0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Contribution |

Health-Related Quality of Life as a Predictor of Mortality Following Coronary Artery Bypass Graft Surgery FREE

John S. Rumsfeld, MD; Samantha MaWhinney, ScD; Martin McCarthy, Jr, PhD; A. Laurie W. Shroyer, PhD; Catherine B. VillaNueva, RN, MS, MBA; Maureen O'Brien, MS; Thomas E. Moritz, MS; William G. Henderson, PhD; Frederick L. Grover, MD; Gulshan K. Sethi, MD; Karl E. Hammermeister, MD; for the Participants of the Department of Veterans Affairs Cooperative Study Group on Processes, Structures, and Outcomes of Care in Cardiac Surgery
[+] Author Affiliations

Author Affiliations: Division of Cardiology (Drs Rumsfeld and Hammermeister), Departments of Preventive Medicine and Biometrics (Dr MaWhinney), and Medicine (Dr Shroyer), and Division of Cardiothoracic Surgery (Dr Grover), Health Sciences Center, University of Colorado, Denver; Cardiology Section (Dr Hammermeister), Cardiac Research (Dr Shroyer and Mss VillaNueva and O'Brien), and Surgery Service (Dr Grover), Denver Veterans Affairs Medical Center, Denver, Colo; Department of Preventive Medicine, Northwestern University, Chicago, Ill (Dr McCarthy); Veterans Affairs Cooperative Studies Program Coordinating Center, Hines Veterans Affairs Medical Center, Hines, Ill (Mr Moritz and Dr Henderson); Cardiothoracic Surgery Section, Tucson Veterans Affairs Medical Center, Tucson, Ariz (Dr Sethi); and Department of Surgery, Health Sciences Center, University of Arizona, Tucson (Dr Sethi).
Principal Investigators of the Department of Veterans Affairs Cooperative Study Group on PSOCS: Marvin Kirsh, MD, Ann Arbor, Mich; Stewart Scott, MD (deceased); John Lucke, MD, Asheville, NC; John Handy, MD, Charleston, SC; David Fullerton, MD, Denver, Colo; Donald DePinto, MD, Hines, Ill; H. Gareth Tobler, MD, and Kwabena Mawulawd, MD, Little Rock, Ark; G. Hossein Almassi, MD, Milwaukee, Wis; Herbert Ward, MD, Minneapolis, Minn; Walter Merrill, MD, Nashville, Tenn; Rick Esposito, MD, New York, NY; O. LaWayne Miller, MD, San Antonio, Tex; Gulshan Sethi, MD, Tucson, Ariz; Shukri Khuri, MD, West Roxbury, Mass; and Riyad Tarazi, MD, San Diego, Calif.


JAMA. 1999;281(14):1298-1303. doi:10.1001/jama.281.14.1298.
Text Size: A A A
Published online

Context Health-related quality of life has not been evaluated as a predictor of mortality following coronary artery bypass graft (CABG) surgery. Evaluation of health status as a mortality predictor may be useful for preoperative risk stratification.

Objective To determine whether the Physical and Mental Component Summary scores from the preoperative Short-Form 36 (SF-36) health status survey predict mortality following CABG surgery after adjustment for known clinical risk variables.

Design Prospective cohort study conducted between September 1992 and December 1996.

Setting Fourteen Veterans Affairs hospitals.

Patients Of the 3956 patients undergoing CABG surgery only and who were enrolled in the Processes, Structures, and Outcomes of Care in Cardiac Surgery study, the 2480 who completed a preoperative SF-36.

Main Outcome Measure All-cause mortality within 180 days after surgery.

Results A total of 117 deaths (4.7%) occurred within 180 days of CABG surgery. The Physical Component Summary of the preoperative SF-36 was a statistically significant risk factor for 6-month mortality after adjustment for known clinical risk factors for mortality following CABG surgery. In multivariate analysis, a 10-point lower SF-36 Physical Component Summary score had an odds ratio (OR) of 1.39 (95% confidence interval [CI], 1.11-1.77; P=.006) for predicting mortality. The SF-36 Mental Component Summary score was not associated with 6-month mortality in multivariate analyses (OR, 1.09; 95% CI, 0.92-1.29; P=.31).

Conclusions The Physical Component Summary score from the preoperative SF-36 is an independent risk factor for mortality following CABG surgery. The baseline Mental Component Summary score does not appear to be predictive of mortality. Preoperative patient self-report of the physical component of health status may be helpful for risk stratification and clinical decision making for patients undergoing CABG surgery.

Figures in this Article

Coronary artery bypass graft (CABG) surgery is one of the most common surgical procedures in the United States and requires considerable resources. In 1995, approximately 573,000 CABG surgeries were performed in the United States at an average cost of $44,820 per procedure.1 Clinical decision making about whether to perform CABG surgery usually includes physician assessment of a patient's mortality risk from the procedure. Clinical predictors of mortality following CABG surgery can help guide physicians and patients in risk stratification.24 Established clinical risk factors include a history of previous heart surgery, the severity of coronary artery disease, and the degree of comorbidity.

There is an increasing interest in exploring health-related quality of life (HRQL) in relation to a given health care episode, such as CABG surgery.5,6 Multiple self-report (by the patient) health-status measurement tools to assess HRQL, such as the Short-Form 36 (SF-36), have been developed in the past 2 decades.7 While these tools vary in the constructs they measure, all have the common goal of capturing "health status as perceived by the patient in areas of health identified to be of value to the patient."8 The most common constructs proposed for generic health surveys to capture HRQL are physical functioning, psychological functioning, social functioning, role functioning, and general health perceptions.810 Measurements of self-perceived health status can be used to evaluate the broad impact of a disease on a patient and the effectiveness of interventions. They can play a role in the clinical management of patients with cardiac disorders by extending the assessment process beyond traditional clinical parameters and tracking the multidimensional impact of treatments over time.11

To date, preoperative HRQL has not been evaluated as a predictor of mortality following CABG surgery. Evaluating HRQL as a mortality predictor may add to the understanding of how the outcome of a given health care episode is influenced by a patient's preoperative health status. Furthermore, it is possible that patient self-report may be a valuable tool for risk stratification before CABG surgery. The present study tests the hypothesis that preoperative HRQL variables reflecting the physical and mental components of health status independently predict 6-month mortality following CABG surgery, even after adjusting for traditional clinical risk variables.

Subjects

The study population was derived from the Department of Veterans Affairs (VA) Cooperative Study in Health Services No. 5, entitled Processes, Structures, and Outcomes of Care in Cardiac Surgery (PSOCS), a multicenter, prospective, observational study investigating the links between processes and structures of care and risk-adjusted outcomes. Details of the PSOCS study are published elsewhere12,13 and are only reported here as relevant to the current analyses. Approximately 1500 variables representing patient-related risk factors, processes, structures, and outcomes of care were collected for a representative sample of 4969 patients who underwent cardiac surgery at 14 VA medical centers between September 1992 and December 1996. The 14 VA medical centers (Ann Arbor, Mich; Asheville, NC; Charleston, SC; Denver, Colo; Hines, Ill; Little Rock, Ark; Milwaukee Wis; Minneapolis, Minn; Nashville, Tenn; New York, NY; San Antonio, Tex; Tucson, Ariz; West Roxbury, Mass; and San Diego, Calif) were chosen from the total of 43 VA medical centers that perform open heart surgery to be representative of the spectrum of risk-adjusted operative mortality by center. Data were prospectively collected by full-time, trained research nurses located at each of the 14 sites. Risk data were obtained by patient interview and chart review within 72 hours prior to 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 SF-36, the research nurse conducted a personal interview for administration of the baseline SF-36.

All patients enrolled in the PSOCS who only had CABG surgery and who completed a preoperative SF-36 were included. Of the 3956 patients who underwent CABG surgery who were enrolled in the PSOCS study, 2480 (63%) completed a baseline SF-36. The primary reason for not completing the baseline SF-36 was urgent or emergency surgical priority that precluded time to obtain the assessment. Therefore, the study population predominantly included patients undergoing elective surgery.

