. Zaharias E, Cataldo J, Mackin L, Howie-Esquivel J. Simple measures of function and symptoms in hospitalized heart failure patients predict short-term cardiac event-free survival. Nurs Res Pract. 2014;2014:815984.
6. Janssen DJ, Spruit MA, Uszko-Lencer NH, Schols JM, Wouters EF. Symptoms, comorbidities, and health care in advanced chronic obstructive pulmonary disease or chronic heart failure. J Palliat Med. 2011;14:735–743.
7. Gottlieb SS, Khatta M, Friedmann E, et al. The influence of age, gender, and race on the prevalence of depression in heart failure patients. J Am Coll Cardiol. 2004;43:1542–1549.
8. Johnson TJ, Basu S, Pisani BA, et al. Depression predicts repeated heart failure hospitalizations. J Card Fail. 2012;18:246–252.
9. Kato M, Stevenson LW, Palardy M, et al. The worst symptom as defined by patients during heart failure hospitalization: implications for response to therapy. J Card Fail. 2012;18:524–533.
10. Fink AM, Gonzalez RC, Lisowski T, et al. Fatigue, inflammation, and projected mortality in heart failure. J Card Fail. 2012;18:711–716.
11. Heo S, McSweeney J, Tsai PF, Ounpraseuth S. Differing effects of fatigue and depression on hospitalizations in men and women with heart failure. Am J Crit Care. 2016;25:526–534.
12. Wu JR, Lennie TA, Dekker RL, Biddle MJ, Moser DK. Medication adherence, depressive symptoms, and cardiac event-free survival in patients with heart failure. J Card Fail. 2013;19:317–324.
13. 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.
14. Heo S, Moser DK, Pressler SJ, Dunbar SB, Mudd-Martin G, Lennie TA. Psychometric properties of the Symptom Status Questionnaire-Heart Failure. J Cardiovasc Nurs. 2015;30:136–144.
15. Eastwood JA, Moser DK, Riegel BJ, et al. Commonalities and differences in correlates of depressive symptoms in men and women with heart failure. Eur J Cardiovasc Nurs. 2012;11:356–365.
16. Crowder RS, Irons BK, Meyerrose G, Seifert CF. Factors associated with increased hospital utilization in patients with heart failure and preserved ejection fraction. Pharmacotherapy. 2010;30:646–653.
17. Ross JS, Mulvey GK, Stauffer B, et al. Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med. 2008;168:1371–1386.
18. Ingle L, Rigby AS, Carroll S, et al. Prognostic value of the 6 min walk test and self-perceived symptom severity in older patients with chronic heart failure. Eur Heart J. 2007;28:560–568.
19. Mangla A, Kane J, Beaty E, Richardson D, Powell LH, Calvin JE Jr. Comparison of predictors of heart failure-related hospitalization or death in patients with versus without preserved left ventricular ejection fraction. Am J Cardiol. 2013;112:1907–1912.
20. IBM SPSS Statistics for Windows [computer program]. Version 24.0. Armonk, NY: IBM Corp; 2016.
21. Montgomery DC, Peck EA, Vining GG. Introduction to Linear Regression Analysis. 5th ed. Hoboken, NJ: John Wiley & Sons Inc; 2012.
22. Jurgens CY, Hoke L, Byrnes J, Riegel B. Why do elders delay responding to heart failure symptoms? Nurs Res. 2009;58:274–282.
23. Perez-Moreno AC, Jhund PS, Macdonald MR, et al. Fatigue as a predictor of outcome in patients with heart failure: analysis of CORONA (Controlled Rosuvastatin Multinational Trial in Heart Failure). JACC Heart Fail. 2014;2:187–197.
24. Jiménez JA, Redwine LL, Rutledge TR, et al. Depression ratings and antidepressant use among outpatient heart failure patients: implications for the screening and treatment of depression. Int J Psychiatry Med. 2012;44:315–334.
25. 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.
26. Reeder KM, Ercole PM, Peek GM, Smith CE. Symptom perceptions and self-care behaviors in patients who self-manage heart failure. J Cardiovasc Nurs. 2015;30:E1–E7.
27. Fan X, Meng Z. The mutual association between depressive symptoms and dyspnea in Chinese patients with chronic heart failure. Eur J Cardiovasc Nurs. 2015;14:310–316.
28. Williams BA. The clinical epidemiology of fatigue in newly diagnosed heart failure. BMC Cardiovasc Disord. 2017;17:122.
29. Tang WR, Yu CY, Yeh SJ. Fatigue and its related factors in patients with chronic heart failure. J Clin Nurs. 2010;19:69–78.
30. Mills PJ, Wilson K, Iqbal N, et al. Depressive symptoms and spiritual wellbeing in asymptomatic heart failure patients. J Behav Med. 2015;38:407–415.
31. 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.
32. Heo S, McSweeney J, Ounpraseuth S, Shaw-Devine A, Fier A, Moser DK. Testing a holistic meditation intervention to address psychosocial distress in patients with heart failure: a pilot study. J Cardiovasc Nurs. 2018;33:126–134.
33. Bower JE, Crosswell AD, Stanton AL, et al. Mindfulness meditation for younger breast cancer survivors: a randomized controlled trial. Cancer. 2015;121:1231–1240.
34. Garlick M, Wall K, Corwin D, Koopman C. Psycho-spiritual integrative therapy for women with primary breast cancer. J Clin Psychol Med Settings. 2011;18:78–90.
35. Bekelman DB, Allen LA, McBryde CF, et al. Effect of a collaborative care intervention vs usual care on health status of patients with chronic heart failure: the CASA randomized clinical trial. JAMA Intern Med. 2018;178:511–519.
Keywords:

depression; health services; heart failure; symptoms

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved