Summary Health Status Measures in Advanced Heart Failure: Relationship to Clinical Variables and Outcome

Journal of Cardiac Failure Vol. 13 No. 7 2007

Summary Health Status Measures in Advanced Heart Failure: Relationship to Clinical Variables and Outcome MARK D. SULLIVAN, MD, PhD,1 WAYNE C. LEVY, MD,1 JOAN E. RUSSO, PhD,1 BARBARA CRANE, RN,1 AND JOHN A. SPERTUS, MD, MPH2 Seattle, Washington; Kansas City, Missouri

ABSTRACT Background: Patient-centered health status measures are important because they capture the patient’s perspective on their heart failure, but it is unclear which of these have independent prognostic significance. Methods and Results: A total of 142 consecutive subjects from a specialty heart failure clinic were assessed at baseline with a broad array of clinical, laboratory, and self-report measures including four summary measures of health status. The relationships between these measures and their association with the combined end point of transplantation or death over a mean follow-up of 3 years were examined. In unadjusted analyses, the Kansas City Cardiomyopathy Questionnaire (KCCQ) summary score had the strongest association with the combined end point (HR [for each unit score difference] 5 0.98 [0.96e0.99], P 5 .002). In the adjusted Cox proportional hazards model including all 4 summary measures, the Seattle Heart Failure Score, V02, systolic blood pressure, and medical comorbidity, only the Standard Gamble utility remained significantly associated with time to the combined end point (HR [for each 0.01 utility score difference] 5 0.98 [0.97e0.99], P 5 .007). Conclusions: Our study suggests that summary health status measures are simple and significant indicators of prognosis in advanced heart failure patients. The KCCQ summary score summarizes a wide range of clinical variables from the patient’s point of view, whereas the standard gamble utility contains important prognostic information not captured in usual clinical variables. (J Cardiac Fail 2007;13:560e568) Key Words: Health utility, quality of life, prognosis, standard gamble.

embraced as outpatient performance measures of healthcare quality in heart failure (HF) and coronary artery disease.3,4 Summary health status measures may also be important as prognostic indicators of other important outcomes, such as mortality and morbidity. Self-rated health is the most well-studied summary health measure in this regard. A recent meta-analysis demonstrated that persons with ‘‘poor’’ self-rated health had a 2-fold higher mortality risk compared with persons with ‘‘excellent’’ self-rated health after adjusting for potential confounding variables.5 In HF patients, disease-specific health status measures such as the Kansas City Cardiomyopathy Questionnaire (KCCQ) summary score have also been shown to independently predict hospitalization or death.6,7 Health state utility measures, including the standard gamble (SG) and the feeling thermometer (FT), allow patients to assign a single value to their current health along a continuum of 0 for death to 1 for perfect health. Although health status measures describe the patient’s health state, health utility measures allow the patient to assign a single value to his health state that is different from these summaries of patients’ symptoms, function, and quality of life in that they explicitly capture the ‘‘value’’ that patients assign to their current health

Health status is defined as the patient’s health state as perceived by the patient. Patient-centered health status measures often explicitly measure patients’ symptoms, function, and health-related quality of life.1 They have become important tools in clinical research and are becoming increasingly important in routine clinical care because they capture the patient’s perspective on their health, a critical goal in achieving the Institute of Medicine’s vision of a patient-centered health care system.2 In fact, the routine assessment of patients’ health status has recently been

From the 1Psychiatry and Behavioral Sciences, University of Washington, Seattle and 2Mid-America Heart Institute, University of Missouri, Kansas City. Manuscript received October 30, 2006; revised manuscript received February 1, 2007; revised manuscript accepted April 9, 2007. Reprint requests: Mark D. Sullivan, MD, PhD, Psychiatry and Behavioral Sciences, Box 356560, University of Washington, 1959 NE Pacific St., Seattle, WA 98195. Supported by a grant (9970185N, PI: Sullivan) from the American Heart Association, Dallas, Texas. 1071-9164/$ - see front matter Ó 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.cardfail.2007.04.001

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status. These preference-based instruments are particularly useful for cost-effectiveness analysis.8 Many investigators regard the SG, a direct preference instrument that assesses the risk of death that the patient would accept to achieve perfect health as the reference standard for valuing health states.9 The FT10 has been demonstrated to have good responsiveness and validity in several studies11 and may have better reliability than the SG.12 Although slightly different, for the purposes of this article, we will use the term summary health status measures to apply to both the health status and health utility measures. To determine if commonly used summary health status measures have independent prognostic significance, we sought to define their association with clinical variables and outcome among patients with advanced HF. We studied the relation of 4 summary health status measures (self-rated health [SF-1], KCCQ summary score, standard gamble, and the feeling thermometer) to baseline clinical variables and to the time for patients to reach a combined endpoint of transplantation or death. We specifically addressed 2 questions in this study: (1) What demographic and clinical measures routinely used to assess prognosis and plan clinical care are most strongly associated with health status and health utility as perceived by the patient with advanced congestive heart failure (CHF)? and (2) Which of these summary health status measures best predict the combined outcome of transplant or death?

