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J Thorac Cardiovasc Surg 2008;136:665-672
© 2008 The American Association for Thoracic Surgery

Surgery for Acquired Cardiovascular Disease

Socioeconomic status and comorbidity as predictors of preoperative quality of life in cardiac surgery

Colleen Gorman Koch, MD, MSa,b,*, Liang Li, PhDc, Mehdi Shishehbor, DO, MPHd, Steve Nissen, MDd, Joseph Sabik, MDe, Norman J. Starr, MDa,b, Eugene H. Blackstone, MDc,e

a Department of Cardiothoracic Anesthesia, The Cleveland Clinic, Cleveland, Ohio
b Department of Outcomes Research, The Cleveland Clinic, Cleveland, Ohio
c Department of Quantitative Health Sciences, The Cleveland Clinic, Cleveland, Ohio
d Department of Cardiovascular Medicine, The Cleveland Clinic, Cleveland, Ohio
e Department of Thoracic and Cardiovascular Surgery, The Cleveland Clinic, Cleveland, Ohio

Received for publication October 3, 2007; revisions received February 27, 2008; accepted for publication April 6, 2008.

* Address for reprints: Colleen Gorman Koch, MD, MS, Department of Cardiothoracic Anesthesia (G-3), The Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195. (Email: kochc{at}ccf.org).


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Limitations
 Conclusions
 Appendix Table E1
 References
 
Objective: Preoperative quality of life of patients undergoing cardiac surgical procedures has been associated with postoperative morbidity, survival, and quality of life. Patients of lower socioeconomic status have disproportionately greater cardiovascular disease burden and more complications of cardiovascular disease. We examined the interactive effects of demographic characteristics, socioeconomic status, and comorbidity on preoperative functional quality of life measured by the well-validated cardiovascular disease–specific Duke Activity Status Index.

Methods: The patient population consisted of 5581 patients between May 1995 and January 1999 who underwent operations on cardiopulmonary bypass: isolated coronary artery bypass grafting, isolated valve procedures, or combined coronary artery bypass grafting and valve procedures and had a preoperative Duke Activity Status Index, along with socioeconomic status information from United States 2000 census data. Predictors were identified by logistic regression for maximum value of baseline DASI and linear regression for DASI scores less than maximum by means of bagging variable selection.

Results: Lower socioeconomic status was associated of lower risk-adjusted quality of life (maximum Duke Activity Status Index P = .0002, less than maximum Duke Activity Status Index P = .0007). Older age, female sex, certain comorbidities, higher New York Heart Association class, lower left ventricular function, and reoperation were also statistically significantly associated with lower preoperative Duke Activity Status Index.

Conclusion: Lower socioeconomic status is associated with lower risk-adjusted quality of life for patients undergoing cardiac surgery. Quality of life affects morbid outcomes, so further characterization of risk factors for poor quality of life offers an opportunity for intervention.

Abbreviations and Acronyms DASI = Duke Activity Status Index


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Limitations
 Conclusions
 Appendix Table E1
 References
 
Characterizing quality of life for patients with cardiovascular disease who are candidates for surgical procedures is important, because investigations have linked preoperative quality of life with postoperative morbidity. Specifically, lower preoperative health-related quality of life has been associated with increased risk-adjusted mortality,1-3Go prolonged hospital stay,4Go and reduced long-term survival.5Go Factors influencing preoperative quality of life have not been thoroughly investigated, however, although an increased burden of cardiovascular disease has been reported to be disproportionally present in subgroups of patients defined by ethnicity and socioeconomic status.6-8Go To that end, we sought to examine the influence of socioeconomic status, demographic characteristics, and clinical factors, as well as primary valvular and coronary disease status, on preoperative functional quality of life of patients undergoing cardiac operations by means of the Duke Activity Status Index (DASI).


