Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-26T06:21:01.163Z Has data issue: false hasContentIssue false

Quality of life measures (EORTC QLQ-C30 and SF-36) as predictors of survival in palliative colorectal and lung cancer patients

Published online by Cambridge University Press:  30 September 2009

Gunn E. Grande*
Affiliation:
School of Nursing, Midwifery & Social Work, The University of Manchester, Manchester, United Kingdom
Morag C. Farquhar
Affiliation:
General Practice & Primary Care Research Unit, University of Manchester, Institute of Public Health, Manchester, United Kingdom
Stephen I.G. Barclay
Affiliation:
General Practice & Primary Care Research Unit, University of Manchester, Institute of Public Health, Manchester, United Kingdom
Christopher J. Todd
Affiliation:
School of Nursing, Midwifery & Social Work, The University of Manchester, Manchester, United Kingdom
*
Address correspondence and reprint requests to: Gunn E. Grande, School of Nursing, Midwifery and Social Work, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester M13 9PL, United Kingdom. E-mail: gunn.grande@manchester.ac.uk

Abstract

Objective:

Self-reported health-related quality of life (HRQoL) is an important predictor of survival alongside clinical variables and physicians' prediction. This study assessed whether better prediction is achieved using generic (SF-36) HRQoL measures or cancer-specific (EORTC QLQ-C30) measures that include symptoms.

Method:

Fifty-four lung and 46 colorectal patients comprised the sample. Ninety-four died before study conclusion. EORTC QLQ-C30 and SF-36 scores and demographic and clinical information were collected at baseline. Follow-up was 5 years. Deaths were flagged by the Office of National Statistics. Cox regression survival analyses were conducted. Surviving cases were censored in the analysis.

Results:

Univariate analyses showed that survival was significantly associated with better EORTC QLQ-C30 physical functioning, role functioning, and global health and less dyspnea and appetite loss. For the SF-36, survival was significantly associated with better emotional role functioning, general health, energy/vitality, and social functioning. The SF-36 summary score for mental health was significantly related to better survival, whereas the SF-36 summary score for physical health was not. In the multivariate analysis, only the SF-36 mental health summary score remained an independent, significant predictor, mainly due to considerable intercorrelations between HRQoL scales. However, models combining the SF-36 mental health summary score with diagnosis explained a similar amount of variance (12%–13%) as models combining diagnosis with single scale SF-36 Energy/Vitality or EORTC QLQ-C30 Appetite Loss.

Significance of results:

HRQoL contributes significantly to prediction of survival. Generic measures are at least as useful as disease-specific measures including symptoms. Intercorrelations between HRQoL variables and between HRQoL and clinical variables makes it difficult to identify prime predictors. We need to identify variables that are as independent of each other as possible to maximize predictive power and produce more consistent results.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Camilleri-Brennan, J. & Steele, R.J.C. (2001). Prospective analysis of quality of life and survival following mesorectal excision for rectal cancer. British Journal of Surgery, 88, 16171622.Google Scholar
Campbell, M.J. (2001). Statistics at Square Two: Understanding Modern Statistical Applications in Medicine. London: BMJ Books.Google Scholar
Coates, A., Porzsolt, F. & Osoba, D. (1997). Quality of life in oncology practice: Prognostic value of EORTC QLQ-C30 scores in patients with advanced malignancy. European Journal of Cancer, 33, 10251030.CrossRefGoogle ScholarPubMed
Dancey, J., Zee, B., Osoba, D., et al. (1997). Quality of life scores: An independent prognostic variable in a general population of cancer patients receiving chemotherapy. Quality of Life Research, 6, 151158.Google Scholar
den Daas, N. (1995). Estimating length of survival in end-stage cancer: A review of the literature. Journal of Pain and Symptom Management, 10, 548555.Google Scholar
Dharma-Wardene, M., Au, H.J., Hanson, J., et al. (2004). Baseline FACT-G score is a predictor of survival for advanced lung cancer. Quality of Life Research, 13, 12091216.Google Scholar
Efficace, F., Bottomley, A., Coens, C., et al. (2005). Does a patient's self-reported health-related quality of life predict survival beyond key biomedical data in advanced colorectal cancer? European Journal of Cancer, 42, 4249.CrossRefGoogle ScholarPubMed
Efficace, F., Bottomley, A., Smit, E.F., et al. (2006). Is a patient's self-reported health-related quality of life a prognostic factor for survival in non-small-cell lung cancer patients? A multivariate analysis of prognostic factors of EORTC study 08975. Annals of Oncology, 17, 1698–704.Google Scholar
EORTC Study Group on Quality of Life. (1995). EORTC Scoring Manual. Brussels: EORTC.Google Scholar
Farquhar, M., Grande, G., Todd, C., et al. (2002). Defining patients as palliative: Hospital doctors' versus general practitioners' perceptions. Palliative Medicine, 16, 247250.CrossRefGoogle ScholarPubMed
Fowlie, M., Berkeley, J. & Dingwall-Fordyce, I. (1989). Quality of life in advanced cancer: The benefits of asking the patient. Palliative Medicine, 3, 5559.Google Scholar
Glare, P., Virik, K., Jones, M., et al. (2003). A systematic review of physicians' survival predictions in terminally ill cancer patients. British Medical Journal, 327, 195201.Google Scholar
Grande, G.E., Todd, C.J. & Barclay, S.I.G. (1997). Support needs in the last year of life: Patient and carer dilemmas. Palliative Medicine, 11, 202208.CrossRefGoogle ScholarPubMed
Herndon, J.E., Fleishman, S., Kornblith, A.B., et al. (1999). Is quality of life predictive of the survival of patients with advanced nonsmall cell lung carcinoma? Cancer, 85, 333340.Google Scholar
Hosmer, D.H. & Lemeshow, S. (2000). Applied Logistic Regression, 2nd ed.New York: Wiley.CrossRefGoogle Scholar
Jenkinson, C., Layte, R. & Lawrence, K. (1997). Development and testing of the SF-36 summary scale scores in the United Kingdom: Results from a large scale survey and clinical trial. Medical Care, 35, 410416.CrossRefGoogle Scholar
Langendijk, H., Aaronson, N.K., de Jong, J.M., et al. (2000). The prognostic impact of quality of life assessed with the EORTC QLQ-C30 in inoperable non-small cell lung carcinoma treated with radiotherapy. Radiotherapy & Oncology, 55, 1925.CrossRefGoogle ScholarPubMed
Lis, C.G., Gupta, D., Granick, J., et al. (2006). Can patient satisfaction with quality of life predict survival in advanced colorectal cancer? Supportive Care in Cancer, 14, 11041110.CrossRefGoogle ScholarPubMed
Llobera, J., Esteva, M., Rifa, J., et al. (2000). Terminal cancer, duration and prediction of survival time. European Journal of Cancer, 36, 20362043.Google Scholar
Maisey, N.R., Norman, A., Watson, M., et al. (2002). Baseline quality of life predicts survival in patients with advanced colorectal cancer. European Journal of Cancer, 38, 13511357.Google Scholar
Maltoni, M. & Tassinari, D. (2004). Prognostic assessment in terminally ill cancer patients: From evidence-based knowledge to a patient-physician relationship and back. Palliative Medicine, 18, 7779.CrossRefGoogle ScholarPubMed
Manser, R.L., Wright, G., Byrnes, G., et al. (2006). Validity of the Assessment of Quality of Life (AQoL) utility instrument in patients with operable and inoperable lung cancer. Lung Cancer, 53, 217229.Google Scholar
Montazeri, A., Milroy, R., Hole, D., et al. (2001). Quality of life in lung cancer patients, as an important prognostic factor. Lung Cancer, 31, 233240.Google Scholar
Nagelkerke, N.J.D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78, 691692.CrossRefGoogle Scholar
Oxenham, D. & Cornbleet, M.A. (1998). Accuracy of prediction of survival by different professional groups in a hospice. Palliative Medicine, 12, 117118.Google Scholar
Regan, J., Yarnold, J., Jones, P.W., et al. (1991). Palliation and life quality in lung cancer: How good are clinicians at judging treatment outcome? British Journal of Cancer, 64, 396400.CrossRefGoogle ScholarPubMed
Siegel, S. & Castellan, N.J. (1988). Non-parametric Statistics for the Behavioural Sciences, 2nd ed.Singapore: McGraw-Hill.Google Scholar
Tamburini, M., Brunelli, C., Rosso, S., et al. (1996). Prognostic value of quality of life scores in terminal cancer patients. Journal of Pain and Symptom Management, 11, 3241.Google Scholar
Toscani, F., Brunelli, C., Miccinesi, G., et al. (2005). Predicting survival in terminal cancer patients: Clinical observation or quality-of-life evaluation? Palliative Medicine, 19, 220227.Google Scholar
Vigano, A., Dorgan, M., Buckingham, J., et al. (2000). Survival prediction in terminal cancer patients: A systematic review of the medical literature. Palliative Medicine, 14, 363374.Google Scholar
Ware, J.E. & Sherbourne, C.D. (1992). The MOS 36-item Short Form Health Survey (SF-36), 1. Conceptual framework and item selection. Medical Care, 30, 473483.Google Scholar