Methods



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Measures

Clinical Physiologic. The following physiologic assessments of cardiac function were obtained: (1) left ventricular ejection fraction obtained by echocardiogram using the apical biplane method, (2) peak oxygen consumption (peak VO2) on cardiopulmonary exercise testing. Subjects sat on the ergometer to obtain resting VO2 for at least 2 minutes. Unloaded pedaling at 60 rpm for 2 minutes preceded the initiation of the ramp exercise. The ramp was a continuous 5 to 20 watts/min (mean 13.1 6 3.6) in all heart failure subjects. Data were obtained with a metabolic cart (Medigraphics or Quinton QMC; Seattle, WA) coupled to an electronically braked cycle ergometer. Gas and volume calibrations were performed before each test. Peak VO2 and peak workload (watts) was defined as the highest 60-s average. (3) Serum sodium. Medical comorbidity. To adjust for the burden of comorbid medical illness, the Cumulative Illness Rating Scale-Geriatric (CIRS-G)13 a validated adaptation of the original Cumulative Illness Rating Scale,14 was used to assess the level of medical comorbidity in populations with multiple chronic medical illnesses. Thirteen categories of chronic illness are rated in severity (range 0e4) based on manual-guided chart review by a nurse. The CIRS-G has been used in studies of cancer patients and has been shown to be distinct from functional status.15 Physical examination. To quantify the clinical status of patients, the following physical findings were documented by the attending cardiologist: (1) resting heart rate after the patient had been resting in the supine position for at least 2 minutes, (2) systolic and diastolic blood pressure, and (3) body-mass index, (4) jugular venous pressure, and (5) presence of rales or third heart sound on chest auscultation.

Subjects Consecutive subjects were recruited from the Heart Failure/PreTransplant Clinic at the University of Washington Medical Center over a 2-year period (between April 1, 1999, and March 31, 2001). Eligible patients were contacted and informed consent obtained. Each subject was followed until March 5, 2003. Patients are referred to this clinic by cardiologists and primary care physicians in a 5-state area for the management of advanced HF and potential cardiac transplantation. Subjects were thus free of many of the significant medical comorbidities typically affecting cohorts of HF patients. This permitted a more accurate assessment of the independent association of patients’ health status with advanced HF. Patients referred as inpatients to University of Washington Medical Center were enrolled as study subjects at their first University of Washington Medical Center clinic visit after discharge. To be eligible, heart failure had to represent the greatest medical limitation on daily function for the patient in the judgment of the attending cardiologist. All patients were required to be able to read, write, speak, and understand English at a level that allows them to be interviewed and complete questionnaires. Usual clinical care for subjects in this observational study was not altered. Psychosocial and functional assessment through interview, questionnaires and 6-minute walk tests, were added to the standard clinical assessments. The study was approved by the Human Subjects Review Committee at the University of Washington. All subjects provided informed consent.

Cardiac history (documented by a cardiologist). (1) Cause of cardiomyopathy (idiopathic, ischemic, valvular, alcoholic, hypertrophic, other); (2) history of myocardial infarction; (3) history of revascularization procedure; (4) presence of dyspnea with effort, dyspnea at night, dyspnea at rest, or orthopnea; (5) severity of CHF symptoms as assessed by the Multidimensional Assessment of Fatigue Scale,16 a scale with documented reliability and validity in medical populations.17 The scale was adapted to also assess breathlessness and chest pain. (6) The Seattle Heart Failure Score, a validated measure of heart failure prognosis based on: age, gender, New York Heart Association (NYHA) class, ischemic etiology, ejection fraction, weight, systolic blood pressure, medications/devices (angiotensin-converting enzyme inhibitor, angiotensin receptor blocker, b-blocker, aldosterone blocker, statin, allopurinol, implantable cardiac defibrillator, cardiac re-synchronization therapy, cardiac re-synchronization therapy with defibrillator, and diuretic dose), hemoglobin, percent lymphocytes, serum sodium, uric acid, and total cholesterol. Observed function. The 6-minute walk:18 this is a measurement of the distance walked (without urging) in 6 minutes. It is well validated for use in patients with CHF, predicting both cardiac events and survival.19 It is now the most commonly used performancebased functional measure in CHF.20 The Borg Dyspnea Scale is a 0 to 10 scale with descriptors used to quantify the severity of

562 Journal of Cardiac Failure Vol. 13 No. 7 September 2007 dyspnea. It is well validated for use in CHF.21 The same research nurse administered the 6-minute walk and Borg dyspnea scale in identical fashion to all research subjects. No coaching or encouragement of subjects was done.