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Limitations
 Conclusions
 Appendix Table E1
 References
 
Patients
The initial study population included 12,130 consecutive patients who underwent cardiac surgical procedures with cardiopulmonary bypass between May 1995 and January 1999. Of these patients, 10,495 had baseline preoperative DASI surveys performed and underwent isolated coronary artery bypass grafting, coronary artery bypass grafting with valve procedures, or isolated valve procedures. If a patient underwent multiple operations, only the last one was considered. Of these patients, 5581 (53%) had complete census block information from United States 2000 census for socioeconomic status evaluation. Preoperative and perioperative variables were collected prospectively, concurrent with patient care, and entered into Cleveland Clinic Department of Cardiothoracic Anesthesia and Cardiovascular Information Registries. The institutional review board approved the use of these databases for research, with patient consent waived.

Preoperative DASI
DASI is a validated 12-item disease-specific quality of life questionnaire for patients with cardiovascular disease.9,10Go Each item is weighted according to its known metabolic cost, and items are summed to form the individual patient DASI, which takes values between 0 and 58.2 (Go Figure 1). Better physical functioning is represented by higher scores (Go Table 1).9,10Go The DASI instrument was self-administered preoperatively. If a patient was unable to complete the DASI independently, a trained research assistant read the exact wording of the survey.


Figure 1
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  		Figure 1. Histogram of preoperative Duke Activity Status Index (DASI) scores among 5581 patients with socioeconomic status information. Distribution is close to continuous except for spike at 58.2, representing 904 patients with quality of life status at or beyond index measurement limit.

 

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  		Table 1 The Duke Activity Status Index9Go
 
Socioeconomic Status
We used census block socioeconomic data, a geographic unit containing approximately 1000 residents, as a surrogate for individual socioeconomic status. Census block socioeconomic measures were obtained from the 2000 US census data.11Go We used a previously validated approach to calculate a composite neighborhood socioeconomic status score from 6 census block characteristics: median household income; median value of housing unit; proportion of households receiving interest, dividend, or net rental income; proportion of adults 25 years of age or older who had completed high school; proportion of adults 25 years of age or older who had completed college; and proportion of employed persons 16 years of age or older in executive, managerial, or professional specialty occupations.11Go Briefly, the distribution for each census block characteristic was standardized by dividing the mean value for each measure by its SD. A composite z score for the socioeconomic status of each neighborhood was then determined by summing all 6 resulting standardized score values.11Go Go Figure 2 displays the distribution of socioeconomic status indicators of the study patients with superimposed US population norms. Characteristics of patients with available socioeconomic status information versus those without were generally comparable (Appendix Table E1).


Figure 2
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  		Figure 2. Histograms of 6 socioeconomic status characteristics (N = 5581). Superimposed circles and red lines are corresponding distributions for entire US population. Patients in study sample generally had better socioeconomic status than did general US population.

 
Statistical Methods
Regression analyses were performed to relate variables contained in Go Table 2 with preoperative DASI. The distribution of preoperative DASI values is shown in Figure 1. The score is a semicontinuous variable on its scale, with a discrete point mass at the maximum score 58.2 representing a substantial proportion of patients (16%) with functional status at or beyond the measurement limit of the DASI. To accommodate this special feature of the DASI score, we used a two-part regression model: a logistic regression to model the probability of reaching the maximum DASI score and a multiple linear regression to model mean DASI scores among patients whose DASI scores were less than the maximum value. Bootstrap aggregation (bagging)12Go was used to select important predictors into the models: 500 bootstrap data sets were generated; for each bootstrap data set, a stepwise model selection was performed with a variable entry P value of .10 and a retention P value of .05. Results were aggregated, and variables that were selected in at least 50% of bootstrap data sets were retained in the final model.


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  		Table 2 Patient characteristics and details of cardiac operation (N = 5581)
 

   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Limitations
 Conclusions
 Appendix Table E1
 References
 
The univariate association between the total socioeconomic status z score and the preoperative DASI score is illustrated in Go Go Figures 3 and 4. Figure 3 shows that patients with higher socioeconomic status had a better chance of reaching the best DASI score at 58.2. Figure 4 demonstrates a positive association across the entire range of DASI values; that is, patient groups with higher DASI scores also had higher socioeconomic status.