Predictor and Outcome Variables

The primary independent, or predictor, variables of interest were the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the preoperative SF-36 (Figure 1). The SF-36 measures 8 health constructs using 8 scales with 2 to 10 items per scale (total of 36 questions).14 Several advantages of the PCS and MCS over the original 8 scales of the SF-36 have been reported.15,16 The summary scores are standardized to the general US population (mean score, 50; SD, 10), allowing easier norm-based interpretation. Scoring of the SF-36 for the original 8 scales and the PCS and MCS summary scores followed the methods described by Ware et al.15 For the PCS, very high scores indicate no physical limitations, disabilities, or decrements in well-being as well as high energy level. Very low scores indicate substantial limitations in self-care, physical, social, and role activities; severe bodily pain; or frequent tiredness.15

Figure. Eight Scales of the Short-Form 36 and Their Relationship to Summary Scores
Graphic Jump Location
Figure adapted from Ware et al15 and reproduced with permission.

For the MCS, very high scores indicate frequent positive affect, absence of psychological distress and of limitations in usual social/role activities due to emotional problems. Very low scores indicate frequent psychological distress, and substantial social and role disability due to emotional problems.15

Candidate preoperative clinical variables for risk adjustment (Table 1) were derived from the published literature on clinical risk variables for mortality following CABG surgery in both VA and non-VA populations.24

Table Graphic Jump LocationTable 1. Comparison of Study Population With Excluded Patients

The dependent, or outcome, variable was all-cause 6-month mortality, defined as death due to any cause within 180 days following CABG surgery. Mortality status was determined by the research nurses and confirmed using the VA Beneficiary Identification and Record Locator system, shown to be comparable with the National Death Index for mortality assessment in a VA population.17 The cause of death was determined by consensus of the multidisciplinary PSOCS Cause of Death Committee, composed of 6 clinician investigators, using all available information including final hospital summaries and death certificates.

Statistical Analyses

All analyses were performed using the SAS statistical software package.18 Baseline characteristics were compared between patients who underwent CABG surgery included in the study population and excluded patients (ie, those without a baseline SF-36) using the Wilcoxon rank sum test for continuous variables and the χ2 test for categorical variables. Univariate analyses were performed between the candidate independent variables (PCS, MCS, and traditional clinical variables) and 6-month mortality using simple logistic regression. Independent variables associated with 6-month mortality that had P≤.10 in the univariate logistic regression analysis were considered in the multivariate modeling of 6-month mortality using multiple logistic regression (SAS Proc Logistic). Odds ratios were calculated for each independent variable in the multivariate models, and 95% confidence intervals (CIs) were calculated using maximum likelihood methods.19 Because the magnitude of the odds ratio (OR) for continuous variables depends on the increment of the variable, we chose approximately 1-SD increments for all continuous variables to standardize the comparison.

The goal of our multivariate analyses was to determine the relative magnitude of PCS and MCS scores as predictors of mortality after adjusting for traditional clinical risk variables. To this end, our primary risk model included both HRQL (PCS and MCS scores) and traditional clinical risk variables as candidate independent variables. To confirm the association between PCS and MCS scores and mortality, we developed an alternative risk model using only clinical risk variables as candidate predictors. We then added PCS and MCS separately and tested their significance as predictors of mortality (above and beyond the traditional clinical risk variables) using the likelihood ratio test statistic.20

Discrimination and reliability (calibration) of the multivariate models were assessed by c-index and the Hosmer-Lemeshow goodness-of-fit test statistic, respectively.20,21 The c-index is the percentage of time that patients who died had a higher predicted probability of death than patients who lived for all possible pairs of patients, one of whom lived for at least 6 months and the other of whom died within 6 months.

In Table 1 the baseline characteristics of the 2480 patients who underwent CABG surgery are compared with the 1476 patients excluded because they had no baseline SF-36. In general, patients included in this study had a lower-risk profile than those not included, likely due to the logistical requirements required to obtain a preoperative SF-36. For example, 11% of the study population had urgent or emergency surgical priority vs 40% of the excluded patients.

The mean preoperative PCS score for the study population was 32.6 or 1.74 SDs below the mean for the general US population. The mean preoperative MCS score was 44.0 or 0.6 SDs below the mean for the general US population.

Six-month mortality for the study population was 4.7% (117 deaths) compared with 6.4% (92 deaths) for the excluded patients. In the study population, 69 deaths (60%) occurred within 30 days of surgery. Of the 117 deaths, the Cause of Death Committee classified 69 deaths (59%) as cardiac, 45 (38%) as noncardiac, and 3 (3%) as unknown cause.

Univariate predictors (P≤.10) of 6-month mortality are shown in Table 2. The PCS score was associated with 6-month mortality, with an OR of 1.59 (95% CI, 1.27-2.00; P<.001) for a 10-point lower baseline PCS score. However, MCS score was not associated with 6-month mortality; the OR was 1.04 (95% CI, 0.89-1.22; P=.59) for a 10-point lower baseline MCS score.

Table Graphic Jump LocationTable 2. Univariate Predictors of 6-Month Mortality for CABG

Our primary multivariate risk model, which included both HRQL and clinical variables as candidate predictors of mortality, showed that PCS was a statistically significant predictor of 6-month mortality even after adjusting for traditional clinical risk variables. A 10-point (1 SD) decrement in a baseline PCS score had an OR of 1.39 (95% CI, 1.11-1.77; P=.006). Statistically significant predictor variables in the multivariate risk model included recent myocardial infarction, prior heart surgery, left ventricular ejection fraction of less than 0.25, left ventricular ejection fraction of 0.25 to 0.34, diuretic use, age, 3-vessel coronary artery disease, PCS score, serum creatinine level, and smoking history (Table 3). This model had good discriminative power (c-index, 0.774) and calibration (P=.96 for Hosmer-Lemeshow test statistic).

Table Graphic Jump LocationTable 3. Multiple Logistic Regression Predictors of 6-Month Mortality Following CABG*

Our alternative risk model, in which we added the HRQL variables to a completed model of traditional clinical risk variables, confirmed that PCS scores predicted mortality above and beyond the traditional clinical risk variables. In this model, a 10-point lower PCS score had an OR of 1.32 (95% CI, 1.04-1.69; P=.03). However, when a baseline MCS score was added to the alternative risk model, it was not associated with 6-month mortality, with an OR of 1.09 (95% CI, 0.92-1.29; P=.31) for a 10-point lower baseline MCS score. The power to conclude that MCS did not predict 6-month mortality was estimated to be 82% to detect a difference in the β coefficient of .02.

Preoperative PCS and MCS scores for the study population were below norms for the US population. The mean preoperative PCS and MCS scores correspond to approximately the 9th and 24th percentiles, respectively, of the US general population.15 However, the preoperative scores for the study population were higher than the 1996 VA national averages of 31.1 for PCS and 40.7 for MCS, likely reflecting the relatively lower-risk profile of the patients who underwent CABG surgery in the study population.22

In this study, a preoperative PCS score was an important independent risk factor for 6-month mortality following CABG surgery, even after adjusting for traditional clinical risk factors. The magnitude of risk associated with change in a PCS score was greater than for change in serum creatinine level and smoking history, established risk factors for mortality following CABG surgery.2,4 In multivariate analysis, a 10-point decrement in a baseline PCS score was associated with a 39% increase in the risk of 6-month mortality following CABG surgery. On the other hand, the baseline MCS score did not predict 6-month mortality following CABG surgery, and this study had more than 80% power to draw this conclusion. Therefore, patient self-report of the baseline physical component of health, but not the mental component of health, predicted mortality following CABG surgery in this study population.

