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Ann Thorac Surg 2004;77:1508-1513
© 2004 The Society of Thoracic Surgeons

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Original article: cardiovascular

Predictors of health-related quality of life after coronary artery bypass surgery

^a Cardiology and Health Services Research, Denver VA Medical Center, University of Colorado Health Sciences Center, Denver, Colorado, USA
^b Colorado Permanente Medical Group, Denver, Colorado, USA
^c Department of Preventive Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, Colorado, USA
^d Department of Preventive Medicine, Northwestern University School of Medicine, Chicago, Illinois, USA
^e Department of Surgery, University of Colorado Health Sciences Center, Denver, Colorado, USA

Accepted for publication October 8, 2003.

^* Address reprint requests to Dr Rumsfeld, Cardiology (111B), Denver VA Medical Center, 1055 Clermont St, Denver, CO 80220, USA
e-mail: john.rumsfeld{at}med.va.gov

	Abstract

Top Abstract Introduction Material and methods Results Comment Acknowledgments References

 
BACKGROUND: Little is known about the determinants of health-related quality of life after coronary artery bypass surgery. We determined the predictors of overall physical and mental health status 6 months after the operation.

METHODS: We evaluated 1,973 patients enrolled in a multicenter Veterans Affairs prospective cohort study who completed preoperative and 6-month postoperative Short Form-36 (SF-36) health status surveys. Multiple linear regression was used to identify the significant independent predictors of 6-month physical and mental component summary scores from the SF-36.

RESULTS: In multivariable analyses adjusting for baseline health status, significant predictors of postoperative physical health status were a history of neurologic disease, peripheral vascular disease, chronic obstructive pulmonary disease, hypertension, current smoking, forced expiratory volume, left ventricular ejection fraction, and serum creatinine. Significant predictors of postoperative mental health status were a history of psychiatric disease, chronic obstructive pulmonary disease, current smoking, age, and New York Heart Association functional class.

CONCLUSIONS: These predictors of health-related quality of life after coronary artery bypass surgery may be useful for preoperative risk assessment and counseling of patients with regard to anticipated health status outcomes. Factors such as current smoking and psychiatric disease may be targets for interventions to improve health-related quality of life outcomes.

	Introduction

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Traditionally, outcomes such as mortality or morbidity have been used to assess therapeutic interventions. Established predictors of these outcomes can be used to risk-stratify and counsel patients before interventions are undertaken. For example, the decision to recommend coronary artery bypass graft (CABG) surgery usually involves assessment of a patient's mortality risk from the procedure, and there are established risk factors for mortality with CABG surgery to help guide this decision [1–4].

Along with the substantial decline in operative mortality with CABG surgery over the past 30 years [5], there has been growing interest in health-related quality of life (HRQL) outcomes. Multiple studies have demonstrated that HRQL improves, on average, after CABG surgery [6–10]. However, this average improvement will not be realized for all patients [9, 10], and little is known about the predictors of HRQL outcomes after CABG surgery. Knowing the preoperative risk factors for poor HRQL outcomes should lead to enhanced patient selection and counseling. This is of particular import since many patients express a preference for quality of life over quantity of life [11]. Furthermore, identification of predictors of HRQL after CABG surgery may lead to the development of interventions to improve HRQL outcomes.

Using data from a large, prospective Veteran's Affairs (VA) cooperative study, we determined the predictors of overall physical and mental health status 6 months after CABG surgery. It is hoped that the results of this study will enhance clinical decision making before CABG surgery by expanding preoperative risk assessment to a broader range of outcomes that are important to patients.

	Material and methods

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Subjects
Patients were enrolled in the VA Cooperative Study in Health Services No. 5, Processes, Structures, and Outcomes of Care in Cardiac Surgery (PSOCS). Details of that study have been published previously, and are repeated here only as relevant to the current analyses [12]. This prospective, observational study included collection of more than 700 variables representing patient risk factors, processes, structures, and outcomes of care on a representative sample of patients undergoing cardiac surgery at 14 VA medical centers from September 1992 to December 1996. The study included both a preoperative and 6-month postoperative Short Form-36 (SF-36) health status survey assessment.

Data were prospectively collected by full-time, trained research nurses located at each of the 14 sites. Preoperative risk data were obtained by patient interview and chart review within 72 hours before surgery. The baseline SF-36 was given to the patients for self-administration within 72 hours of surgery. If a patient was unable to complete the baseline SF-36, personal interview administration was done by the research nurse. The postoperative SF-36 was given to patients at their 6-month follow-up visit. If patients missed this visit, the SF-36 was mailed to them. If not obtained by either of these methods, patients were contacted by a trained interviewer for telephone administration.

All PSOCS study patients who had CABG-only, completed a baseline SF-36 survey, and survived at least 6 months after the operation were eligible for this study. Of the 3,956 CABG-only patients, 2,480 completed a baseline SF-36 survey, and 2,363 survived at least 6 months after the operation. Of the 2,363 eligible patients, 1,973 (83.5%) completed a follow-up SF-36 survey. The primary reason for missing the baseline SF-36 was urgent or emergent surgery precluding time to obtain the survey. Consistent with this, patients who did not complete a preoperative survey were more likely to have Canadian Cardiovascular Society (CCS) class III or IV angina, were more likely to require preoperative intravenous nitroglycerin or intraaortic balloon pump, and were more likely to have a recent myocardial infarction (within 7 days). The results of this study are therefore referent to patients in whom it is logistically feasible to obtain a preoperative SF-36 (ie, largely elective patients). However, we did not exclude nonelective patients a priori, and 11% of the study population had urgent/emergent status. Compared with patients who completed the 6-month SF-36, patients who were alive but did not complete the survey were slightly younger, were more likely to be preoperative CCS class III or IV or preoperative New York Heart Association (NYHA) functional class III or IV, and were more likely to have a history of chronic obstructive pulmonary disease or psychiatric disease.

Variables
The dependent, or outcome, variables for this study were the physical component summary (PCS) and mental component summary (MCS) scores from the 6-month SF-36 health status survey. The PCS and MCS scores reflect a patient's overall physical and mental health status, respectively [13]. The PCS and MCS scores are continuous variables with a range of 0 to 100, where higher scores indicate better health status. The summary scores are standardized to the general US population (mean score = 50; standard deviation = 10). Very high PCS scores indicate no physical limitations, disabilities, or decrements in well-being as well as high energy level; very low PCS scores indicate substantial limitations in self-care, physical, social, and role activities; severe bodily pain; or frequent tiredness. Very high MCS scores suggest frequent positive affect, absence of both psychological distress and limitations in usual social/role activities due to emotional problems; very low MCS scores suggest frequent psychological distress and substantial social and role disability due to emotional problems. Scoring of the SF-36 followed the methods described by Ware and associates [13]

The candidate independent, or predictor, variables were the preoperative demographic, noncardiac, and cardiac variables listed in Table 1. These variables were derived from the published literature on risk variables for mortality and health status outcomes after CABG surgery [1–4, 6–9, 14]. Psychiatric disease was defined as a history of depression or other mental health illness requiring medication within the previous 2 years. Chronic neurologic disease was defined as any neurologic disorder resulting in significant disability for more than 2 months in the previous year.

Adjusted R² was calculated for the multivariable models as a summary measure of goodness of fit [15]. Cook's D and DFFIT statistics were used to identify influential cases in the multivariable models [15]. Because the magnitude of association between continuous independent variables and the outcome variable depends on the increment of the independent variable, we chose approximately one standard deviation increments for all continuous independent variables to standardize comparison. Power was estimated to be more than 99% to detect an increment in R² of 0.05 by the addition of a covariate to the linear regression models. The correlation between baseline PCS and MCS scores was determined using the Spearman correlation coefficient.

To assess the robustness of our findings, secondary logistic regression models were developed with the outcome of greater than versus less than median 6-month PCS and MCS scores. The results of these models were consistent with the linear regression models and are not presented in this manuscript.