Figure 3
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  		Figure 3. Univariate association between socioeconomic status z score and probability of reaching maximum Duke Activity Status Index (DASI) score. Estimated probability and 95% confidence band were calculated from nonparametric logistic regression model. Upward trend is evident.

 

Figure 4
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  		Figure 4. Box plots of total socioeconomic status z scores according to groups defined by preoperative Duke Activity Status Index (DASI) scores. Middle line and boundaries of box represent median and 25th and 75th percentiles. Whiskers on two sides are at 1.5 times length of box from each end of box. Data outside whiskers, potential outliers, are marked by open circles. Patients with better Duke Activity Status Index scores tended to have better socioeconomic status.

 
The association between socioeconomic status and preoperative DASI was further investigated in a multivariable setting in which the effects of other confounders were adjusted. Go Table 3 reports the results, which consists of a logistic regression predicting the probability of reaching the maximum preoperative DASI score and a linear regression predicting the mean preoperative DASI score among those with DASI values lower than 58.2. All variables in Table 2 were considered as potential confounders, and only those selected by the bagging procedure were retained in the final models in Table 3.


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  		Table 3 Predictors for preoperative Duke Activity Status Index with total socioeconomic status z score in multiple regression models (N = 5581)
 
In Table 3, a positive linear regression coefficient or an odds ratio larger than 1 indicates that increasing value of the corresponding continuous variable (or presence of the corresponding comorbid condition) is associated with better preoperative DASI score or with increased chance of reaching the maximum DASI score, respectively. In general, the results from logistic regression and linear regression are congruent; when the odds ratio of a variable is larger than 1, its regression coefficient is usually larger than 0, and vice versa.

After risk adjustment, higher total socioeconomic status z score was statistically significantly associated with better preoperative DASI (odds ratio 1.03, P = .0002; coefficient 0.121, P = .0007). This is consistent with the univariate analysis results.

Factors related to lower preoperative DASI include older age, female sex, higher body mass index (>25 kg/m2), lower hematocrit, abnormal left ventricular function, heart failure, chronic obstructive pulmonary disease, peripheral vascular disease, previous stroke, renal disease, diabetes, higher New York Heart Association functional class, emergency surgery, and reoperation. The nomograms in Go Figure 5 (A and B) further illustrate the interrelationships of age, socioeconomic status, and preoperative DASI according to the model from Table 3. Go Figure 6 displays the univariate relationship between New York Heart Association functional class and DASI, which is consistent with the findings in Table 3. The DASI by nature is, however, more refined in terms of overall summary score related to answering specific questions weighted according to metabolic cost.


Figure 5
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  		Figure 5. Nomograms by age group. A, Estimated probability (thick, solid line) of reaching maximum Duke Activity Status Index (DASI) with pointwise 95% confidence interval (thin dashed line). B, Expected Duke Activity Status Index (DASI) (< 8.2, thick, solid line) with pointwise 95% confidence interval (thin, dashed line). At all age groups, increasing socioeconomic status was associated with better preoperative Duke Activity Status Index.

 

Figure 6
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  		Figure 6. Histograms of preoperative Duke Activity Status Index (DASI) by New York Heart Association (NYHA) functional classification. Patients with lower New York Heart Association functional classification generally had better socioeconomic status.

 
Although all the variables retained in the final models are statistically significant, they differ in importance. In this study, we measured the importance of a variable in the final model by the relative reduction in R 2 (for linear regression) or pseudo R 2 (for logistic regression) caused by removing that variable from the model. R 2 and pseudo R 2 can be seen as roughly the proportion of variation in the preoperative DASI that can be modeled exactly by the predictors. They are approximately 25% for both logistic and linear final models. Table 3 shows that in the models predicting preoperative DASI, age is the most important predictor. The importance of total socioeconomic status score is of similar magnitude to those of diabetes, peripheral vascular disease, and renal disease.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Limitations
 Conclusions
 Appendix Table E1
 References
 
