- Email: [email protected]
Quality of life in lung cancer patients
Lung Cancer 31 (2001) 233– 240 www.elsevier.nl/locate/lungcan
Quality of life in lung cancer patients As an important prognostic factor Ali Montazeri a,*, Robert Milroy b, David Hole c, James McEwen a, Charles R. Gillis c a
Department of Public Health, Uni6ersity of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ, UK b Department of Respiratory Medicine, Stobhill Hospital, Glasgow G21 3UW, UK c West of Scotland Cancer Sur6eillance Unit, Department of Public Health, Uni6ersity of Glasgow, 2 Lilybank Gardens, Glasgow G12 8RZ, UK Received 10 February 2000; received in revised form 9 June 2000; accepted 15 June 2000
Abstract Given that lung cancer is one of the common cancers world-wide, the implications of focusing on quality of life as well as survival require to be understood. We have carried out a study of the relationship between survival and quality of life in patients with lung cancer comparing patients those who lived with those who died within 3 months. The design of the study allowed every patient in a defined geographical area with a potential diagnosis of lung cancer to be studied from first outpatient consultation till after a definitive treatment has been given. Quality of life was measured using three standard questionnaires: the Nottingham Health Profile (NHP), the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ– C30) and its lung cancer supplementary questionnaire (QLQ–LC13) in addition to a study specific questionnaire collecting data on demographic, social, clinical and performance status. The contribution of quality of life in relation to survival adjusted for known prognostic factors was determined using Cox’s proportional hazard model. In all 129 lung cancer patients were interviewed, and 96 patients were alive at 3-months follow-up. Only 90 of 96 patients alive at 3-months follow-up were assessable. Descriptive analyses showed that those who were dead had more perceived health problems, greater level of symptoms and significant lower physical and role functioning and global quality of life at presentation. On the other hand, univariate analyses showed that patients’ aggregate scores on the NHP, the functioning scores, and global quality of life scores alone were significant predictors of survival (PB 0.03, P B0.04, PB 0.04, respectively ). The multivariate analyses showed that pre-diagnosis global quality of life was the most significant predictor of the length of survival even after adjusting for known prognostic factors (age, P B 0.04; extent of disease, PB0.03; global quality of life, PB 0.02), while performance status, sex and weight loss were not. This study confirmed that pre-diagnosis quality of life was a significant predictor of survival. Indeed, pre-diagnosis quality of life should be considered as a clinical status which has to be established by physicians before treatment starts as it is such an important predictor of survival. © 2001 Elsevier Science Ireland Ltd. All rights reserved. * Corresponding author. Present address: Iranian Centre for Breast Cancer, PO Box 13185-1488, Tehran, Iran. Tel.: +98-216492431; fax: + 98-21-6418036. E-mail address: [email protected] (A. Montazeri). 0169-5002/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 1 6 9 - 5 0 0 2 ( 0 0 ) 0 0 1 7 9 - 3
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A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
Keywords: Lung cancer; Quality of life; Survival; EORTC QLQ– C30; EORTC QLQ– LC13
1. Introduction Lung cancer is well known as an important public health problem because of its high incidence [1], rapid progression [2], and poor outcome [3]. Thus, measuring quality of life has become an important endpoint of current clinical trials and other studies of lung cancer care. Despite much attention, uncertainty about choice of instruments, study design and analysis in measuring quality of life remains [4,5]. Our review of literature indicates that studies of quality of life have demonstrated harm from much of the treatment offered as well as benefit. Thus, quality of life should become one of the main outcome measures in clinical trials and treatment evaluations [6]. However, findings from more recent studies clearly suggest that effective systematic treatment in selected lung cancer patients can improve both survival and quality of life [7 – 9]. We have been fortunate in carrying out this study in area of high incidence of lung cancer in greater Glasgow where we had access to potential cases of lung cancer coming to outpatient clinics for diagnosis. This paper presents data from a prospective study of quality of life in lung cancer patients where quality of life was assessed pre-diagnosis drawn from a general population as opposed to the selected samples who are entered into clinical trials. The paper reports on the importance of quality of life as a prognostic factor of duration of survival.
2. Patients and methods
2.1. Study design A population-based study of quality of life in patients with lung cancer was conducted during 1 complete calendar year in the northern sector of Glasgow, Scotland. The population of the study area was 169 016 and the average annual number of incident of lung cancer was 150 cases. The
intention was to interview all new lung cancer patients attending the chest clinic of a large teaching and district general hospital (Stobhill NHS Trust). All 167 general practitioners (GPs) in the defined area were asked to give permission for their patients to take part in the study. An administrative officer unconnected with the study personnel obtained all GP requests for the chest clinics and gave a weekly list to A.M., thus blinding him to the potential diagnosis status of the patients. In addition to GP referrals other sources of identification of possible cases were considered. These included the bronchoscopy diary list, pathology results, internal referrals, and the in-patient list in the Respiratory Medicine ward and the Oncology Clinic.