The findings of this study are strengthened by its prospective design, large population, and comprehensive set of clinical risk variables considered for risk adjustment. To our knowledge, this study is the first to use the SF-36, a widely used, reliable, and valid health status questionnaire, in relation to CABG surgery. Furthermore, this study quantitates the magnitude of risk associated with the HRQL variables, thereby placing them in context with traditional clinical risk variables.

Although HRQL variables have not been evaluated as predictors of mortality in patients who undergo CABG surgery, several studies provide support for the findings of this study. Functional status measurements, which include some similar items to components of the PCS, have been reported to predict mortality after hospitalization in the elderly.23 In addition, activities of daily living and general health have been reported to predict mortality in patients with congestive heart failure.24 Also, lower PCS scores alone predicted 5-year mortality in patients enrolled in the Medical Outcomes Study.15

Several studies have established that patient-reported HRQL measurements provide information beyond measurement of clinical variables alone in patients with cardiac disease. Permanyer-Miralda et al11 suggested that performance measured by an exercise treadmill may be better predicted by self-perception of health than by conventional interpretation of functional capacity by physicians. Krumholz et al25 reported that the physical function scale of the SF-36 was more responsive to change with angioplasty than Canadian Cardiovascular Society anginal class, concluding that the SF-36 was "capable of depicting the burden of disease and the benefits of treatment across a number of highly valued health states." Chen et al26 evaluated anginal status, the SF-36, and comorbidity in patients with coronary artery disease, and noted that, "our results argue for the inclusion of measurements of patient health-state preferences in addition to those of symptom severity in studies of treatment outcomes."26

The finding that the baseline MCS score was not predictive of mortality was somewhat surprising as previous evidence has linked mental health and cardiac outcomes, including mortality. Depression is an independent risk factor for the development of ischemic heart disease, and has been shown to predict long-term mortality in patients with known coronary artery disease.2729 Furthermore, an MCS score of less than 42 has been associated with clinical depression.15 Even though approximately 47% of the study population had a baseline MCS score of less than 42, the MCS score was not predictive of mortality in this study.

A question may be raised about the necessity and practicality of measuring baseline HRQL in addition to the traditional clinical variables such as left ventricular ejection fraction or serum creatinine. We feel there are several reasons why finding that the baseline PCS score predicts mortality following CABG surgery is important and should be considered for preoperative risk assessment. First, from an epidemiologic standpoint, the PCS score represents an independent risk factor for mortality following CABG surgery, even after adjustment for traditional clinical risk variables. Therefore, a baseline PCS score helps to explain the variance in the outcome above and beyond clinical variables. Second, the magnitude of effect associated with a change in PCS score (39% increase in risk for an approximately 1-SD lower PCS score) is clinically important. Third, the SF-36 is a noninvasive, patient self-report measurement. All previous established risk factors for mortality following CABG surgery are either test results (some invasive) or subjective measurements by a physician. Finding a patient self-report measurement that predicts mortality shows that patients can, in effect, assist in the classification of their risk status. Finally, health status measurements, such as the SF-36, are increasingly available to physicians. For example, the VA now collects annual assessments on veterans using modified versions of the SF-36 and SF-12.22,30 Data like these may become increasingly important to clinicians and patients making clinical decisions when health status can be linked to care outcomes.

Determination of a preoperative PCS score may be useful when counseling patients regarding their risk of mortality in conjunction with traditional clinical measurements. For example, if a patient is at moderate risk for operative mortality from CABG surgery based on traditional clinical variables, he/she may decide against surgery if he/she knows the incremental risk for mortality based on a low PCS score. Alternatively, a patient with significant comorbidity, but a good PCS score, may be considered a better candidate compared with a similar patient with a lower PCS score. Moreover, it is possible that patients with low preoperative PCS scores need additional clinical evaluation or therapy targeting factors that may be contibuting to their lower PCS scores.