Principal Findings
The novel aspect of our investigation is that lower socioeconomic status in patients with cardiovascular disease who are candidates for cardiac operations is associated with significantly lower preoperative quality of life, even after adjustment for preoperative demographic characteristics, laboratory values, comorbidity, clinical status, preoperative valve disease, and degree and extent of coronary artery stenosis. The influence of socioeconomic status on preoperative quality of life in cardiac surgical patients has not been previously explored; however, studies have linked low socioeconomic status to cardiovascular disease risk factors,7,11,13,14Go with morbidity after cardiovascular events6,7,14-17Go and with morbid outcomes after cardiac interventions.18Go The addition of socioeconomic status, although not as influential as age or sex, provides additional information in terms of variable importance of a magnitude similar to that of certain comorbid conditions, such as diabetes, peripheral vascular disease, and renal disease (Table 3 shows relative reductions in R 2 and pseudo R 2).

The precise mechanisms underlying risk for those of lower socioeconomic status are unknown. The relationship between environment and health behavior and components of socioeconomic status does, however, influence health status19Go and therefore may affect health-related quality of life. Denvir and colleagues18Go examined the influence of socioeconomic status on clinical outcomes and quality of life after percutaneous coronary intervention. At both baseline and 12 months after percutaneous coronary intervention, patients of lower socioeconomic status had lower mean health-related quality of life scores than did those with higher socioeconomic status. Multivariable analysis demonstrated that health-related quality of life scores were significantly lower at both baseline and follow-up for patients of low socioeconomic status.18Go

Increasing age, female sex, more comorbidities, specific valve lesions, and distribution of coronary disease are associated with lower risk-adjusted quality of life at presentation for surgery. Our previous work demonstrated that female sex was associated with lower risk-adjusted preoperative and follow-up quality of life for patients undergoing cardiac surgery.20Go We hypothesized that lower follow-up quality of life was related to lack of access and referral to cardiac rehabilitation programs. Certainly, socioeconomic disparities between the sexes could be an additional factor.

Our patient population is different from US population norms in terms of the relative distribution of 6 socioeconomic status indicators (Figure 2). Patients who come to our tertiary referral center are more educated, with higher median household incomes, higher median value of housing units, more households receiving dividend or interest payments, and slightly more who are in managerial positions. Our patient population does, however, span the entire socioeconomic spectrum for each of the socioeconomic status indicators. In addition, surgeons in general do not control the selection of patients for operation; rather, referral patterns are more reflective of the primary care and cardiology gatekeepers. Although surgeons can do little about selection bias for individual patient socioeconomic status, they can recognize that low socioeconomic status is a marker both for greater preoperative functional impairment, which has been associated with postoperative morbidity, and for postoperative functional impairment.


   Limitations
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Limitations
Conclusions
 Appendix Table E1
 References
 
Although this was a prospective study, there are inherent limitations associated with an observational study design. Unaccounted or unmeasured variables could influence the study findings. The quality of life instrument used for this investigation reflects a patient's functional health status without capturing a mental component. There have been investigations reporting an interrelationship between quality of life and mental health status.21-23Go

Clinical Implications
Low socioeconomic status serves as a marker for functional impairment and more advanced disease. Although individual patient socioeconomic status is static, preoperative quality of life is not. Among factors that influence quality of life, it may be possible to improve functional status, and that improvement may be translated to improved patient outcome. For example, Hadj and colleagues24Go examined preoperative preparation for cardiac surgery with a combination of metabolic, physical, and mental therapy. They demonstrated that patients undertaking a regimen of physical therapy incorporating nonexhaustive, light exercise and stretching techniques and of mental therapy in form of stress-reduction, relaxation and music, along with medications (eg, antioxidants) had improved physical and mental preparation before cardiac surgery. Hadj and colleagues24Go reported that their therapy was safe and suggested that it could improve quality of life and enhance postoperative recovery.24Go Furthermore, identification of predictors for preoperative functional impairment may allow better discharge planning (skilled nursing, cardiac rehabilitation), because patients with preoperative functional impairment have more postoperative functional impairment.20Go

Moreover, lower health-related quality of life for patients of lower socioeconomic status raises concern regarding health care provided to patients from economically disadvantaged backgrounds. Socioeconomic status does not intrinsically translate to a physiologic variable; however, it probably reflects a patient's quality of life. The impact of poor quality of life on perioperative morbid outcomes has been clearly demonstrated.