2.2. Questionnaires Quality of life was assessed using three standard questionnaires: 1. The Nottingham Health Profile (NHP), a general health measure including profiles on energy, pain, emotional reactions, social isolation, and physical mobility [10]. 2. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ –C30), a core cancer-specific questionnaire containing 30 items on patients’ functioning, global quality of life, disease- and treatment-related symptoms [11]. 3. The EORTC Lung Cancer Questionnaire (EORTC QLQ –LC13), a site-specific questionnaire consisting of 13 items on lung cancer symptoms and its treatment related side-effects [12]. In addition to the above standard questionnaires the Eastern Co-operative Oncology Group performance scale (ECOG) was used to rate patients’ performance status. It is a five-grade observer rating of patients’ physical ability ranging from 0 (normal) to 4 (disabled) [13]. Finally, a short questionnaire was developed especially for this study to collect data on social
A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
and demographic characteristics of the patients. Clinical information including weight loss, histology, extent of disease, and treatment modalities was extracted from case notes.
2.3. Statistical analysis Mann –Whitney test was used to compare the quality of life measures between those patients who were alive at 3-months follow-up (survivors) and those who were dead. This took into account the fact that the distribution of quality of life scores was skewed [14]. Cox’s regression analysis was performed to assess the relationship between patients’ pre-diagnosis quality of life and survival. The survival time for each patient was the time from the pre-diagnosis interview to the follow-up interview for those who were still alive (at least 3 months) or the date of death for those who had died. In univariate analysis each of the three pre-diagnosis aggregate scores (the NHP, functioning, and global quality of life as measured by the EORTC QLQ –C30) was used as independent variable. In multivariate analysis while adjusting for known prognostic factors (age, sex, performance status, extent of disease and weight loss), pre-diagnosis scores on the NHP, and functioning and global quality of life, Cox’s regression analysis was repeated by selecting the ‘forward conditional’ model. Based on this selection, variables are considered one at time for entry in the model [15].
3. Results Based on data obtained from the local cancer registry there were 133 lung cancer patients registered from the Stobhill Hospital catchment area during the study period. Of these, 129 (97%) patients were interviewed and the remaining four patients were missed.
3.1. Patients’ characteristics The characteristics of patients are shown in Table 1. A total of 77 patients (60%) were male, 52 (40%) were female, the mean age was 67.5
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years (S.D.= 9.1, range 40–87), and patients were mostly (57%) from severely deprived areas. Based on pathology records 27 (21%) patients were diagnosed as having small cell lung cancer, 67 (52%) non-small cell cancer, and the remaining 35 (27%) were clinically diagnosed lung cancer patients. The majority of patients had limited disease (n= 101, 78%). Of the patients, 70% (n = 89) had performance status 0 or 1. There were 81 patients (63%) who had active treatment (chemotherapy, radiotherapy, surgery), while the remaining 48 (37%) received ‘best supportive care’.
3.2. Sur6i6al At follow-up 96 patients were alive. Of these six patients were terminally ill. Thus, the analysis was restricted to 90 survivors and 33 deaths. The median survival time for patients who had died was 40 days (S.D.= 18.7).
3.3. General health Patients’ scores on the Nottingham Health Profile are shown in Table 2. Except for social isolation and sleep disturbance, all other measures were statistically significant confirming that those who were dead had more perceived health problems at presentation.
3.4. Functioning and global quality of life Similar results were found for patients’ functioning and global quality of life as measured by the EORTC QLQ –C30. On three measures (physical and role functioning and global quality of life) there were significant differences between survivors and deaths (Table 3).
3.5. Main symptoms The main symptoms of the disease as measured by the EORTC QLQ –C30 and QLQ –LC13 showed no significant difference between survivors and deaths except for fatigue indicating that both survivors and deaths presented with relatively similar symptoms. However, almost in all measures the deaths had a greater level of symptoms (Table 4).
A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
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Table 1 Lung cancer patients socio-demographic and clinical characteristics at baseline and at 3 months later at follow-up stage (survivors and deaths) Pre-diagnosis sample (n=129) No. (%)
3 months post-diagnosisa (n =90) No. (%)
Deaths (n = 33) No. (%)
Gender Male Female
77 (60) 52 (40)
52 (58) 38 (42)
22 (67) 11 (33)
Age (years) Mean (S.D.)
67.5 (9.1)
66.8 (8.7)
70.0 (9.9)
Marital status Married Widowed/divorced Single
77 (60) 45 (35) 7 (5)
54 (60) 32 (36) 4 (4)
18 (55) 12 (36) 3 (9)
Social depri6ation category Affluent Middle Deprived
23 (18) 32 (25) 74 (57)
18 (20) 21 (23) 51 (57)
5 (15) 9 (27) 19 (58)
Cell type Small cell Non-small cell Unspecified
27 (21) 67 (52) 35 (27)
19 (21) 50 (56) 21 (23)
7 (21) 14 (43) 12 (36)
101 (78) 28 (22)
77 (86) 13 (14)
20 (64) 13 (36)
Extent of disease Limited Extensive Initial treatment Chemotherapy Radiotherapy Surgery Best supportive care
36 39 6 48
(28) (30) (5) (37)
27 31 6 26
(30) (34) (7) (29)
8 6 0 19
(24) (18) (00) (58)
Performance status (score) Normal activity (0) Symptoms (1) Sometimes in bed (2) Need to be in bed (3) Confined to bed (4)
29 60 25 15 0
(23) (47) (19) (11) (00)
23 46 14 7 0
(25) (51) (16) (8) (00)
4 12 10 7 0
(12) (36) (30) (21) (00)
Weight loss Significant weight lossb Possible weight lossc Weight steady No comment
51 12 40 26
(40) (9) (31) (20)
33 8 31 18
(37) (9) (34) (20)
15 4 7 7
(46) (12) (21) (21)
a At 3-months follow-up six patients were in the last stage of their lives and died soon afterwards. Thus, these were excluded from the analysis. b Significant weight loss was defined as significant body weight variation compared to the weight 6 months prior to diagnosis. c Although it was not clear whether a patient had a significant weight loss or not, the consultant commented in the case record that patient had possible weight loss.
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Table 2 Pre-diagnosis scores of lung cancer patients on the Nottingham Health Profile for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Physical mobility Energy Pain Emotional reactions Social isolation Sleep a
P
Survivors (n= 90), mean (S.E.M.)
Deaths (n= 33), mean (S.E.M.)
23.7 33.6 19.9 22.4 11.2 35.5
46.2 63.4 30.9 30.4 13.5 37.7
(2.7) (3.9) (2.9) (2.5) (2.3) (3.3)
(4.7) (7.7) (5.1) (3.9) (3.4) (5.3)
B0.01 B0.01 0.03 0.04 0.36 0.69
The higher values indicate more perceived health problems: min., 0; max., 100. S.E.M. = standard error of mean.
Table 3 Pre-diagnosis functioning and global quality of life scores of lung cancer patients on the EORTC QLQ-C30 for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Physical functioning Role functioning Social functioning Cognitive functioning Emotional functioning Global quality of life a
P
Survivors (n =90), mean (S.E.M.)
Deaths (n =33), mean (S.E.M.)
67.1 64.4 87.0 86.7 78.6 53.5
47.9 42.4 85.3 87.9 80.1 41.2
(2.6) (3.7) (2.2) (2.2) (2.3) (2.3)
(5.0) (6.2) (4.1) (2.7) (3.1) (4.3)
B0.01 B0.01 0.57 0.82 0.92 0.02
The higher values indicate a higher level of functioning and quality of life: min., 0; max., 100. S.E.M. = standard error of mean.
Table 4 Pre-diagnosis scores of main symptoms on the EORTC QLQ-C30 and QLQ-LC13 for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Fatigue Appetite loss Pain (overall) Pain in chest Pain in shoulder Pain elsewhere Cough Dyspnoea Sleep difficulties Hemoptysis a
P
Survivors (n=90), mean (S.E.M.)
Deaths (n = 33), mean (S.E.M.)
32.8 30.4 23.3 19.2 25.9 20.0 43.4 36.3 30.0 8.1
46.5 42.4 36.4 21.9 31.3 36.4 48.5 41.9 25.3 9.6
(3.0) (3.5) (2.6) (5.0) (3.6) (3.1) (6.2) (2.8) (3.8) (3.8)
(5.1) (6.5) (6.3) (3.0) (6.3) (7.3) (3.4) (4.9) (6.2) (2.4)
The higher values indicate a greater degree of symptoms: min., 0; max., 100. S.E.M. =standard error of mean.
0.02 0.11 0.13 0.57 0.48 0.06 0.42 0.35 0.41 0.57
A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
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3.6. Sur6i6al and quality of life To assess whether any of the quality of life scores made an independent contribution to length of survival, Cox’s regression analysis was carried out. Each of the three aggregate scores (pre-diagnosis: 1, NHP; 2, functioning; and 3, global quality of life scores) was divided into good and poor or high and low based on their relation to the mean. Based on separate analysis, patients’ pre-diagnosis scores on the NHP and functioning scores, and global quality of life were found to be significant predictors of survival (P B0.03, P B 0.04, PB 0.04, respectively ). In order to allow for adjustment of known prognostic factors, that is age, sex, performance status, weight loss, and extent of disease, and also pre-diagnosis general health status, functioning, and global quality of life scores, Cox’s regression analysis was repeated. The results indicated that the pre-diagnosis global quality of life as measured by the EORTC QLQ – C30 remained a major significant predictor of length of survival (age, P B0.04; extent of disease, P B0.03; global quality of life, PB0.02). As it is clear, global quality of life provided the best predictor of survival amongst the three quality of life measures (Table 5).
4. Discussion The findings of this study clearly suggest that the pre-diagnosis quality of life is an important
prognostic factor of the length of survival. This is one of the most striking findings in quality of life studies of survival in lung cancer patients. Ruckdeschel et al. [16] in a series of quality of life studies using the Functional Living IndexCancer (FLI-C) reported that the total baseline quality of life score (aggregate score on the FLIC) alongside performance status, weight loss and stage of disease were significant predictors of survival in 438 lung cancer patients. Similarly, Ganz et al. [17] using the same questionnaire but studying only 40 lung cancer patients, found that baseline quality of life was a significant predictor of subsequent survival. Buccheri et al. [18] in a study of 128 lung cancer patients using the Therapy Impact Questionnaire (TIQ) found that in addition to the stage of disease some aspect of quality of life such as difficulty at work and doing household jobs are prognostic factors of improved survival. Recently, Gralla et al. [19] in a study of 673 non-small cell lung cancer patients using the Lung Cancer Symptom Scale (LCSS) observed that the baseline quality of life not only predicts the survival, but also has greater impact than known prognostic factors. Similar findings have been reported for other cancers, for example breast cancer where patients’ physical well-being was found to be directly associated with survival [20]. In a large heterogeneous population of cancer patients the prognostic association between quality of life scores as measured by the EORTC QLQ –C30 and survival was assessed and the results indicated that presence of metastatic disease, diagnosis of lung or ovarian
Table 5 Prediction of survival by the Cox’s regression analysis Relative hazard ratio (HR)
95% CI for HR
P
Uni6ariate analysis NHP alone (poor: good) Functioning alone (low: high) Global quality of life alone (low: high)
3.0 2.1 2.2
1.7–6.2 1.1–4.3 1.1–4.4
B0.03 B0.04 B0.04
Multi6ariate analysis a Age (years) Extent of disease (extensive: limited) Global quality of life (low: high)
1.1 3.0 3.2
1–1.1 1.4–6.5 1.5–6.9
B0.04 B0.03 B0.02
a
Includes adjustment for every known prognostic factors, and the general health status and functioning scores.