Self-reported function. (1) Impairment in role functioning was assessed using the Sheehan Disability Scale. This measure has been validated in both psychiatric and medical populations and uses 3 11-point Likert scales to assess impairment in work, social, and family/home function.22 (2) Ability to work outside the home and number of days per month that the patient needed to cut down on their usual activities because of his or her health were assessed using validated single-item questions.23

Psychiatric. Depression diagnoses were obtained through administration of the Primary Care Evaluation of Mental Disorders (PRIME-MD) psychiatric diagnostic interview24 For these analyses, subjects were classified as having any Diagnostic and Statistical Manual of Mental Disorders-IV25 depression diagnosis (major depression, dysthymia, or minor depression) versus no depression. Severity of depressive symptoms was obtained through both interview (Hamilton Depression Rating Scale26) and selfreport (Symptoms Check List-20 [SCL-2027])measures. The Hamilton Anxiety Rating Scale was also administered. This has also been validated for use in medical populations.28 Summary Health Status Health status. (1) The Medical Outcomes Study Short-Form 36 (SF-36) was used as the generic health status instrument. This well-validated instrument29 has demonstrated validity in heart failure patients.30 Our report focuses on only the first question ‘‘In general, how would you rate your health’’? with response options of ‘‘excellent, very good, good, fair, or poor,’’ which has been extensively studied as a predictor of prognosis. (2) The disease-specific health status instrument used was the KCCQ. It is scored 0 to 100, where higher scores indicate greater function, less symptoms, and higher quality of life. This measure is valid, reliable, and responsive and has been shown to better discriminate compensated and decompensated CHF patients than the SF-36 or the Minnesota Living With Heart Failure Questionnaire.31 We report here on the overall summary score as it is the best distillation of patients’ overall HF-specific health status and has readily interpretable thresholds of change.32 Health utility. (3) The FT is a visual analog scale depicted as a vertical thermometer in which the worst state is dead (a score of 0) and the best state is full health (equal to a score of 100). It has been disseminated as part of the Euroqol (now called the EQ5D) health utility measure.10,33 (4) SG. This instrument offers patients a choice concerning a hypothetical treatment (choice A) that will return the patient to full health (with probability P) for 5 years, but also carries the risk of immediate death (probability 1 e P). The alternative (choice B) is the guarantee that the patient will stay in their own health state, or a marker state for 5 years until death. The interviewer used a chance board with the ping-pong approach varying the probability P in steps of .05 to obtain the value, P*, where the patient considered choice A equal to choice B.34 This indifference probability, P*, is the utility value for the patient’s own health state, ranging from dead (5 0) to full health (5 1). The greater a patient’s willingness to accept a risk of death

to escape their current health state, the lower their health utility rating. Statistical Analyses We compared the groups with and without a combined endpoint on all study demographic, clinical and health variables using ttests for continuous variables and chi-squares with correction for continuity for the categorical ones. Several of the health variables were missing less than 10% of the observations and those data were imputed using a regression technique. Pearson bivariate correlations were calculated for the relationship between the 4 health variables and the demographic and clinical variables and among themselves. Because some of these relationships (eg, with SG utility) are nonlinear, these Pearson correlations will be a conservative estimate of the strength of the relationship. Next 4 multiple linear regression models were fit to determine which set of demographic and clinical variables were most associated with the health status variables. To fit these models, all variables that showed significant correlations (P ! .05) with the individual health outcomes were entered into a model. Then a combination of backwards and forwards stepwise regression techniques was used to arrive at the set of predictors that were statistically significant (P ! .05). Only statistically significant variables were retained. Our last set of analyses used Cox proportional hazards models to examine the relationship between the health status variables and whether the combined endpoint of transplantation or death occurred during the follow-up period. In the first model, we forced all 4 health status variables into the model. However, because of the multicolinearity among the variables none of the individual health status variables was statistically significant. For that reason, we fit 4 individual Cox models, 1 for each summary health status variable. Last, we fit an adjusted Cox model that tested the significance of all 4 health status variables in the presence of significant covariates. All the demographic and clinical variables that were significantly different (P ! .05) between the end point groups were tested in a Cox model as potential covariates using backwards and forwards inclusion techniques. Covariates that were statistically significant (P ! .05) were retained in the model. Summary health status variables significant in the Cox models were dichotomized at the median value and the model was refit in order to generate the hazard functions presented in the figures.