These disadvantaged patients enter the health care system when their functional status has deteriorated significantly more than is the case for patients of higher socioeconomic status. Regardless of whether this is an issue of access or financial resources, patients coming to surgery with more functional impairment may burden the health care system more than if they had a a less impaired state. Frist25Go summarized the federal government's decision to address socioeconomic status along with race and geography to overcome health disparities in the Minority and Health Disparities Research and Education Act of 2000. He noted that low socioeconomic status has been associated with less access to care and fewer community resources.25-27Go Mechanic28Go commented that it is well established that socioeconomic status differences fundamentally influence health outcomes.


   Conclusions
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Limitations
 Conclusions
Appendix Table E1
 References
 
Our findings are novel, in that lower socioeconomic status was found to have a risk-adjusted link to lower preoperative functional quality of life for patients with cardiovascular disease who are candidates for cardiac surgery. Demographic characteristics such as age, sex, and comorbidity were also associated with lower preoperative functional status. It is unknown why patients of low socioeconomic status have more functional impairment; however, socioeconomic status not only further discriminates patients at risk for lower quality of life after cardiac surgery but aids in resource allocation and discharge planning for recovery after hospital discharge.


   Appendix Table E1
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Limitations
 Conclusions
 Appendix Table E1
References
 

Characteristics of the entire patient population and those with socioeconomic status information
Variable All patients (N = 10,456) Patients with complete SES data (N = 5581)
Demographic data
 Age (y, median and interquartile range) 66 (56–73) 67 (58–74)
 Female (No.) 3094 (29.6) 1787 (32.0)
 Body mass index (kg/m2, median and interquartile range) 27.2 (24.4–30.5) 27.5 (24.7–30.9)
 Body surface area (m2, median and interquartile range) 1.97 (1.81–2.13) 1.98 (1.82–2.14)
 Ethnicity (No.)
   White 9063 (86.7) 5073 (90.9)
   Black 423 (4.1) 350 (6.3)
   Asian 682 (6.5) 50 (0.9)
   Other * 288 (2.8) 108 (1.9)
Preoperative laboratory values
 Hematocrit (%, median and interquartile range) 40.4 (37.0–43.2) 40.0 (36.5–42.8)
 Albumin (mg/dL, median and interquartile range) 4.2 (3.9–4.5) 4.1 (3.8–4.4)
 Creatinine (mg/dL, median and interquartile range) 1.0 (0.8–1.2) 1.0 (0.9–1.2)
 Blood urea nitrogen (mg/dL, median and interquartile range) 17.0 (14.0–22.0) 17.0 (14.0–22.0)
 Bilirubin (mg/dL, median and interquartile range) 0.7 (0.5–0.9) 0.7 (0.5–0.9)
Cardiac morbidity (No.)
 Abnormal left ventricular function 5014 (48.0) 2734 (49)
 Heart failure 2636 (25.2) 1445 (25.9)
 Atrial fibrillation 511 (4.9) 241 (4.3)
 New York Heart Association functional class 1328 (12.7) 727 (13.0)
   II 4943 (47.3) 2558 (45.8)
   III 1766 (16.9) 913 (16.4)
   IV 2419 (23.1) 1383 (24.8)
 Previous myocardial infarction 4893 (46.8) 2728 (48.9)
Clinical presentation (No.)
 Emergency surgery 256 (2.4) 161 (2.9)
Comorbidity (No.)
 Hypertension 6069 (58.0) 3405 (61.0)
 Chronic obstructive pulmonary disease 745 (7.1) 433 (7.8)
 Smoking 6233 (59.6) 3423 (61.3)
 Type 1 diabetes 1000 (9.6) 584 (10.5)
 Type 2 diabetes 1457 (13.9) 805 (14.4)
 Stroke 637 (6.1) 373 (6.7)
 Peripheral vascular disease 1107 (10.6) 640 (11.5)
 Renal disease 110 (1.1) 51 (0.9)
Coronary disease, >70% stenosis (No.)
 Left main trunk 923 (8.8) 541 (9.7)
 Left anterior descending 6613 (63.2) 3658 (65.5)
 Left circumflex 5560 (53.2) 3093 (55.4)
 Right coronary artery 5921 (56.6) 3353 (60.1)
Valve disease (No.)
 Aortic valve stenosis 1622 (15.5) 857 (15.4)
 Mitral valve stenosis 431 (4.1) 195 (3.5)
 Aortic valve regurgitation 1861 (17.8) 896 (16.1)
 Mitral valve regurgitation 3473 (33.2) 1636 (29.3)
Procedures (No.)
 Coronary artery bypass grafting 7761 (74.2) 4412 (79.1)
 Aortic valve replacement 2082 (19.9) 1071 (19.2)
 Aortic valve repair 169 (1.6) 67 (1.2)
 Mitral valve replacement 899 (8.6) 414 (7.4)
 Mitral valve repair 1549 (14.8) 641 (11.5)
 Tricuspid valve replacement or repair 412 (3.9) 189 (3.4)
 Reoperation 2384 (22.8) 1110 (19.9)
* Other ethnicities include Hispanics, Native Americans, and Arabs.