Several limitations of this study should be addressed. First, the study population included only patients with a baseline SF-36, conferring a selection bias toward more elective, lower-risk cases. It is often logistically difficult to obtain an SF-36 in urgent/emergency cases. Furthermore, most patients who require urgent/emergency intervention have pressing indications for CABG surgery, such as life-threatening ischemia, which may make patient-reported HRQL information less important in clinical decision making. Therefore, our largely elective surgical study population may reflect a population in which one may consider baseline HRQL in preoperative evaluation. Second, this study does not address determinants of the baseline PCS score. Understanding the specific causes of a low preoperative PCS score may improve clinical interpretation of the score and help to determine if the score can be improved. Traditional clinical risk measurements cannot simply be a substitute for patient self-report of physical health. Identification of the determinants of self-report of physical health remains an important goal for future research. Third, this study was conducted in a VA patient population, which is largely made up of men, and veteran populations appear to have worse health status than nonveteran populations. Both of these factors may limit the generalizability of our findings to non-VA settings.6,22,30

In summary, this study demonstrated that the PCS score from the preoperative SF-36 is an independent predictor of mortality following CABG surgery. The baseline MCS score does not appear to be associated with mortality. In this era of limited resources and expensive testing, preoperative patient self-report of the physical component of health status may be useful in risk stratification and decision making before undergoing CABG surgery.

American Heart Association.  1998 Heart and stroke statistical update. Available at: http://www.amhrt.org/Scientific/Hsstats98. Accessed March 2, 1999.
Jones RH, Hannan EL, Hammermeister KE.  et al.  Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery.  J Am Coll Cardiol.1996;28:1478-1487.
Grover FL, Johnson RR, Marshall G, Hammermeister KE.for the Department of Veterans Affairs Cardiac Surgeons.  Factors predictive of operative mortality among coronary artery bypass subsets.  Ann Thorac Surg.1993;56:1296-1307.
Grover FL, Johnson RR, Shroyer AL.  et al.  Veterans Affairs Continuous Improvement in Cardiac Surgery Study.  Ann Thorac Surg.1994;58:1845-1851.
Ellwood PM. Shattuck lecture on outcomes management.  N Engl J Med.1988;318:1549-1556.
McCarthy M, Shroyer AL, Sethi G.  et al.  Self-report measures for assessing treatment outcomes in cardiac surgery patients.  Med Care.1995;33(suppl):OS76-OS85.
McHorney CA. Generic health measurement: past accomplishments and a measurement paradigm for the 21st century.  Ann Intern Med.1997;127:743-750.
Barr JT. The outcomes movement and health status measures.  J Allied Health.1995;24:13-28.
Ware Jr JE. The status of health assessment, 1994.  Annu Rev Public Health.1995;16:327-354.
Patrick DL, Deyo RA. Generic and disease-specific measures in assessing health status and quality of life.  Med Care.1989;27(suppl):S217-S232.
