Quality of Life, Hope, Social Support, and Self-Care in heart failure patients

Quality of Life, Hope, Social Support, and Self-Care in heart failure patients

Research Abstracts Understanding the variables that impact the quality of life can help clinicians target interventions to support and maximize QOL a...

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Research Abstracts

Understanding the variables that impact the quality of life can help clinicians target interventions to support and maximize QOL among individuals with CHF. Methods: A cross-sectional exploratory design was used. Specific study aims were to determine (a) whether there were differences in self-reported health status and HRQOL of men and women with CHF, (b) whether the variables of age, race/ethnicity, living status, number of comorbidities, perceived general health status, and perceived symptom management differ by gender, and (c) whether gender, age, race, number of comorbidities, living status, perceived general health status, perceived symptom management perceived physical health status, and perceived mental health status predict HRQOL. A non-probability convenience sample of 113 participants, who met the criteria for participation, were recruited from two cardiac clinics. Results: There were no significant differences among gender categories on the variables of health status and HRQOL. Independent samples t-test showed no significant differences in age, total number of comorbidities, perceived general health status, and perceived symptoms management by gender. Chi-square test results revealed that gender and living status were independent, as were gender and race/ethnicity. Hierarchical regression analysis showed all predictors for each hypothesized dependent variable in the model contributed to the outcome. Perceived symptoms management was a direct predictor of functional status, and its prediction of the mental health component of functional status represented the model’s highest relationship. The model’s only nonsignificant path was from comorbidities to HRQOL. The variables of perceived general health status, physical health status, mental health status, and perceived symptom management contributed to the HRQOL outcome when age, but not comorbidities, was the exogenous variable. Using kappa, agreement regarding functional status was only 44% between patients and their healthcare providers, adjusted to 37% for chance agreement. Conclusion: The findings from this study support the conclusions from previous research that HRQOL among those with congestive heart failure (CHF) is impacted by numerous variables. Information from this exploratory study increases the support for the idea that changes made in symptom management can bring about positive changes in physical and emotional wellbeing, perceived general health status, and, ultimately, HRQOL. The results of model testing provide health care providers with information that can be used in guiding treatment approaches to help improve QOL.

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QUALITY OF LIFE, HOPE, SOCIAL SUPPORT, AND SELF-CARE IN HEART FAILURE PATIENTS K.A. McGurk, Nursing, University of San Diego, San Diego, CA Purpose: The purpose of this study was twofold. First, it filled a gap in the heart failure and nursing literature by describing Quality of Life and the psychosocial variables of Hope, Social Support, and Self-Care in heart failurepatients. Second, it tested the conceptual relationships between Quality of Life, Hope, Social Support, and Self-Carein this population. Background: Heart failure is a significant, chronic health problem that results in significant personal and economic burdens. Best practice guidelines have been developed in an effort to halt or slow this progressive condition. Yet, despite these interventions, heart failure remains the primary reason for hospital admissions and readmissions. Much is known about physiological factors related to this condition. Less is known about the psychosocial aspects that influence disease risk, progression, and treatment. This is especially true when the relationships between quality of life, hope, social support, and self-care are examined. Methods: A convenience sample of participants was recruited from a cohort of patients attending military-based heart failure clinics located in Southern California. Participants were members of the armed services or family members who qualified for health care at these centers. The Statistical Package for Social Sciences (SPSS) was used for statistical analysis. Descriptive statistics (measures of central tendency and variability) were calculated for the variables of quality of life, hope, social support, and selfcare. Pearson Product-Moment Correlations were used to examine the nature and degree of relationship between quality of life and continuous demographic variables. For categorical demographic variables, t-tests or analysis of variance (ANOVA) were conducted. Pearson Product-Moment Correlations were used to examine the nature and degree of the bivariate relationships among quality of life, hope, social support, and self-care. Multiple linear regression was conducted to examine the relationship between the dependent variable, quality of life, and the independent variables hope, social support, and self-care. Relevant demographic variables were included as covariates in the regression model. Results: Preliminary results reveal that Quality of life is inversely related to Hope (r = .346, p < 0.01) and is not correlated with Social Support (r = .172, p = .180) or Self-Care: self-care

JULY/AUGUST 2010

HEART & LUNG

Research Abstracts

maintenance score (r = .136, p = .317); selfcare management score (r = .026, p = .847); selfcare confidence (r = -.066, p = .605). Conclusion: Preliminary findings are provocative. Review of the literature reveals that most studies support a positive relationship between quality of life, hope, social support, and self-care. In this study, only hope was found to be correlated with quality of life and that relationship was negative! Statistical analysis continues and will be completed by January 31, 2010. This study will be submitted in partial fulfillment of the requirements for Doctor of Philosophy in Nursing at the University of San Diego, California.