Missing data for New York Heart Association functional class, albumin, bilirubin, previous myocardial infarction, and coronary stenosis variables were less than 5%, and missing values have been imputed by mean or median.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Limitations
 Conclusions
 Appendix Table E1
 References
 

  1. Gardner SC, Grunwald GK, Rumsfeld JS, Rumsfeld JS, Cleveland Jr. JC, Schooley LM, et al. Comparison of short-term mortality risk factors for valve replacement versus coronary artery bypass graft surgery. Ann Thorac Surg 2004;77:549-556.[Abstract/Free Full Text]
  2. Ho PM, Masoudi FA, Peterson PN, Shroyer AL, McCarthy Jr. M, Grover FL, et al. Health-related quality of life predicts mortality in older but not younger patients following cardiac surgery. Am J Geriatr Cardiol 2005;14:176-182.[Medline]
  3. Rumsfeld JS, MaWhinney S, McCarthy Jr. M, Shroyer AL, VillaNueva CB, O'Brien M, et al. Health-related quality of life as a predictor of mortality following coronary artery bypass graft surgery. Participants of the Department of Veterans Affairs Cooperative Study Group on Processes, Structures, and Outcomes of Care in Cardiac Surgery. JAMA 1999;281:1298-1303.[Abstract/Free Full Text]
  4. Curtis LH, Phelps CE, McDermott MP, Rubin HR. The value of patient-reported health status in predicting short-term outcomes after coronary artery bypass graft surgery. Med Care 2002;40:1090-1100.[Medline]
  5. Koch CG, Li L, Lauer M, Sabik J, Starr NJ, Blackstone EH. Effect of functional health-related quality of life on long-term survival after cardiac surgery. Circulation 2007;115:692-699.[Abstract/Free Full Text]
  6. Bonow RO, Grant AO, Jacobs AK. The cardiovascular state of the union: confronting healthcare disparities. Circulation 2005;111:1205-1207.[Free Full Text]
  7. Mensah GA, Mokdad AH, Ford ES, Greenlund KJ, Croft JB. State of disparities in cardiovascular health in the United States. Circulation 2005;111:1233-1241.[Abstract/Free Full Text]
  8. Winkleby MA, Kraemer HC, Ahn DK, Varady AN. Ethnic and socioeconomic differences in cardiovascular disease risk factors: findings for women from the Third National Health and Nutrition Examination Survey, 1988-1994. JAMA 1998;280:356-362.[Abstract/Free Full Text]
  9. Hlatky MA, Boineau RE, Higginbotham MB, Lee KL, Mark DB, Califf RM, et al. A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index). Am J Cardiol 1989;64:651-654.[Medline]
  10. Nelson CL, Herndon JE, Mark DB, Pryor DB, Califf RM, Hlatky MA. Relation of clinical and angiographic factors to functional capacity as measured by the Duke Activity Status Index. Am J Cardiol 1991;68:973-975.[Medline]
  11. Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, et al. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med 2001;345:99-106.[Medline]
  12. Breiman L. Bagging and predicting. Machine Learning 1996;24:123-140.
  13. Sharma S, Malarcher AM, Giles WH, Myers G. Racial, ethnic and socioeconomic disparities in the clustering of cardiovascular disease risk factors. Ethn Dis 2004;14:43-48.[Medline]