Permanyer-Miralda G, Alonso J, Anto JM.  et al.  Comparison of perceived health status and conventional functional evaluation in stable patients with coronary artery disease.  J Clin Epidemiol.1991;44:779-786.
Shroyer AL, London MJ, Sethi GK.  et al.  Relationships between patient-related risk factors, processes, structures, and outcomes of cardiac surgical care.  Med Care.1995;33(suppl):OS26-OS34.
Shroyer AL, London MJ, VillaNueva CB.  et al.  The processes, structures, and outcomes of care in cardiac surgery study protocol.  Med Care.1995;33(suppl):OS17-OS25.
Ware J, Snow K, Kosinski M, Gandek B. SF-36 Health Survey: Manual and Interpretation Guide. Boston, Mass: The Health Institute, New England Medical Center; 1993.
Ware J, Kosinski M, Keller S. SF-36 Physical and Mental Health Summary Scales: A User's Manual. 2nd ed. Boston, Mass: The Health Institute, New England Medical Center; 1994.
Ware Jr JE, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek A. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures.  Med Care.1995;33(suppl):AS264-AS279.
Fisher SG, Weber L, Goldberg J, Davis F. Mortality ascertainment in the veteran population.  Am J Epidemiol.1995;141:242-250.
SAS Institute Inc.  SAS/STAT Software, Release 6.07: Changes and Enhancements. Cary, NC: SAS Institute Inc; 1992.
SAS Institute Inc.  Logistic Regression Examples Using the SAS System, Version 6. Cary, NC: SAS Institute Inc; 1995.
Hosmer Jr DW, Lemeshow S. Applied Logistic Regression. New York, NY: Wiley-Interscience; 1989.
Harrell Jr FE, Lee KL, Matchar DB, Reichert TA. Regression models for prognostic prediction.  Cancer Treat Rep.1985;69:1071-1077.
Kazis LE. Health Status of Veterans: Physical and Mental Component Summary Scores (SF-36V), 1996 National Survey of Ambulatory Care Patients Executive Report. Washington, DC: VA Headquarters, Center for Health Quality Outcomes and Economic Research; 1996.
Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients.  JAMA.1998;279:1187-1193.
Konstam V, Salem D, Pouleur H.  et al. for the Left Ventricular Dysfunction Investigators.  Baseline quality of life as a predictor of mortality and hospitalization in 5025 patients with congestive heart failure, SOLVD Investigations.  Am J Cardiol.1996;78:890-895.
Krumholz HM, McHorney CA, Clark L.  et al.  Changes in health after elective percutaneous coronary revascularization.  Med Care.1996;34:754-759.
Chen AY, Daley J, Thibault GE. Angina patients' ratings of current health and health without angina.  Med Decis Making.1996;16:169-177.
Barefoot JC, Helms MJ, Mark DB.  et al.  Depression and long-term mortality risk in patients with coronary artery disease.  Am J Cardiol.1996;78:613-617.
Glassman AH, Shapiro PA. Depression and the course of coronary artery disease.  Am J Psychiatry.1998;155:4-11.
Ford DE, Mead LA, Chang PP.  et al.  Depression is a risk factor for coronary artery disease in men.  Arch Intern Med.1998;158:1422-1426.
Kazis LE. Health Status of Veterans: Physical and Mental Component Summary Scores (SF-12V), 1997 National Survey of Ambulatory Care Patients, Executive Report. Washington, DC: VA Headquarters, Center for Health Quality Outcomes and Economic Research; 1997.