HEART FAILURE MEDICATION USE AFTER MITRAL VALVE REPAIR: IS THERE ROOM FOR IMPROVEMENT? N.M. Albert, Nursing Research & Innovation and George M. and Linda H. Kaufman Center for Heart Failure, Cleveland Clinic, Cleveland, OH; C.M. Lewis, Heart Failure Program, Vanderbilt University Medical Center, Nashville, TN; M.T. Karafa, S.M. Morrison, Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH Purpose: This study examines use of core heart failure medications after mitral valve repair in patients with a baseline rejection fraction # 40% and changes in ejection fraction (pre-to-postoperation at discharge and at 1 year) in groups based on heart failure drug use. Background: Predictors of survival after mitral valve repair are ejection fraction, age, New York Heart Association functional class, history of coronary artery disease, serum creatinine level, systolic blood pressure, and severity of mitral regurgitation; however little information is available on conformity of use of core heart failure medications after mitral valve repair and the impact of heart failure therapies on left ventricular dysfunction. Methods: An institutional review board approved cardiovascular surgery registry was queried from 2005 through 2007 for patients with ejection fraction # 40% prior to mitral valve repair at a large Midwest center. Drug class data, patient demographics, surgical information and postoperative ejection fraction were extracted. Univariable and multivariable regression models were created to assess associations between patient characteristics and changes in ejection fraction from baseline to discharge and 1-yr follow-up.

HEART & LUNG VOL. 39, NO. 4

Results: In 375 patients, mean age was 66.0 ( 11.8) years, 64% were male, 86% were Caucasian, baseline mean ejection fraction was 29.0% ( 8.5) and 68.5% had coronary artery bypass and mitral valve repair. Median length of stay from surgery to discharge (25th, 75th interquartile days) was 10.0 (7.0, 15.0) days. From preoperation to post mitral valve repair discharge, core heart failure drug use decreased: Angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE-I/ARB) from 71% to 52.5%, P < 0.001; beta-blocker from 80% to 74%, P = 0.03; digitalis from 27.5% to 14%, P < 0.001; statin/lipid lowering drug use from 68% to 56%, P < 0.001; and ACEI/ARB + beta-blocker from 52.5% to 43.5%, P = 0.01. Mitral regurgitation severity, male gender, myocardial infarction history and preoperative statin/lipid lowering drug use were associated with greater reduction in ejection fraction from pre- mitral valve repair to discharge, all P # 0.04. After adjustment, male gender and preoperative statin/ lipid lowering agent use predicted change in ejection fraction from pre- mitral valve repair to discharge. Digitalis use at discharge was associated with an increase in ejection fraction at 1-year post mitral valve repair and remained significant after adjustment (parameter estimate, 7.34; 95% confidence intervals: 3.36, 10.71; P = 0.03). Conclusion: After mitral valve repair, core/related heart failure drug prescription rates were significantly lower than preoperative rates. By drug class, only statin and digoxin use were associated with discharge and 1-year postoperative ejection fraction, respectively. Since heart failure drugs improve survival and reduce hospitalization for worsening heart failure, greater focus on discharge prescription of core heart failure drugs is warranted, based on guideline-recommended heart failure therapies.

DO CENTRAL ADIPOSITY MEASURES PREDICT LIFESTYLE PATTERNS AND KNOWLEDGE IN OBESE PATIENTS WITH HEART FAILURE? N.M. Albert, Nursing Research & Innovation and George M. and Linda H. Kaufman Center for Heart Failure, Cleveland Clinic, Cleveland, OH; J.F. Bena, A.S. Tang, Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH; J. Forney, E. Slifcak, Nursing Research and Innovation, Cleveland Clinic, Cleveland, OH Purpose: To examine relationships between central adiposity measures (waist-to-hip ratio, waist circumference, and metabolic syndrome status) and

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