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


METHODS
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.


CONCLUSIONS
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.
Grande
Gunn E
GE
School of Nursing, Midwifery & Social Work, University of Manchester, Manchester, United Kingdom. grande@manchester.ac.uk
Farquhar
Morag C
MC
Barclay
Stephen I G
SI
Todd
Christopher J
CJ
D000368:Aged; D015179:Colorectal Neoplasms / Q000401:mortality / Q000473:pathology / Q000523:psychology* / Q000628:therapy*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000523:psychology* / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D010166:Palliative Care / Q000523:psychology*; D011379:Prognosis; D011581:Psychological Tests / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data; D011788:Quality of Life / Q000523:psychology*; D018434:Sickness Impact Profile; D016019:Survival Analysis