A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
cancer, ECOG performance status, global quality of life and emotional functioning were significantly associated with survival. Global quality of life was predictive in all patients, in sub-groups of patients with metastatic disease, with breast and lung cancer and other tumour types [21]. In contrast, a study of patients with malignant melanoma [22] found that pre-treatment global quality of life scores, as measured by the EORTC QLQ –C30, were not predictive of survival. It was argued that these patients did not have advanced disease, as did the lung and breast cancer patients. Thus, it is possible to suggest that pre-treatment quality of life may not have predictive value in all patients with all cancers [23]. Some researchers argue that even for lung cancer patients overall quality of life is not a significant predictor of survival. In a recent publication they showed that after adjustment for significant clinical factors, a patient-provided pain report had the greatest prognostic importance [24]. However the major difference between the present study and those quoted is that we measured quality of life before diagnosis, while the others measured it after the diagnosis was established. Thus, the validity of our measurement of pre-diagnosis quality of life is less likely to be biased by any temporary effects due to patients being informed of their diagnosis of lung cancer and possible prognosis. Our finding that pre-diagnosis quality of life is a significant factor for survival outcome like other known prognostic factors has important implications. Pre-diagnosis assessment of quality of life could help physicians in their clinical decisions as it directly relates to patients’ survival time. Studies have shown that initial prognostic factors can predict quality of life in small cell lung cancer patients during chemotherapy even after controlling for response to treatment [25]. Although some observed changes in quality of life scores over time did not correlate well with objective response to the treatment in non-small cell lung cancer patients, baseline quality of life scores predicted those more likely to respond to treatment [26]. We, therefore argue that assessment of quality of life in lung cancer patients should be integrated into clinical practice and evaluated
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prospectively. The next step is to find how should clinicians introduce quality of life measures in practice and how should the outcome of any resulting treatment be adequately evaluated.
References [1] Parkin DM, Pisani P, Ferlay J. Estimation of the worldwide incidence of 18 major cancers in 1985. Int J Cancer 1993;54:594– 606. [2] Thatcher N, Spiro S, editors. New perspectives in lung cancer. London: BMJ, 1994. [3] Aisner J, Belani CP. Lung cancer: recent changes and expectations of improvements. Semin Oncol 1993;20:383– 9. [4] Hopwood P, Cull A. Quality of life. In: Thatcher N, Spiro S, editors. New perspectives in lung cancer. London: BMJ, 1994:161– 76. [5] Hollen PJ, Gralla RJ, Cox C, Eberly SW, Kris MG. A dilemma in analysis: issues in the serial measurement of quality of life in patients with advanced lung cancer. Lung Cancer 1997;18:119– 36. [6] Montazeri A, Gillis CR, McEwen J. Quality of life in lung cancer patients: a review of literature from 1970– 1995. Chest 1998;113:476– 81. [7] Bunn PA, Jr. Triplet chemotherapy combinations with new agents: is there a rationale? Semin Oncol 1998;25(suppl. 9):55– 61. [8] Bunn PA, Jr, Kelly K. New chemotherapeutic agents prolong survival and improve quality of life in non-small cell lung cancer: a review of the literature and future directions. Clin Cancer Res 1998;4:1087– 100. [9] Cutler PH, Miskovsky NM, Lerner PB, et al. Relationship between quality of life and clinical outcomes in advanced non-small cell lung cancer: best supportive care (BSC) versus BSC plus chemotherapy. Lung Cancer 1999;24:17– 24. [10] Hunt SM, Mckenna SP, McEwen J. The Nottingham Health Profile user’s manual. Manchester, UK: Galen Research, 1993. [11] Aaronson NK, Ahmedzai S, Bergman B, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality of life instrument for use in international clinical trials in oncology. J Natl Cancer Inst 1993;85:365– 76. [12] Bergman B, Aaronson NK, Ahmedzai S, Kaasa S, Sullivan M. The EORTC QLQ-LC13: a modular supplement to the EORTC Core Quality of Life Questionnaire (QLQC30) for use in lung cancer clinical trials. Eur J Cancer 1994;30A:635– 42. [13] Zubrod CG, Scheiderman MA, Frei E. Appraisal of methods for the study of chemotherapy in man: comparative therapeutic trial of nitrogen mustard and triethylene thiophosphoramide. J Chronic Dis 1960;11:7– 33.