Figures

Figure. Eight Scales of the Short-Form 36 and Their Relationship to Summary Scores
Graphic Jump Location
Figure adapted from Ware et al15 and reproduced with permission.

Tables

Table Graphic Jump LocationTable 1. Comparison of Study Population With Excluded Patients
Table Graphic Jump LocationTable 2. Univariate Predictors of 6-Month Mortality for CABG
Table Graphic Jump LocationTable 3. Multiple Logistic Regression Predictors of 6-Month Mortality Following CABG*

References

American Heart Association.  1998 Heart and stroke statistical update. Available at: http://www.amhrt.org/Scientific/Hsstats98. Accessed March 2, 1999.
Jones RH, Hannan EL, Hammermeister KE.  et al.  Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery.  J Am Coll Cardiol.1996;28:1478-1487.
Grover FL, Johnson RR, Marshall G, Hammermeister KE.for the Department of Veterans Affairs Cardiac Surgeons.  Factors predictive of operative mortality among coronary artery bypass subsets.  Ann Thorac Surg.1993;56:1296-1307.
Grover FL, Johnson RR, Shroyer AL.  et al.  Veterans Affairs Continuous Improvement in Cardiac Surgery Study.  Ann Thorac Surg.1994;58:1845-1851.
Ellwood PM. Shattuck lecture on outcomes management.  N Engl J Med.1988;318:1549-1556.
McCarthy M, Shroyer AL, Sethi G.  et al.  Self-report measures for assessing treatment outcomes in cardiac surgery patients.  Med Care.1995;33(suppl):OS76-OS85.
McHorney CA. Generic health measurement: past accomplishments and a measurement paradigm for the 21st century.  Ann Intern Med.1997;127:743-750.
Barr JT. The outcomes movement and health status measures.  J Allied Health.1995;24:13-28.
Ware Jr JE. The status of health assessment, 1994.  Annu Rev Public Health.1995;16:327-354.
Patrick DL, Deyo RA. Generic and disease-specific measures in assessing health status and quality of life.  Med Care.1989;27(suppl):S217-S232.
Permanyer-Miralda G, Alonso J, Anto JM.  et al.  Comparison of perceived health status and conventional functional evaluation in stable patients with coronary artery disease.  J Clin Epidemiol.1991;44:779-786.
Shroyer AL, London MJ, Sethi GK.  et al.  Relationships between patient-related risk factors, processes, structures, and outcomes of cardiac surgical care.  Med Care.1995;33(suppl):OS26-OS34.
Shroyer AL, London MJ, VillaNueva CB.  et al.  The processes, structures, and outcomes of care in cardiac surgery study protocol.  Med Care.1995;33(suppl):OS17-OS25.
Ware J, Snow K, Kosinski M, Gandek B. SF-36 Health Survey: Manual and Interpretation Guide. Boston, Mass: The Health Institute, New England Medical Center; 1993.
Ware J, Kosinski M, Keller S. SF-36 Physical and Mental Health Summary Scales: A User's Manual. 2nd ed. Boston, Mass: The Health Institute, New England Medical Center; 1994.
Ware Jr JE, Kosinski M, Bayliss MS, McHorney CA, Rogers WH, Raczek A. Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures.  Med Care.1995;33(suppl):AS264-AS279.
Fisher SG, Weber L, Goldberg J, Davis F. Mortality ascertainment in the veteran population.  Am J Epidemiol.1995;141:242-250.
SAS Institute Inc.  SAS/STAT Software, Release 6.07: Changes and Enhancements. Cary, NC: SAS Institute Inc; 1992.
SAS Institute Inc.  Logistic Regression Examples Using the SAS System, Version 6. Cary, NC: SAS Institute Inc; 1995.
Hosmer Jr DW, Lemeshow S. Applied Logistic Regression. New York, NY: Wiley-Interscience; 1989.
Harrell Jr FE, Lee KL, Matchar DB, Reichert TA. Regression models for prognostic prediction.  Cancer Treat Rep.1985;69:1071-1077.
Kazis LE. Health Status of Veterans: Physical and Mental Component Summary Scores (SF-36V), 1996 National Survey of Ambulatory Care Patients Executive Report. Washington, DC: VA Headquarters, Center for Health Quality Outcomes and Economic Research; 1996.
Inouye SK, Peduzzi PN, Robison JT, Hughes JS, Horwitz RI, Concato J. Importance of functional measures in predicting mortality among older hospitalized patients.  JAMA.1998;279:1187-1193.
Konstam V, Salem D, Pouleur H.  et al. for the Left Ventricular Dysfunction Investigators.  Baseline quality of life as a predictor of mortality and hospitalization in 5025 patients with congestive heart failure, SOLVD Investigations.  Am J Cardiol.1996;78:890-895.
Krumholz HM, McHorney CA, Clark L.  et al.  Changes in health after elective percutaneous coronary revascularization.  Med Care.1996;34:754-759.
Chen AY, Daley J, Thibault GE. Angina patients' ratings of current health and health without angina.  Med Decis Making.1996;16:169-177.
Barefoot JC, Helms MJ, Mark DB.  et al.  Depression and long-term mortality risk in patients with coronary artery disease.  Am J Cardiol.1996;78:613-617.
Glassman AH, Shapiro PA. Depression and the course of coronary artery disease.  Am J Psychiatry.1998;155:4-11.
Ford DE, Mead LA, Chang PP.  et al.  Depression is a risk factor for coronary artery disease in men.  Arch Intern Med.1998;158:1422-1426.
Kazis LE. Health Status of Veterans: Physical and Mental Component Summary Scores (SF-12V), 1997 National Survey of Ambulatory Care Patients, Executive Report. Washington, DC: VA Headquarters, Center for Health Quality Outcomes and Economic Research; 1997.
CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Please click the checkbox indicating that you have read the full article in order to submit your answers.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 248

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles
JAMAevidence.com