Finally, missing quality of life questionnaires can potentially bias risk models (ie, selection bias from survey nonresponders) [16]. We therefore used a propensity score instrumental variable method to assess whether the multivariable models had biased variable estimates because of missing questionnaires [17]. Analyses were performed using the SAS version 8.0 (SAS Institute, Cary, NC) and power analyses were done using PASS software (NCSS, Kaysville, UT).

	Results

Top Abstract Introduction Material and methods Results Comment Acknowledgments References

 
Baseline (preoperative) characteristics of the study population are contained in Table 1. The average age was 63 years. Approximately two thirds of the study population had CCS class III or IV angina, one third were NYHA class III or IV, and approximately 10% had prior heart surgery. Overall, the study population had a significant burden of comorbidity, including diabetes (25%), peripheral vascular disease (27%), cerebral vascular disease (17%), chronic obstructive pulmonary disease (12%), and psychiatric disease (7%). One quarter of the patients were current smokers at the time of the operation. The mean preoperative PCS score was 33.0, which is approximately 1.3 standard deviations below the age- and sex-matched norm for the United States population [13]. The mean preoperative MCS score was 44.3, which is approximately 0.7 standard deviations below the age- and sex- matched norm for the United States population [13].

The mean postoperative PCS score was 38.2 (SD 10.6), giving an average improvement of 5.2 points for the study population (p < 0.001). The multivariable risk model for 6-month physical health status is contained in Table 2. After adjustment for baseline PCS score, variables associated with lower postoperative PCS scores (ie, worse physical health status) included a history of chronic neurologic disease, peripheral vascular disease, chronic obstructive pulmonary disease, hypertension, current smoking, lower forced expiratory volume, left ventricular ejection fraction less than 0.55, lower preoperative MCS score, and elevated serum creatinine. The adjusted R² for the multivariable model was 0.204, indicating that the model explained approximately 20% of the variance in the outcome of 6-month PCS score. Cook's D and DFFIT tests identified no outlying influential cases for this model, and propensity score analysis indicated that the model was not biased because of missing HRQL assessments.

	Comment

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With the exception of ejection fraction and NYHA class, all of the predictors of HRQL in this study were noncardiac, representing either comorbidity or baseline HRQL. One possible explanation is that preoperative cardiac limitations may largely be corrected by successful surgery, leaving noncardiac comorbid conditions as the primary predictors of postoperative health status. Whatever the explanation, noncardiac variables appear to have a major impact on HRQL outcomes after CABG surgery. Therefore, interventions to improve HRQL outcomes after CABG surgery may need to focus on noncardiac comorbidity. In this regard, potentially modifiable risk factors may be targeted, such as smoking and psychiatric disease.

Smoking has been associated with worse HRQL in both cardiac and noncardiac populations [18, 19]. This is the first study to link active smoking before CABG surgery with adverse HRQL outcomes, and smoking was associated with worse physical and mental health status in this study. Previous studies have demonstrated that smoking is also associated with higher mortality after CABG surgery [20]. Because smoking is potentially modifiable and associated with a broad spectrum of adverse outcomes, it would appear to be an important target for interventions to improve patient outcomes, including HRQL, after CABG surgery.

The finding that a history of psychiatric disease is associated with worse mental health status outcomes may not be surprising. However, it is important to note that it was an independent predictor of worse mental health status after controlling for baseline mental health status. Furthermore, this finding adds to the increasing evidence of a link between mental health status and outcomes in cardiac patients [21, 22]. For example, baseline emotional distress has been shown to predict worse HRQL, less symptomatic benefit, and increased cardiac events after CABG surgery [23]. To maximize HRQL outcomes after CABG surgery, it may be important to develop and test interventions centered on psychiatric disease, such as improved recognition and treatment of depression. Although the treatment of depression has not been shown to improve mortality in post–myocardial infarction patients, these studies have demonstrated that there are safe and effective treatments for depression in cardiac populations, such as selective serotonin reuptake inhibitors [24, 25]. Future studies are needed to evaluate whether treatment of depression improves mortality, morbidity or HRQL outcomes in patients undergoing CABG surgery.

