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Health Status Measurements at Diagnosis As Predictors of Survival Among Adults With Brain Tumors

Helen McCarter, William Furlong, Anthony C. Whitton, David Feeny, Sonja DePauw, Andrew R. Willan, Ronald D. Barr

From The Juravinski Cancer Centre; Department of Clinical Epidemiology and Biostatistics; Department of Pediatrics; Centre for Health Economics and Policy Analysis, McMaster University; McMaster Children's Hospital, Hamilton Health Sciences, Hamilton; Health Utilities Inc, Dundas; Institute of Health Economics; Departments of Economics and Public Health Sciences, University of Alberta, Edmonton; Program in Population Health Sciences, Hospital for Sick Children, Toronto, Canada; and Kaiser Permanente Northwest Center for Health Research, Portland, OR

Address reprint requests to Ronald D. Barr, MD, Health Sciences Centre, Room 3N27B, 1200 Main St W, Hamilton, Ontario, L8S 4J9, Canada; e-mail: rbarr{at}mcmaster.ca

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... Author Contributions REFERENCES

 
PURPOSE: The intent of this study was to determine whether baseline measures of functional capacity and performance could be used to predict survival in adults following the diagnosis of brain tumors.

PATIENTS AND METHODS: Comprehensive health status and health-related quality of life (HRQL) were measured using the Health Utilities Index (HUI; McMaster University, Hamilton, Canada) system by a self-assessment questionnaire in a survey of 100 consecutive patients. The Karnofsky Performance Score (KPS) and Folstein's Mini-Mental State Examination (MMSE) scores were measured by a physician blinded to the HUI results. The patients were observed for up to 5 years to recorded dates of death.

RESULTS: An HUI questionnaire was completed for 93% of the patients and 69% died within 5 years of assessment. The HUI revealed a burden of morbidity and complexity of disability that far exceeded that reported for the general population. KPS and MMSE correlated strongly with each other (r = 0.52; P < .001). A decrease of 0.1 units in HUI Mark 2 (HUI2) self-care single-attribute utility score was associated with an increased hazard of death of 30% (P = .023) for patients with low-grade tumors (n=25). For patients with high-grade tumors (n=56), a 10 unit decrease in the KPS, a 5 unit decrease in MMSE, and a 0.1 decrease in HUI Mark 3 (HUI3) speech and dexterity single-attribute scores were associated with an increased hazard of death of 20% (P = .022), 26% (P = .015), 36% (P = .021), and 18% (P = .035), respectively.

CONCLUSION: Scores derived from the measurement of HRQL following diagnosis can predict survival in adults with brain tumors.

	INTRODUCTION

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There is growing interest in self-ratings of health as predictors of mortality in the general population.¹ A preference-based instrument, the Health Utilities Index (HUI; McMaster University, Hamilton, Canada), demonstrated validity in this regard in a large cohort in the United States.² Such quality of life scores are independent prognostic variables in mixed groups of patients with cancer³ and have been predictive of survival, using various measures, in metastatic non–small-cell lung cancer,⁴ metastatic breast cancer⁵ and relapsed disease,⁶ metastatic malignant melanoma,⁷ advanced colorectal cancer,⁸ advanced/metastatic bladder cancer,⁹ and aggressive lymphoma.¹⁰ However, the ability of self-assessments of health status to predict survival in adult patients with brain tumors has been evaluated only rarely.^11-14

The comprehensive health status of brain tumor patients after completion of treatment, measured using the HUI,^15-17 has been described previously in both adults¹⁸ and children.^19,20 This prospective study was designed to measure the health status of adults following the diagnosis of brain tumors using the HUI, Karnofsky Performance Score (KPS),²¹ and Folstein's Mini-Mental State Examination (MMSE)²²; and to assess the ability of the measurements at baseline to predict survival.

	PATIENTS AND METHODS

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Patients
One hundred consecutive and newly diagnosed patients (older than 18 years old) with primary brain tumors who attended the Juravinski Cancer Centre (JCC; Hamilton, Canada) from February 1995 to October 1996 were invited to participate in the study. JCC is a tertiary care center with a regional neuro-oncology program and a geographically defined catchment area with a population of 1.7 million. All patients signed consent forms, which were approved by the Research Ethics Board of McMaster University and Hamilton Health Sciences (both in Hamilton, Canada), in order to participate in the study.

Health and Life Status Measurement
The baseline measurements were collected from each patient after diagnosis, but before the appointment with an oncologist and treatment with radiation therapy or chemotherapy. The assessments were used to describe their health status and health-related quality of life (HRQL), to compare their burden of morbidity with that of the general population, and to investigate associations between patient-reported (HUI) and physician-reported (KPS and MMSE) measurements. The last update on life status for a patient was a minimum of 5 years after baseline assessment date. Survival status was determined by calls to family doctors or other regional cancer centers, calls to patients' families and, as a last resort, by application to the Office of the Registrar General in Ontario for a death certificate. The prognostic power of the health status measures was assessed in terms of the duration of survival from diagnosis to death, censoring those patients still alive after 5 years of follow-up. It was expected that the overall 5-year survival rate would be less then 50%, because the most common primary brain tumor in adults is glioblastoma multiforme, which has a median survival of less than 1 year.²³

Each participating patient was asked to complete a 15-item self-administered health status questionnaire designed to collect sufficient data to classify each participant's usual health status according to both the HUI Mark 2 (HUI2) and HUI Mark 3 (HUI3) systems.²⁴ The assessing physician, who was blinded to the patient's self-assessment, completed the patient's KPS and MMSE. HUI questionnaires measure an individual's capacity rather than performance. KPS and MMSE are measures of performance.

From the responses to the HUI questionnaire, a level for each of six attributes in HUI2 and for each of the eight attributes in HUI3 was determined using an algorithm described by Furlong et al.²⁵ The HUI2 attributes measured are sensation (vision, hearing, speech), mobility, emotion, cognition, self-care, and pain. Each attribute has four or five descriptive levels. The attributes measured by the HUI3 each have five or six levels and are vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. In the evolution of HUI, HUI3 dexterity replaced HUI2 self-care. The constructs for emotion, cognition, and pain differ across the two systems. HUI2 emotion is focused on worry and anxiety, cognition on learning new information, and pain on frequency of pain and analgesic use. HUI3 emotion focuses on happiness and interest in life, cognition on memory and thinking, and pain on severity and frequency of disruption of activities.

HUI3 has been included in numerous general population health surveys.^17,24,26 HRQL scores for comprehensive health states defined by the HUI2 and HUI3 systems are calculated using published multiattribute utility functions derived from preferences of the general population.^15,16 For HUI2, the HRQL utility scale is defined from –0.03 (all attributes at their lowest level) to 0.00 for dead to 1.00 for perfect health (all attributes at level 1). For HUI3, the scale is defined from –0.36 (all attributes at their lowest level) to 0.00 for dead to 1.00 for perfect health.

Single-attribute utility scales are defined such that the lowest level (most disabled) for an attribute has a score of 0.00 and normal (level 1) has a score of 1.00. KPS measures are defined on a scale from 0 (dead) to 100 (normal function) and MMSE measures are defined on a scale from 0 (no neurologic function) to 30 (normal neurologic function).

The HUI3 results from brain tumor patients were compared to HUI3 results about the health of the general population obtained by Statistics Canada during telephone interviews in the 1991 Canadian General Social Survey (GSS).²⁷ The 1991 GSS was a nationwide random sample of the noninstitutionalized population age 15 years and older. The response rate was 80% and the sample size was 11,924 respondents.

Statistical Analyses
Statistical significance was set at the 5% level. Frequency distributions were used to describe categoric-scale measurements and summary statistics (mean, standard deviation [SD], minimum, and maximum) were used to describe the distributions of interval-scale measurements. Student's t tests and analysis of variance (ANOVA) were used to assess differences in means for measures with interval-scale properties. The ² test was used to assess the significance of differences in proportions. Although the MMSE and KPS are continuous measures, scores are generally classified into ranges corresponding with meaningful categories, such as normal, mild, moderate or severe cognitive impairment for the MMSE.²⁸ For the KPS there are 10 categories,²⁹ each of which corresponds to 10-point differences on a 0 to 100 scale. In the context of low-grade glioma, Brown et al³⁰ suggested that differences of three or more in MMSE scores should be regarded as clinically important differences. For HUI, differences equal to or greater than 0.03 between HRQL scores are considered clinically important on account of effective size–based benchmarking, and because differences of 0.03 or greater in HRQL scores are associated with a difference of one level in one attribute between two health-state vectors.^31,32 Differences equal to or greater than 0.05 between mean single-attribute scores are considered important because the descriptive levels within HUI attributes are meaningfully different from each other and the smallest difference in single attribute scores between levels is 0.05.²⁴ The severity of disability for attribute levels (ie, none or mild or moderate or severe) is described using established schemes.²⁵

Pearson (r) correlation coefficients were used to assess associations among KPS, MMSE, and each of the HUI measures. For interpreting results, correlation coefficients were assigned the following degrees of association: 0.00 to 0.19, negligible; 0.20 to 0.34, weak; 0.35 to 0.49, moderate; and 0.50, strong.³³

The proportional hazards model is a statistical technique used to model the ability of variables such as age, sex, and severity of disease to predict the duration of patient survival.³⁴

Proportional hazards modeling, assuming a Weibull distribution, was used to estimate the prognostic effect in terms of increase in hazard of death for a difference of 0.1 in HUI scores (10% of scale), a difference of 10 units in KPS (10% of scale), and a difference of five units in MMSE scores (17% of scale). We did not examine the influence of other variables because of the heterogeneity and limited size of the sample. The forward selection procedure was used to identify the set of independent variables for inclusion in the final models.