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A. Montazeri et al. / Lung Cancer 31 (2001) 233–240 [21] Dancey J, Zee B, Osoba D, et al. Quality of life scores: an independent prognostic variable in a general population of cancer patients receiving chemotherapy. Qual Life Res 1997;6:151– 8. [22] Osoba D, Zee B, Sadura A. Measurement of quality of life in an adjuvant trial of gamma interferon versus levamisole in malignant melanoma. In: Salmon SE, editor. Adjuvant therapy of cancer VII. Philadelphia: Lippincott, 1993:412– 6. [23] Osoba D. Lessons learned from measuring health-related quality of life in oncology. J Clin Oncol 1994;12:608– 16. [24] Herndon JE, Fleishman S, Kornblith AB, Kosty M, Green MR, Holland J. Is quality of life predictive of the survival of patients with advanced non-small cell lung carcinoma? Cancer 1999;85:333– 40. [25] Bernhard J, Hurny C, Bacchi M, et al. Initial prognostic factors in small cell lung cancer predicting quality of life during chemotherapy. Br J Cancer 1996;74:1660– 7. [26] Tester WJ, Jin PY, Reardon DH, Cohen JB, Cohen MH. Phase II study of patients with metastatic non-small cell carcinoma of the lung treated with paclitaxel by 3-h infusion. Cancer 1997;79:724– 9.
[14] Swinscow TDV. Statistics at square one. London: British Medical Association, 1993. [15] Norusis MJ. SPSS advanced statistics 6.1. Chicago: SPSS, 1994. [16] Ruckdeschel JC, Piantadosi S. Quality of life in lung cancer surgical adjuvant trials. Chest 1994;106(suppl 6):324s– 8s. [17] Ganz PA, Lee JJ, Siau J. Quality of life assessment: an independent prognostic variable for survival in lung cancer. Cancer 1991;67:3131–5. [18] Buccheri GF, Ferrigno D, Tamburini M, Brunelli C. The patients’ perception of his own quality of life might have an adjunctive prognostic significance in lung cancer. Lung Cancer 1995;12:45– 58. [19] Gralla RJ, Hollen PJ, Eberley S, Cox C. Quality of life score predicts both response and survival in patients receiving chemotherapy for non-small cell lung cancer. Support Care Cancer 1995;3:378–9 (abstract). [20] Coates A, Gebski V, Signorini D, et al. Prognostic value of quality-of-life scores during chemotherapy for advanced breast cancer. J Clin Oncol 1992;10:1833–8.
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Quality of life in lung cancer patients As an important prognostic factor Ali Montazeri a,*, Robert Milroy b, David Hole c, James McEwen a, Charles R. Gillis c a
Department of Public Health, Uni6ersity of Glasgow, 1 Lilybank Gardens, Glasgow G12 8RZ, UK b Department of Respiratory Medicine, Stobhill Hospital, Glasgow G21 3UW, UK c West of Scotland Cancer Sur6eillance Unit, Department of Public Health, Uni6ersity of Glasgow, 2 Lilybank Gardens, Glasgow G12 8RZ, UK Received 10 February 2000; received in revised form 9 June 2000; accepted 15 June 2000
Abstract Given that lung cancer is one of the common cancers world-wide, the implications of focusing on quality of life as well as survival require to be understood. We have carried out a study of the relationship between survival and quality of life in patients with lung cancer comparing patients those who lived with those who died within 3 months. The design of the study allowed every patient in a defined geographical area with a potential diagnosis of lung cancer to be studied from first outpatient consultation till after a definitive treatment has been given. Quality of life was measured using three standard questionnaires: the Nottingham Health Profile (NHP), the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ– C30) and its lung cancer supplementary questionnaire (QLQ–LC13) in addition to a study specific questionnaire collecting data on demographic, social, clinical and performance status. The contribution of quality of life in relation to survival adjusted for known prognostic factors was determined using Cox’s proportional hazard model. In all 129 lung cancer patients were interviewed, and 96 patients were alive at 3-months follow-up. Only 90 of 96 patients alive at 3-months follow-up were assessable. Descriptive analyses showed that those who were dead had more perceived health problems, greater level of symptoms and significant lower physical and role functioning and global quality of life at presentation. On the other hand, univariate analyses showed that patients’ aggregate scores on the NHP, the functioning scores, and global quality of life scores alone were significant predictors of survival (PB 0.03, P B0.04, PB 0.04, respectively ). The multivariate analyses showed that pre-diagnosis global quality of life was the most significant predictor of the length of survival even after adjusting for known prognostic factors (age, P B 0.04; extent of disease, PB0.03; global quality of life, PB 0.02), while performance status, sex and weight loss were not. This study confirmed that pre-diagnosis quality of life was a significant predictor of survival. Indeed, pre-diagnosis quality of life should be considered as a clinical status which has to be established by physicians before treatment starts as it is such an important predictor of survival. © 2001 Elsevier Science Ireland Ltd. All rights reserved. * Corresponding author. Present address: Iranian Centre for Breast Cancer, PO Box 13185-1488, Tehran, Iran. Tel.: +98-216492431; fax: + 98-21-6418036. E-mail address: [email protected] (A. Montazeri). 0169-5002/01/$ - see front matter © 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S 0 1 6 9 - 5 0 0 2 ( 0 0 ) 0 0 1 7 9 - 3
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A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
Keywords: Lung cancer; Quality of life; Survival; EORTC QLQ– C30; EORTC QLQ– LC13
1. Introduction Lung cancer is well known as an important public health problem because of its high incidence [1], rapid progression [2], and poor outcome [3]. Thus, measuring quality of life has become an important endpoint of current clinical trials and other studies of lung cancer care. Despite much attention, uncertainty about choice of instruments, study design and analysis in measuring quality of life remains [4,5]. Our review of literature indicates that studies of quality of life have demonstrated harm from much of the treatment offered as well as benefit. Thus, quality of life should become one of the main outcome measures in clinical trials and treatment evaluations [6]. However, findings from more recent studies clearly suggest that effective systematic treatment in selected lung cancer patients can improve both survival and quality of life [7 – 9]. We have been fortunate in carrying out this study in area of high incidence of lung cancer in greater Glasgow where we had access to potential cases of lung cancer coming to outpatient clinics for diagnosis. This paper presents data from a prospective study of quality of life in lung cancer patients where quality of life was assessed pre-diagnosis drawn from a general population as opposed to the selected samples who are entered into clinical trials. The paper reports on the importance of quality of life as a prognostic factor of duration of survival.