It is interesting to note that baseline PCS scores were predictive of 6-month MCS scores, and vice-versa. By design, the MCS and PCS scores are not correlated [13], reflecting mental and physical health status, respectively, and baseline PCS and MCS scores were not correlated in this study population (r = 0.02; p = 0.30). However, it is easy to imagine that worse physical health status could affect mental health. Similarly, worse mental health status can be associated with worse physical health status, as is found in conditions such as depression, where patients may have heightened perceptions of physical limitation. Previous work by our group and others have demonstrated that baseline HRQL is a major determinant of postoperative HRQL as well as mortality [4, 7, 9, 10, 26]. The results of this study further support the clinical utility of obtaining a preoperative HRQL assessment in order to risk-stratify patients with regard to HRQL and other outcomes.

With the exception of chronic neurologic disease, psychiatric history, and baseline MCS scores, all of the variables found to be predictive of 6-month HRQL in this study have previously been associated with elevated mortality risk for CABG surgery [1–4]. Therefore, a patient with one or more of these variables has an elevated mortality risk, and if they survive the surgery, they are also at risk for worse HRQL outcomes. Given that the vast majority of patients survive the operation, HRQL outcomes after CABG surgery are of paramount importance. Furthermore, a focus on HRQL is consistent with the Institute of Medicine's call for more patient-centered care to improve the quality of care in the United States [27]. The results of this study expand risk-stratification beyond mortality, and this information can be used to better counsel patients about the potential risks and benefits of the operation.

Several previous studies have reported predictors of HRQL after CABG surgery, finding several of the same risk factors as this study such as peripheral vascular disease, hypertension, chronic obstructive pulmonary disease, and elevated NYHA functional class [6–9, 14]. Limitations of previous studies have included retrospective ascertainment of preoperative HRQL, failure to control for baseline HRQL, combining CABG and valve replacement patients, limited clinical datasets available for risk-modeling, or failure to use validated health status assessments. Strengths of this study include its large size, prospective design (including preoperative HRQL assessment), large set of clinical variables available for risk modeling, and use of the SF-36, which is a validated tool for the assessment of overall physical and mental health status and for assessment of HRQL around CABG surgery [13, 28]. Finally, we assessed for potential selection bias from missing questionnaires, which is often ignored in quality of life studies [16].

Several potential limitations of this study should be addressed. First, the study population was largely older male veterans, which may limit generalizability. Second, the study population excluded patients in whom it was not logistically possible to obtain a preoperative HRQL assessment. However, baseline HRQL assessment is unlikely to impact the decision to undergo CABG surgery in patients who need urgent/emergent surgery for pressing clinical indications. Third, the multivariable models explained less than 25% of the variance in HRQL outcomes. However, this is consistent with other multiple regression models in the literature predicting quality of life [29]. Furthermore, quality of life 6 months after CABG surgery may be impacted by factors such as processes and structures of care, complications of the surgery, or interim life change or health events. While future studies should evaluate these variables as predictors of 6-month health status, the focus of this study was on the preoperative predictors of HRQL outcomes in order to inform preoperative risk assessment and counseling of patients. Finally, we cannot exclude selection bias from missing quality of life surveys. However, this study had good follow-up and when advanced statistical methods were employed to assess for potential bias in the regression models, no evidence of bias was found.

In summary, the predictors of HRQL outcomes after CABG surgery delineated in this study can be used by clinicians in preoperative risk stratification and counseling of patients. Future studies should address whether interventions targeting one or more of these factors can improve patient outcomes.

	Acknowledgments

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This study was funded by the Health Services Research and Development Service and Cooperative Studies Program of the US Department of Veterans Affairs. Doctor Rumsfeld is supported by a VA Health Services Advanced Research Career Development Award (RCD 98341–2). The authors would like to acknowledge the contribution of Dr David Werking to this project.

	References

Top Abstract Introduction Material and methods Results Comment Acknowledgments References

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Frederick L. Grover Karl E. Hammermeister Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Rumsfeld, J. S. Articles by Hammermeister, K. E. Search for Related Content PubMed PubMed Citation Articles by Rumsfeld, J. S. Articles by Hammermeister, K. E. Related Collections Coronary disease