Results Characteristics of the sample are displayed in Table 1. Of 411 patients attending the clinic during the recruitment period, 249 were eligible for the study. Of these 150 provided complete baseline data. The 142 who provided complete baseline and endpoint data are included in this analysis. Among the 249 eligibles, the 142 providing complete data were older (54 versus 52 years) and less likely to be female (22.5 versus 28%). This was a middle-aged sample (mean age 54) with significant left ventricular (mean left ventricular ejection fraction 5 26%) and functional impairment (mean NYHA class 5 2.8). Thirty-nine subjects (28%) experienced either transplantation or death during the 3-year follow-up. This included 24 (17%) who received a cardiac transplant and 15 (11%) who died. Two (2%) died from cancer; 13 (9%) died of cardiovascular causes. Among the 24 patients undergoing cardiac transplant, 5 patients

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Table 1. Sample Characteristics Stratified by Combined End Point Status

Characteristic Demographic Age (y) Men Ethnic minority Clinicaldobserved SHFS New York Heart Association Class Systolic blood pressure (mm Hg) Diastolic blood pressure Resting heart rate (bpm) Body mass index (kg/m2) Serum creatinine Serum sodium Left ventricular ejection fraction (%) Jugular venous pressure (mm Hg) Brain natriuretic peptide (units) Ischemic etiology 6-minute walk (feet) Cumulative Illness Rating Scale Total Score Rales Edema Third heart sound Peak V02 Diabetes mellitus Clinicaldtreatments b-blockers Diuretics ACE inhibitors or receptor blockers Statins Clinicaldself-reported (HF symptoms) Borg MAF breathlessness severity MAF chest pain severity MAF fatigue severity Dyspnea with effort (Y/N) Dyspnea at night (Y/N) Dyspnea at rest (Y/N) Orthopnea (Y/N) Clinicaldself-reported (function) Sheehan role function Any day cut-down activities (days) Working outside the home Clinicaldself-reported (psychological) Any prime-MD depression diagnosis Hamilton Depression Scale Hamilton Anxiety Scale Hopkins Symptom Checklist 20-item depression scale (SCL-20) Self-efficacy disease knowledge Self-efficacy maintain function Self-efficacy change diet Self-efficacy control symptoms Social support Married Summary health status Health status KCCQ summary score SF-1 general health Health utility Feeling thermometer Standard gamble

Total Sample n 5 142

Transplantation or Death During Follow-up n 5 39 (28%)

No Transplantation or Death During Follow-up n 5 103 (72%)

n (%) Mean 6 SD

n (%) Mean 6 SD

n (%) Mean 6 SD

53.2 6 10.1 110 (77.5%) 21 (14.8%)

55.9 6 10.0 30 (76.9%) 6 (15.4%)

52.1 6 10.0 80 (77.7%) 15 (14.6%)

.04 1.00 1.00

0.93 6 0.90 2.7 6 0.7 103.2 6 15.0 61.7 6 10.2 72.1 6 13.2 29.5 6 5.8 1.3 6 0.4 136.8 6 3.0 27.2 6 9.2 6.1 6 5.9 78.9 6 166.8 49 (35%) 1209.6 6 337.0 8.4 6 3.8

1.64 6 0.95 3.0 6 0.6 96.0 6 12.8 56.7 6 9.5 73.2 6 11.8 29.7 6 6.3 1.5 6 0.4 135.6 6 3.4 24.9 6 8.0 8.6 6 4.9 86.5 6 133.5 17 (43.6%) 1139.8 6 315.6 10.8 6 3.9

0.68 6 0.75 2.6 6 0.7 105.8 6 14.9 63.6 6 9.8 71.6 6 13.6 29.4 6 5.6 1.2 6 0.3 137.2 6 2.8 28.1 6 9.5 5.2 6 6.0 76.0 6 178.6 32 (31.1%) 1233.7 6 342.2 7.5 6 3.4

!.001 .002 !.001 !.001 .53 .83 !.001 .004 .06 .002 .75 .17 .16 !.001

11 (7.7%) 36 (25.4%) 74 (52.1%) 16.8 6 4.1 26 (18%)

5 (12.8%) 13 (33.3%) 23 (59.0%) 14.9 6 3.3 14 (35.9%)