2. Patients and methods
2.1. Study design A population-based study of quality of life in patients with lung cancer was conducted during 1 complete calendar year in the northern sector of Glasgow, Scotland. The population of the study area was 169 016 and the average annual number of incident of lung cancer was 150 cases. The
intention was to interview all new lung cancer patients attending the chest clinic of a large teaching and district general hospital (Stobhill NHS Trust). All 167 general practitioners (GPs) in the defined area were asked to give permission for their patients to take part in the study. An administrative officer unconnected with the study personnel obtained all GP requests for the chest clinics and gave a weekly list to A.M., thus blinding him to the potential diagnosis status of the patients. In addition to GP referrals other sources of identification of possible cases were considered. These included the bronchoscopy diary list, pathology results, internal referrals, and the in-patient list in the Respiratory Medicine ward and the Oncology Clinic.
2.2. Questionnaires Quality of life was assessed using three standard questionnaires: 1. The Nottingham Health Profile (NHP), a general health measure including profiles on energy, pain, emotional reactions, social isolation, and physical mobility [10]. 2. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ –C30), a core cancer-specific questionnaire containing 30 items on patients’ functioning, global quality of life, disease- and treatment-related symptoms [11]. 3. The EORTC Lung Cancer Questionnaire (EORTC QLQ –LC13), a site-specific questionnaire consisting of 13 items on lung cancer symptoms and its treatment related side-effects [12]. In addition to the above standard questionnaires the Eastern Co-operative Oncology Group performance scale (ECOG) was used to rate patients’ performance status. It is a five-grade observer rating of patients’ physical ability ranging from 0 (normal) to 4 (disabled) [13]. Finally, a short questionnaire was developed especially for this study to collect data on social
A. Montazeri et al. / Lung Cancer 31 (2001) 233–240
and demographic characteristics of the patients. Clinical information including weight loss, histology, extent of disease, and treatment modalities was extracted from case notes.
2.3. Statistical analysis Mann –Whitney test was used to compare the quality of life measures between those patients who were alive at 3-months follow-up (survivors) and those who were dead. This took into account the fact that the distribution of quality of life scores was skewed [14]. Cox’s regression analysis was performed to assess the relationship between patients’ pre-diagnosis quality of life and survival. The survival time for each patient was the time from the pre-diagnosis interview to the follow-up interview for those who were still alive (at least 3 months) or the date of death for those who had died. In univariate analysis each of the three pre-diagnosis aggregate scores (the NHP, functioning, and global quality of life as measured by the EORTC QLQ –C30) was used as independent variable. In multivariate analysis while adjusting for known prognostic factors (age, sex, performance status, extent of disease and weight loss), pre-diagnosis scores on the NHP, and functioning and global quality of life, Cox’s regression analysis was repeated by selecting the ‘forward conditional’ model. Based on this selection, variables are considered one at time for entry in the model [15].
3. Results Based on data obtained from the local cancer registry there were 133 lung cancer patients registered from the Stobhill Hospital catchment area during the study period. Of these, 129 (97%) patients were interviewed and the remaining four patients were missed.
3.1. Patients’ characteristics The characteristics of patients are shown in Table 1. A total of 77 patients (60%) were male, 52 (40%) were female, the mean age was 67.5
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years (S.D.= 9.1, range 40–87), and patients were mostly (57%) from severely deprived areas. Based on pathology records 27 (21%) patients were diagnosed as having small cell lung cancer, 67 (52%) non-small cell cancer, and the remaining 35 (27%) were clinically diagnosed lung cancer patients. The majority of patients had limited disease (n= 101, 78%). Of the patients, 70% (n = 89) had performance status 0 or 1. There were 81 patients (63%) who had active treatment (chemotherapy, radiotherapy, surgery), while the remaining 48 (37%) received ‘best supportive care’.
3.2. Sur6i6al At follow-up 96 patients were alive. Of these six patients were terminally ill. Thus, the analysis was restricted to 90 survivors and 33 deaths. The median survival time for patients who had died was 40 days (S.D.= 18.7).