	RESULTS

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The study population is broadly representative of populations of patients attending many brain tumor treatment facilities. Four patients declined to participate and three provided incomplete HUI questionnaire data, leaving a study sample of 93 patients. Table 1 shows the demographic and diagnostic characteristics and treatments received after baseline assessments. The most common diagnosis was that of low-grade or high-grade glioma. The tumor was supratentorial in the great majority of patients. Three patients with inaccessible right thalamic, brainstem, and midline tumors did not have surgery. The mean time between surgery and baseline assessment, for the 86 patients who had surgery before health status assessment, was 26 days (SD, 50.0), and the median time was 14 days, with a minimum of 3 days and maximum of 347 days. Table 2 shows the distribution of all patients and the glioma patient groups by type of surgery. Assessment of the prognostic effects of baseline health status and HRQL measures is confounded with the extent of surgical resection, which was related to the histology of the tumor (Table 2).

With the exception of 10 patients, who required the assistance of a caregiver, all patients were able to complete the HUI questionnaire without help. As a whole, this group of patients reported considerable functional disability (levels 2 through 6) compared with the general population, although it should be noted that the general population is not completely free of morbidity (Table 3). As expected, a significantly higher proportion of patients than persons in the general population reported problems in sensation (vision, hearing, and speech), ambulation, dexterity, and cognition. As noted previously in a cross-sectional study of such patients,¹⁸ pain was also reported significantly more often. The attributes most frequently reported to be affected were vision (72% of patients), cognition (51% of patients) and pain (47% of patients). Perhaps surprisingly, the frequency of emotional problems defined by HUI3 in terms of happiness was not significantly greater than that of the general population. However, almost one half of the patients had emotional morbidity based on the HUI2 construct of anxiety.

Two patients were lost to follow-up, although they were both alive and well at 3.5 and 4.5 years after treatment. Both were considered as still alive at the time of last follow-up, for the purpose of analysis. The results of the proportional hazards modeling for time to death are shown in Table 8. The median survival duration was 16.4 months, and 69% (64 of 93 patients) died within 5 years after assessment. Of the 56 patients with high-grade gliomas, 54 patients (94.6%) died; while of the 25 patients with low-grade gliomas, eight patients (32.0%) died; P < .001. Proportional hazards modeling for time to death was carried out controlling for tumor grade. HUI2 self-care (0.1 unit decrease) was associated with a significantly increased hazard for death of 30% (P = .023) among patients with low-grade tumors. The KPS (10-unit decrease), MMSE (five-unit decrease), HU13 speech (0.1-unit decrease), and HUI3 dexterity (0.1-unit decrease) are predictive for survival of patients in the high-grade group (20%, P = .022; 26%, P = .015; 36%, P = .021; and 18%, P = .035 increased hazard of death, respectively), when considered separately. KPS and HUI3 speech are predictive of survival collectively with increased hazards for death of 18% (P = .034) and 33% (P = .038), respectively. MMSE and HU13 dexterity are also predictive of survival collectively, with increased hazards for death of 29% (P < .01) and 20% (P < .02), respectively. Multivariable model hazard ratios are multiplicative. Therefore, a person with a high-grade tumor, a 10-unit decrease in KPS, and a 0.1-unit decrease in HUI3 speech would have a 57% increased hazard for death (1.18 x 1.33 = 1.57). Similarly, a person with a high-grade tumor, a 5-unit decrease in MMSE, and a 0.1-unit decrease in HUI3 dexterity would have a 55% increased hazard for death (1.29 x 1.20 = 1.55).

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... Author Contributions REFERENCES

The majority of patients were able to report on their health status without help, as in our previous study.¹⁸ The burden of morbidity revealed was considerable compared with that in the general population. More than 90% of patients described some morbidity and more than 70% had disability in more than one attribute of health. This is similar to the previously reported data on a comparable patient population seen in follow-up after completion of all treatment.¹⁸ The attribute associated with the most frequent disability, other than needing glasses to correct vision, was cognition. The greater burden of cognitive morbidity revealed by HUI3 rather than HUI2, reflects the greater number of levels in the HUI3 cognition attribute, providing more descriptive power. In addition, cognition has greater weight in the HUI3 multiattribute utility function for calculating overall HRQL scores than in the multiattribute utility function for HUI2. For example, the "corner state" for cognition (the lowest level on cognition and highest level on all other attributes) is 0.20 for HUI3 and 0.63 for HUI2.³⁶ Studies by others have shown also that cognitive function is not predictive of outcome in similar populations of patients, except in older individuals with the highest histologic grade of tumor¹² (glioblastoma multiforme). In a study from Europe of more than 500 patients with glioblastoma multiforme,¹³ cognitive function was predictive of survival independently of age. A more recent report from Brown et al¹⁴ of nearly 200 patients with high-grade gliomas describes the use of various baseline measures to predict survival. Two HRQL instruments (the Linear Analog Self Assessment and Functional Assessment of Cancer Therapy–Brain) were not predictive, nor was the MMSE. The only scale having predictive value was the Symptom Distress Scale assessment of fatigue. In a previous publication,³⁷ these authors reported, in a review of phase II studies of patients with high-grade gliomas, that performance status was predictive of survival, as was the extent of surgical resection. The impact of the extent of resection on survival could not be assessed in the present study because of the relationship between the extent of resection and the histology of the tumor. The likely variability among patients in recovery from surgery is not relevant in this study because it was designed to test whether assessments of health status and HRQL, collected at an initial visit to a multidisciplinary neurooncology clinic, were predictive of survival. The variability in surgical recovery is just one of many features that characterize this initial visit.