6 (5.8%) 23 (22.3%) 51 (49.5%) 17.5 6 4.2 12 (11.7%)

.17 .20 .35 !.001 .003

102 (71.8%) 125 (88.0%) 138 (97%) 55 (39%)

25 36 38 16

(64.1%) (92.3%) (97.4%) (41.0%)

77 (74.8%) 89 (86.4%) 100 (97.1%) 39 (37.9%)

.29 .40 1.00 .88

1.8 6 1.7 15.2 6 12.1 7.8 6 9.4 19.8 6 11.2 123 (87.9%) 17 (12.0%) 14 (9.9%) 26 (18.4%)

2.2 6 1.7 19.3 6 12.2 8.9 6 10.4 24.9 6 10.8 38 (97.4%) 3 (7.7%) 4 (10.3%) 7 (17.9%)

1.7 6 1.7 13.6 6 11.7 7.3 6 9.4 17.9 6 10.7 85 (84.2%) 14 (13.6%) 10 (9.7%) 19 (18.6%)

.18 .01 .39 .001 .06 .50 1.00 1.00

4.8 6 3.2 82 (58.6%) 37 (26.2%)

5.7 6 3.1 30 (76.9%) 7 (17.9%)

52 (51.5%) 30 (29.4%)

.01 .24

41 (29%) 10.3 6 9.5 11.0 6 8.6 1.2 6 0.9

17 (43.6%) 11.3 6 8.8 12.4 6 7.5 1.2 6 0.9

24 (23.3%) 10.0 6 9.8 10.4 6 8.9 1.2 6 0.9

.02 .45 .22 .72

3.1 6 0.7 3.0 6 1.7 3.3 6 1.8 3.9 6 1.8 28.2 6 4.6 104 (73.2%)

3.3 6 0.9 3.1 6 2.2 3.0 6 1.7 4.0 6 1.7 29.2 6 4.8 30 (76.9%)

3.0 6 0.7 2.9 6 1.5 3.3. 6 1.9 3.9 6 1.8 27.9 6 4.6 74 (71.8%)

.10 .43 .40 .84 .16 .69

60.3 6 21.9 33.2 6 23.6

51.2 6 21.5 23.7 6 19.8

63.8 6 21.1 36.8 6 24.0

.002 .003

57.3 6 26.4 72.5 6 25.6

51.2 6 25.2 64.6 6 27.9

59.6 6 26.7 75.7 6 24.1

.13 .03

P Value X2

(1) or t (140)

SD, standard deviation; SHFS, Seattle Heart Failure Score; VO2, peak oxygen consumption; ACE, angiotensin-converting enzyme; HF, heart failure; KCCQ, Kansas City Cardiomyopathy Questionnaire; SF, short form, MAF, multidimensional assessment of fatigue; PRIME-MD, primary care evaluation of mental disorders; SCL-20, symptoms check list-20.

564 Journal of Cardiac Failure Vol. 13 No. 7 September 2007 were United Network of Organ Sharing status 1A at the time of transplant, 9 were status 1B, and 10 were status 2. For each variable, those reaching the combined end point were compared with those not reaching it. For the self-reported heart failure symptom scales, the combined end point group reported significantly worse breathlessness and fatigue severity than the no endpoint group. The combined end point group reported significantly more reduced activity days and was more likely to have a depression diagnosis. Last, the group that reached the combined end point had significantly worse standard gamble, KCCQ summary and General Health mean scores than those who did not reach the endpoint. The combined end point group did have a somewhat lower feeling thermometer mean score (25.2 versus 26.7), but it did not reach significance (P 5 .13). Table 2 shows the bivariate Pearson correlation coefficients between clinical variables and the four summary health status measures. Few of these standard clinical variables were significantly related to these measures and the correlations are generally weak (r 5 0.2). All six correlations among the 4 health status measures were significant (P ! .01). The average intercorrelation is r 5 0.45 with the highest correlation between the KCCQ summary score and the SF-1 General Health item (r 5 0.67) and the lowest correlation between the feeling thermometer and the standard gamble (r 5 0.26). Table 3 shows the bivariate Pearson correlations between patient-reported symptom, functional, and psychologic status and the 4 patient-reported summary health status Table 2. Bivariate Correlations Between Clinical Variables and Patient-Reported Summary Health Status Measures Health Status Measures Age Men Ethnic minority SHFS Ischemic CM CIRS Total Diastolic BP Systolic BP Pulse BMI Rales Edema 3rd heart sound JVD Sodium Creatinine Ejection fraction Diabetes V02 Summary measures Feeling thermometer Standard gamble KCCQ total score