3.3. General health Patients’ scores on the Nottingham Health Profile are shown in Table 2. Except for social isolation and sleep disturbance, all other measures were statistically significant confirming that those who were dead had more perceived health problems at presentation.
3.4. Functioning and global quality of life Similar results were found for patients’ functioning and global quality of life as measured by the EORTC QLQ –C30. On three measures (physical and role functioning and global quality of life) there were significant differences between survivors and deaths (Table 3).
3.5. Main symptoms The main symptoms of the disease as measured by the EORTC QLQ –C30 and QLQ –LC13 showed no significant difference between survivors and deaths except for fatigue indicating that both survivors and deaths presented with relatively similar symptoms. However, almost in all measures the deaths had a greater level of symptoms (Table 4).
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Table 1 Lung cancer patients socio-demographic and clinical characteristics at baseline and at 3 months later at follow-up stage (survivors and deaths) Pre-diagnosis sample (n=129) No. (%)
3 months post-diagnosisa (n =90) No. (%)
Deaths (n = 33) No. (%)
Gender Male Female
77 (60) 52 (40)
52 (58) 38 (42)
22 (67) 11 (33)
Age (years) Mean (S.D.)
67.5 (9.1)
66.8 (8.7)
70.0 (9.9)
Marital status Married Widowed/divorced Single
77 (60) 45 (35) 7 (5)
54 (60) 32 (36) 4 (4)
18 (55) 12 (36) 3 (9)
Social depri6ation category Affluent Middle Deprived
23 (18) 32 (25) 74 (57)
18 (20) 21 (23) 51 (57)
5 (15) 9 (27) 19 (58)
Cell type Small cell Non-small cell Unspecified
27 (21) 67 (52) 35 (27)
19 (21) 50 (56) 21 (23)
7 (21) 14 (43) 12 (36)
101 (78) 28 (22)
77 (86) 13 (14)
20 (64) 13 (36)
Extent of disease Limited Extensive Initial treatment Chemotherapy Radiotherapy Surgery Best supportive care
36 39 6 48
(28) (30) (5) (37)
27 31 6 26
(30) (34) (7) (29)
8 6 0 19
(24) (18) (00) (58)
Performance status (score) Normal activity (0) Symptoms (1) Sometimes in bed (2) Need to be in bed (3) Confined to bed (4)
29 60 25 15 0
(23) (47) (19) (11) (00)
23 46 14 7 0
(25) (51) (16) (8) (00)
4 12 10 7 0
(12) (36) (30) (21) (00)
Weight loss Significant weight lossb Possible weight lossc Weight steady No comment
51 12 40 26
(40) (9) (31) (20)
33 8 31 18
(37) (9) (34) (20)
15 4 7 7
(46) (12) (21) (21)
a At 3-months follow-up six patients were in the last stage of their lives and died soon afterwards. Thus, these were excluded from the analysis. b Significant weight loss was defined as significant body weight variation compared to the weight 6 months prior to diagnosis. c Although it was not clear whether a patient had a significant weight loss or not, the consultant commented in the case record that patient had possible weight loss.
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Table 2 Pre-diagnosis scores of lung cancer patients on the Nottingham Health Profile for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Physical mobility Energy Pain Emotional reactions Social isolation Sleep a
P
Survivors (n= 90), mean (S.E.M.)
Deaths (n= 33), mean (S.E.M.)
23.7 33.6 19.9 22.4 11.2 35.5
46.2 63.4 30.9 30.4 13.5 37.7
(2.7) (3.9) (2.9) (2.5) (2.3) (3.3)
(4.7) (7.7) (5.1) (3.9) (3.4) (5.3)
B0.01 B0.01 0.03 0.04 0.36 0.69
The higher values indicate more perceived health problems: min., 0; max., 100. S.E.M. = standard error of mean.
Table 3 Pre-diagnosis functioning and global quality of life scores of lung cancer patients on the EORTC QLQ-C30 for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Physical functioning Role functioning Social functioning Cognitive functioning Emotional functioning Global quality of life a
P
Survivors (n =90), mean (S.E.M.)
Deaths (n =33), mean (S.E.M.)
67.1 64.4 87.0 86.7 78.6 53.5
47.9 42.4 85.3 87.9 80.1 41.2
(2.6) (3.7) (2.2) (2.2) (2.3) (2.3)
(5.0) (6.2) (4.1) (2.7) (3.1) (4.3)
B0.01 B0.01 0.57 0.82 0.92 0.02
The higher values indicate a higher level of functioning and quality of life: min., 0; max., 100. S.E.M. = standard error of mean.
Table 4 Pre-diagnosis scores of main symptoms on the EORTC QLQ-C30 and QLQ-LC13 for survivors and patients who had died at 3 months post-diagnosisa Pre-diagnosis scores
Fatigue Appetite loss Pain (overall) Pain in chest Pain in shoulder Pain elsewhere Cough Dyspnoea Sleep difficulties Hemoptysis a
P
Survivors (n=90), mean (S.E.M.)
Deaths (n = 33), mean (S.E.M.)
32.8 30.4 23.3 19.2 25.9 20.0 43.4 36.3 30.0 8.1
46.5 42.4 36.4 21.9 31.3 36.4 48.5 41.9 25.3 9.6
(3.0) (3.5) (2.6) (5.0) (3.6) (3.1) (6.2) (2.8) (3.8) (3.8)
(5.1) (6.5) (6.3) (3.0) (6.3) (7.3) (3.4) (4.9) (6.2) (2.4)
The higher values indicate a greater degree of symptoms: min., 0; max., 100. S.E.M. =standard error of mean.