As expected, the attributes of cognition in HUI2 and HUI3 correlated with the MMSE. Unexpectedly, cognition also correlated with KPS. The inability to process day-to-day information has an impact on performance, so this relationship does make sense. KPS and MMSE correlate moderately and, for the above reason, this is perhaps to be expected. The attributes of mobility, self-care, and ambulation correlated moderately or strongly with KPS, but dexterity correlated only weakly, which again makes sense as KPS is based largely on the first three attributes. Self-care in HUI2 was replaced by dexterity in HUI3 because of the overlap (lack of structural independence) among self-care and other attributes.³⁸

The complementarity of the HUI2 and HUI3 systems is evident from the association of increased hazard of death with the HUI2 attribute of self-care for those with low-grade tumors and from the association of the HUI3 attribute of dexterity with an increased hazard of death in those with high-grade tumors. In the latter group, the combined KPS/speech and MMSE/dexterity models are evidence for the complementarity of patient- and physician-reported outcomes. It should be emphasized that the HUI questionnaire asked about "usual health status." A short specific recall duration (eg, "past 1 week") might be expected to identify more morbidity and possibly result in more HUI measures with prognostic importance.

While other quality of life scores have been significantly predictive of survival in various populations of adults with cancer, as summarized by Dancey et al,³ only one more recent report included patients with primary brain tumors and it showed that the median survival time was longer for patients with good HRQL than for patients with poor HRQL.³⁹ However, that study had a mixture of patients with brain metastases and patients with primary brain tumors, and only 37% of the patients participated in the study. In another recent report, baseline HRQL data were obtained on almost 500 patients with glioblastoma.⁴⁰ Unfortunately, these data were not analyzed with respect to prediction of survival.

This study is limited by the predetermined sample size of 100 participants, restricting the power of the analyses. Nevertheless, the use of a generic measure of HRQL, in an unselected consecutive sample of adults with brain tumors who attended a population-based referral center for patients with cancer, suggests that the results are generalizable. Repetition of this survey is warranted before such a conclusion can be drawn.

The results of our study reinforce the case for the use of patient-reported health status measures, in addition to standard clinical appraisals that focus on the disease, not only in formal clinical trials but also as a routine tool for the management of patients with cancer. This approach leads us closer to meeting the challenge, articulated by WHO in 1948, of regarding health as more than just the absence of disease or infirmity.

	Authors' Disclosures of Potential Conflicts of Interest

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Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Authors Employment Leadership Consultant Stock Honoraria Research Funds Testimony Other William Furlong Health Utilities Inc (A) David Feeny Health Utilities Inc (A)

Dollar Amount Codes (A) $10,000 (B) $10,000-99,999 (C) $100,000 (N/R) Not Required

	Author Contributions

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION Authors' Disclosures of... Author Contributions REFERENCES

 

Conception and design: William Furlong, David Feeny, Andrew R. Willan, Ronald D. Barr Provision of study materials or patients: Helen McCarter, Anthony C. Whitton Collection and assembly of data: Helen McCarter, Sonja DePauw Data analysis and interpretation: William Furlong, David Feeny, Sonja DePauw, Andrew R. Willan, Ronald D. Barr Manuscript writing: William Furlong, Ronald D. Barr Final approval of manuscript: William Furlong, Anthony C. Whitton, David Feeny, Sonja DePauw, Andrew R. Willan, Ronald D. Barr

 

	ACKNOWLEDGMENTS

 
We thank the patients who participated in the surveys; Gary Foster for proportional hazards modeling; and Baljit Samrai, John Horsman, and Danielle Hunter for data management and analysis.

	NOTES

 
Supported by the Hamilton Health Sciences Foundation. A.R.W. is funded through the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada (Grant No. 44868-03).

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

	REFERENCES

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Submitted February 1, 2006; accepted May 17, 2006.

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