Feeling Standard Thermometer Gamble 0.18* 0.14 0.08 0.21** 0.04 0.01 0.10 0.19* 0.10 0.01 0.09 0.08 0.04 0.10 0.18* 0.05 0.08 0.02 0.11 d d d

KCCQ Total Score

SF-1 General Health

0.04 0.03 0.06 0.08 0.15 0.06 0.09 0.08 0.04 0.07 0.12 0.02 0.15 0.12 0.20* 0.02 0.08 0.14 0.14

0.13 0.09 0.15 0.31*** 0.05 0.15 0.15 0.25** 0.10 0.03 0.01 0.18* 0.03 0.23** 0.20* 0.01 0.01 0.06 0.17*

0.04 0.03 0.12 0.23** 0.06 0.14 0.05 0.04 0.07 0.07 0.04 0.06 0.05 0.19* 0.20* 0.06 0.01 0.16 0.22*

0.26** d d

0.65*** 0.36*** d

0.47*** 0.30*** 0.67***

KCCQ, Kansas City Cardiomyopathy Questionnaire; SF, short form; SHFS, Seattle Heart Failure Score; CM, cardiomyopathy; CIRS, Cumulative Illness Rating Scale-Geriatric; BP, blood pressure; BMI, body mass index; JVD, jugular venous distension; VO2, peak oxygen consumption. *P ! .05, **P ! .01, ***P ! .001.

measures. These correlations are much stronger, up to r 5 0.82 for the KCCQ summary score, r 5 0.63 for feeling thermometer and general health, though only reaching r 5 0.34 for the standard gamble utility. Almost all of the correlations were statistically significant for the feeling thermometer, KCCQ summary score, and the SF-1 General Health item. The standard gamble shows much weaker relationships to symptoms, functional status, and psychologic status. Among symptoms, fatigue shows the strongest association with the summary health status measures. Among functional status measures, the Sheehan Role Function measure shows the strongest relationship. Among psychologic measures, depression and anxiety measures show a stronger relationship than self-efficacy or social support measures. Table 4 describes the multiple linear regression models for the four health status variables using the significant clinical variables from Tables 2 and 3. The KCCQ summary score is significantly associated with 7 different clinical variables, which together account for 87% of its variance. These include lower NYHA class, dyspnea with effort, chest pain, fatigue, and breathlessness severity, Sheehan role functioning interference and SCL depression. The feeling thermometer is significantly associated with four different clinical variables, which together account for 47% of the variance. Higher feeling thermometer scores are associated with lower NYHA class and Sheehan role functioning interference, higher self-efficacy disease knowledge, and not having a depression diagnosis. The SF-1 General Health score is related to 3 clinical variables: dyspnea with effort, fatigue severity, and the ability to work outside the home. These variables account for 46% of the variance in the SF-1 score. The standard gamble utility rating is significantly associated with 2 clinical variables (serum sodium and breathlessness severity) accounting for 13% of the variance in this score. We fit 4 individual Cox proportional hazards models, 1 for each summary health status variable. All the models with the exception of feeling thermometer were statistically significant indicating they were related to time to combined end point: FT (HR [hazard ratio] 5 0.99 [0.98e1.00], P 5 .07); SG (HR 5 0.98 [0.97e0.99], P 5 .01); KCCQ summary (HR 5 0.98 [0.96e0.99], P 5 .002); and SF-1 General Health (HR 5 0.98 [0.96e0.99], P 5 .004). A stepwise model including all 4 summary health status measures, but no clinical covariates indicated that the KCCQ summary score had the strongest association with the end point. Figure 1 presents the Cox proportional hazard plot for groups above and below the median KCCQ summary score of 59. Note that all the hazard ratios are per 1 unit change in these 100-point scales, which explains why the effects are all close to 1. When all 4 summary measures were tested in models that also controlled for significant clinical variables, the only significant measure was the standard gamble (HR 5 0.98 [0.97e0.99] per unit change, P 5 .007). The adjusted Cox proportional hazards model for the standard gamble

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Table 3. Bivariate Correlations Between Symptom, Functional, and Psychologic Status and Patient-Reported Summary Health Status Measures Symptoms

Feeling Thermometer

Borg dyspnea after 6-minute walk Breathlessness severity Chest pain severity Fatigue severity Dyspnea with effort (Y/N) Dyspnea at night (Y/N) Dyspnea at rest (Y/N) Orthopnea (Y/N) Functional status 6-minute walk (feet) New York Heart Association class Sheehan role function (average) Any cut-down activity (days) Working outside home Psychological factors Hamilton anxiety (interview) Hamilton depression (interview) SCL-20 depression (questionnaire) Any PRIME-MD depression diagnosis Self-efficacy disease knowledge Self-efficacy maintain function Self-efficacy control symptoms Self-efficacy change diet Social support Married