0.02 0.11 0.13 0.57 0.48 0.06 0.42 0.35 0.41 0.57
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3.6. Sur6i6al and quality of life To assess whether any of the quality of life scores made an independent contribution to length of survival, Cox’s regression analysis was carried out. Each of the three aggregate scores (pre-diagnosis: 1, NHP; 2, functioning; and 3, global quality of life scores) was divided into good and poor or high and low based on their relation to the mean. Based on separate analysis, patients’ pre-diagnosis scores on the NHP and functioning scores, and global quality of life were found to be significant predictors of survival (P B0.03, P B 0.04, PB 0.04, respectively ). In order to allow for adjustment of known prognostic factors, that is age, sex, performance status, weight loss, and extent of disease, and also pre-diagnosis general health status, functioning, and global quality of life scores, Cox’s regression analysis was repeated. The results indicated that the pre-diagnosis global quality of life as measured by the EORTC QLQ – C30 remained a major significant predictor of length of survival (age, P B0.04; extent of disease, P B0.03; global quality of life, PB0.02). As it is clear, global quality of life provided the best predictor of survival amongst the three quality of life measures (Table 5).
4. Discussion The findings of this study clearly suggest that the pre-diagnosis quality of life is an important
prognostic factor of the length of survival. This is one of the most striking findings in quality of life studies of survival in lung cancer patients. Ruckdeschel et al. [16] in a series of quality of life studies using the Functional Living IndexCancer (FLI-C) reported that the total baseline quality of life score (aggregate score on the FLIC) alongside performance status, weight loss and stage of disease were significant predictors of survival in 438 lung cancer patients. Similarly, Ganz et al. [17] using the same questionnaire but studying only 40 lung cancer patients, found that baseline quality of life was a significant predictor of subsequent survival. Buccheri et al. [18] in a study of 128 lung cancer patients using the Therapy Impact Questionnaire (TIQ) found that in addition to the stage of disease some aspect of quality of life such as difficulty at work and doing household jobs are prognostic factors of improved survival. Recently, Gralla et al. [19] in a study of 673 non-small cell lung cancer patients using the Lung Cancer Symptom Scale (LCSS) observed that the baseline quality of life not only predicts the survival, but also has greater impact than known prognostic factors. Similar findings have been reported for other cancers, for example breast cancer where patients’ physical well-being was found to be directly associated with survival [20]. In a large heterogeneous population of cancer patients the prognostic association between quality of life scores as measured by the EORTC QLQ –C30 and survival was assessed and the results indicated that presence of metastatic disease, diagnosis of lung or ovarian
Table 5 Prediction of survival by the Cox’s regression analysis Relative hazard ratio (HR)
95% CI for HR
P
Uni6ariate analysis NHP alone (poor: good) Functioning alone (low: high) Global quality of life alone (low: high)
3.0 2.1 2.2
1.7–6.2 1.1–4.3 1.1–4.4
B0.03 B0.04 B0.04
Multi6ariate analysis a Age (years) Extent of disease (extensive: limited) Global quality of life (low: high)
1.1 3.0 3.2
1–1.1 1.4–6.5 1.5–6.9
B0.04 B0.03 B0.02
a
Includes adjustment for every known prognostic factors, and the general health status and functioning scores.
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cancer, ECOG performance status, global quality of life and emotional functioning were significantly associated with survival. Global quality of life was predictive in all patients, in sub-groups of patients with metastatic disease, with breast and lung cancer and other tumour types [21]. In contrast, a study of patients with malignant melanoma [22] found that pre-treatment global quality of life scores, as measured by the EORTC QLQ –C30, were not predictive of survival. It was argued that these patients did not have advanced disease, as did the lung and breast cancer patients. Thus, it is possible to suggest that pre-treatment quality of life may not have predictive value in all patients with all cancers [23]. Some researchers argue that even for lung cancer patients overall quality of life is not a significant predictor of survival. In a recent publication they showed that after adjustment for significant clinical factors, a patient-provided pain report had the greatest prognostic importance [24]. However the major difference between the present study and those quoted is that we measured quality of life before diagnosis, while the others measured it after the diagnosis was established. Thus, the validity of our measurement of pre-diagnosis quality of life is less likely to be biased by any temporary effects due to patients being informed of their diagnosis of lung cancer and possible prognosis. Our finding that pre-diagnosis quality of life is a significant factor for survival outcome like other known prognostic factors has important implications. Pre-diagnosis assessment of quality of life could help physicians in their clinical decisions as it directly relates to patients’ survival time. Studies have shown that initial prognostic factors can predict quality of life in small cell lung cancer patients during chemotherapy even after controlling for response to treatment [25]. Although some observed changes in quality of life scores over time did not correlate well with objective response to the treatment in non-small cell lung cancer patients, baseline quality of life scores predicted those more likely to respond to treatment [26]. We, therefore argue that assessment of quality of life in lung cancer patients should be integrated into clinical practice and evaluated
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prospectively. The next step is to find how should clinicians introduce quality of life measures in practice and how should the outcome of any resulting treatment be adequately evaluated.
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