  14. Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation 1993;88:1973-1998.[Abstract/Free Full Text]
  15. Rathore SS, Masoudi FA, Wang Y, Curtis JP, Foody JM, Havranek EP, et al. Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure: findings from the National Heart Failure Project. Am Heart J 2006;152:371-378.[Medline]
  16. Winkleby MA, Cubbin C. Influence of individual and neighbourhood socioeconomic status on mortality among black, Mexican-American, and white women and men in the United States. J Epidemiol Community Health 2003;57:444-452.[Abstract/Free Full Text]
  17. Albert MA, Glynn RJ, Buring J, Ridker PM. Impact of traditional and novel risk factors on the relationship between socioeconomic status and incident cardiovascular events. Circulation 2006;114:2619-2626.[Abstract/Free Full Text]
  18. Denvir MA, Lee AJ, Rysdale J, Walker A, Eteiba H, Starkey IR, et al. Influence of socioeconomic status on clinical outcomes and quality of life after percutaneous coronary intervention. J Epidemiol Community Health 2006;60:1085-1088.[Abstract/Free Full Text]
  19. Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood) 2002;21:60-76.[Abstract/Free Full Text]
  20. Koch CG, Khandwala F, Cywinski JB, Ishwaran H, Estafanous FG, Loop FD, et al. Health-related quality of life after coronary artery bypass grafting: a gender analysis using the Duke Activity Status Index. J Thorac Cardiovasc Surg 2004;128:284-295.[Abstract/Free Full Text]
  21. Ho PM, Masoudi FA, Spertus JA, Peterson PN, Shroyer AL, McCarthy Jr. M, et al. Depression predicts mortality following cardiac valve surgery. Ann Thorac Surg 2005;79:1255-1259.[Abstract/Free Full Text]
  22. Blumenthal JA, Lett HS, Babyak MA, White W, Smith PK, Mark DB, et al. Depression as a risk factor for mortality after coronary artery bypass surgery. Lancet 2003;362:604-609.[Medline]
  23. Connerney I, Shapiro PA, McLaughlin JS, Bagiella E, Sloan RP. Relation between depression after coronary artery bypass surgery and 12-month outcome: a prospective study. Lancet 2001;358:1766-1771.[Medline]
  24. Hadj A, Esmore D, Rowland M, Pepe S, Schneider L, Lewin J, et al. Pre-operative preparation for cardiac surgery utilising a combination of metabolic, physical and mental therapy. Heart Lung Circ 2006;15:172-181.[Medline]
  25. Frist WH. Overcoming disparities in U.S. health care. Health Aff (Millwood) 2005;24:445-451.[Abstract/Free Full Text]
  26. Stewart A, Nápoles-Springer AM. advancing health disparities research: can we afford to ignore measurement issues?. Med Care 2003;41:1207-1220.[Medline]
  27. Cohen D, Farley T, Mason K. Why is poverty unhealthy? Social and physical mediators. Soc Sci Med 2003;57:1631-1641.[Medline]
  28. Mechanic D. Policy challenges in addressing racial disparities and improving population health. Health Aff (Millwood) 2005;24:335-338.[Abstract/Free Full Text]



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