Standard Gamble

KCCQ Total Score

SF-1 General Health

0.46*** 0.43*** 0.28*** 0.52*** 0.24** 0.22** 0.27*** 0.30***

0.20* 0.34*** 0.15 0.31*** 0.22* 0.08 0.16* 0.08

0.59*** 0.74*** 0.55*** 0.82*** 0.44*** 0.35*** 0.28*** 0.37***

0.42*** 0.52*** 0.25** 0.63*** 0.40*** 0.20* 0.17* 0.24**

0.45*** 0.43*** 0.61*** 0.28*** .24**

0.13 0.21* 0.32*** 0.09 0.16

0.53*** 0.52*** 0.83*** 0.40*** 0.26**

0.34*** 0.39*** 0.57*** 0.34*** 0.26**

0.48*** 0.52*** 0.49*** 0.47*** 0.29*** 0.36*** 0.30*** 0.18* 0.16 0.06

0.08 0.09 0.22* 0.09 0.01 0.15 0.23** 0.12 0.07 0.08

0-.60*** 0.65*** 0.70*** 0.48*** 0.22** 0.35*** 0.46*** 0.24** 0.15 0.07

0.49*** 0.47*** 0.47*** 0.35*** 0.06 0.26** 0.19** 0.16 0.06 0.05

SCL-20, Symptoms Check List-20; PRIME-MD, Primary Care Evaluation of Mental Disorders. *P ! .05, **P ! .01, ***P ! .001.

([HR 5 0.98 [0.97e0.99] per unit change, P 5 .001) controlled for Seattle Heart Failure Score (HR 5 1.74 [1.16e2.62] per unit change, P 5 .001), V02 (HR 5 0.89 [0.81e0.98] per unit change, P 5 .02), systolic blood pressure (HR 5 0.97 [0.94e0.99] per unit change, P 5 .02), and total CIRS score (HR 5 1.18 [1.08e1.28] per unit change, P 5 .007). Figure 2 shows the Cox proportional hazard plot for groups above and below the median standard gamble score of 0.85 adjusted for the covariates. Discussion This study demonstrates that among outpatients with advanced HF, 4 commonly used summary health status

measures differ in their association with baseline clinical variables and the combined end point of death or transplantation. In unadjusted models, the KCCQ summary score was most strongly associated with the combined end point of transplantation or death. This is likely because the KCCQ summary score is strongly related to many other clinical variables such as the Seattle Heart Failure Score, symptom severity, functional status, and depression severity. In models adjusted for other clinical variables significantly associated with the combined end point, the standard gamble utility score remained the only 1 of the 4 summary health status measures significantly associated with the combined end point. This suggests that the personal value assigned by HF patients to their health state

Table 4. Standardized Betas for the Multiple Regression Models Predicting Summary Health Status Scores Health Status Measures NYHA class Sodium Dyspnea with effort MAF chest pain severity score MAF fatigue severity score MAF breathlessness severity Sheehan role function average Ability to work outside home Self-efficacy disease knowledge Any depression diagnosis SCL-20 depression

Feeling Thermometer

Standard Gamble

0.22**

KCCQ Total Score

SF-1 General Health

0.10* 0.16*

0.33*** 0.40***

0.10* 0.13*** 0.13* 0.13* 0.42***

0.23*** 0.69*** 0.13**

0.15* 0.22** F(4,134) 5 31.54*** r2 adj 5 0.47

F(2,138) 5 11.66*** r2 adj 5 0.13

0.21*** F(8,128) 5 112.68*** r2 adj 5 0.87

F(3,134) 5 117.70*** r2 adj 5 0.46

KCCQ, Kansas City Cardiomyopathy Questionnaire; SF, short form; NYHA, New York Heart Association; MAF, multidimensional assessment of fatigue; SCL, symptoms check list; adj, adjusted. *P ! .05, **P ! .01 ***P ! .001.

566 Journal of Cardiac Failure Vol. 13 No. 7 September 2007 1.0

KC SUMMARY DIC AT MEDIAN OF 59 0 - 59.00 59.01+

0.9

0.8

0.7

0.6 0

200

400

600

800

1000

1200

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Fig. 1. Time to outcome for patients above and below median Kansas City Cardiomyopathy Questionnaire summary score.

(compared with death) has prognostic significance after controlling for a broad array of clinical variables. This suggests that the patient perceives something prognostic about their health state not captured by current clinical or selfreport measures used in HF care. Our findings are consistent with research in other populations that find summary measures (such as self-rated health) to be independent predictors of mortality.5 It is also consistent with a recent study of 547 patients with heart failure, which showed the KCCQ summary score to be an independent predictor of the combined end point of death or HF admission, after adjusting for baseline patient characteristics (including demographic, physical exam, and comorbidity measures), and 6-minute walking distance.6 It is important to remember, when interpreting our Survival Function 1.0

0.9

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ABOVE MEDIAN 0

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Fig. 2. Time to outcome for patients above and below median standard gamble score.

findings, that the KCCQ differs from the other summary health status measures assessed in that it is a 23-item disease-specific measure, whereas the other measures are single-item generic measures. These differences would be expected to increase the sensitivity and predictive power of the KCCQ. Our study also suggests that the standard gamble utility is associated with outcome in patients with advanced HF. Health utility scores derived through the standard gamble technique (in which patients are asked what risk of death they would accept to achieve perfect health) have been considered the most valid measure of health state utility on theoretical grounds, but have been found to be subject to framing effects and other biases inherent in empirical studies.35 Although the feeling thermometer is often treated as a health utility measure, it does not meet classic Von Neumann-Morgenstern criteria for assessing decisions under uncertainty.9 In our study, the standard gamble utility was significantly, but not strongly (only 13% of variance accounted for), related to clinical variables in our multivariable model (ability to work outside the home [r 5 0.1] and breathlessness [r 5 e0.05]). Although previous studies have shown that utility scores derived through standard gamble, time tradeoff, or visual analog scales (such as the FT) are correlated with clinical variables, including jugular venous pressure, VO2, functional class, and Minnesota Living With Heart Failure scores,36 the prognostic significance of the standard gamble has not been previously demonstrated. In our study, lower standard gamble utilities may have predicted time to the combined end point of transplantation or death because those with lower utility scores were more willing to gamble on a transplant, the etiology of more than half of our outcomes. Supporting this possibility is that the mean utility scores were nonsignificantly (c2 5 1.70, P 5 .19) lower in those who were transplanted (0.60 6 0.30) than it was in those who died (0.72 6 0.234) or those who did not reach either end point (0.76 6 0.24). The most well-investigated patient-reported summary health measure is the SF-1 self-rated health item, which is an independent predictor of mortality over periods as long as 20 to 25 years.37 The ability of self-rated health to independently predict mortality and functional decline is not fully explained, but has been attributed to associations with factors such as allostatic (metabolic stress) load,38 preventive health behaviors,39 and social participation.40 In our study, standard gamble scores may be associated with prognosis because of their association with risk tolerance or their association with factors also associated with self-rated health. This study has a number of important limitations. First, it is a relatively small sample of patients from a single university HF clinic. The generalizability of these findings to other samples remains to be demonstrated. Second, these patients are younger and have less comorbid illnesses than most HF patients. This means that the findings may not apply to older patients with high rates of comorbidity, but it also allowed us to more specifically assess the effects

Summary Health Status in Heart Failure

of HF on health status. Third, the independent variables were all assessed once at the beginning of the mean 3-year follow-up period. Repeated assessments over time, including responsiveness to therapies, may have provided clinical measures with more predictive power. Fourth, we investigated time to a composite end point of transplantation or death. Ten of the 39 patients who reached this end point were United Network of Organ Sharing status 2 at the time of their transplantation. In these less critically ill patients, transplantation might be prompted, in part, by patients’ willingness to endure risk to improve their outcomes and may account for the association of the standard gamble with our combined clinical outcome. The nonsignificant trend for those undergoing transplantation to have lower SG scores (and thus to be less risk-averse) supports this possibility. Summary health status measures are clinically important for 2 reasons. First, they capture what is important to patients. Both health related quality of life and health utility measures reflect patient experience and patient values.41,42 Second, our study suggests that these measures are simple and significant indicators of prognosis in HF patients. The KCCQ summary score does a good job of summarizing a wide range of clinical variables from the patient’s point of view, whereas the standard gamble appears to capture important prognostic information not captured in usual clinical variables, including the KCCQ. This finding concerning the standard gamble is preliminary and stands in need of replication in a larger and more representative sample. Future research should clarify the nature and strength of the prognostic power of these summary health status measures for HF patients. Better understanding the independent prognostic significance of such measures, including the role of health utilities, could open new avenues for improving HF prognosis and treatment.

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