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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Thorac Oncol. Author manuscript; available in PMC Oct 14, 2010.
Published in final edited form as:
PMCID: PMC2954489
NIHMSID: NIHMS231606
Pretreatment Quality of Life Is an Independent Prognostic Factor for Overall Survival in Patients with Advanced Stage Non-small Cell Lung Cancer
Yingwei Qi, MD, MS,* Steven E. Schild, MD, Sumithra J. Mandrekar, PhD,* Angelina D. Tan, BS,* James E. Krook, MD, Kendrith M. Rowland, MD,§ Yolanda I. Garces, MD,* Gamini S. Soori, MD, Alex A. Adjei, MD, PhD, and Jeff A. Sloan, PhD*
*Mayo Clinic Rochester, Rochester, Minnesota
Mayo Clinic Arizona, Scottsdale, Arizona
Duluth Clinic CCOP, Duluth, Minnesota
§Carle Cancer Center CCOP, Urbana, Illinois
Missouri Valley Cancer Consoritum CCOP, Omaha, Nebraska
Roswell Park Cancer Institute, Buffalo, New York
Address for correspondence: Yingwei Qi, MD, MS, Mayo Clinic Department of Health Sciences Research, 200 1st Street SW, Rochester, MN 55905. qi.yingwei/at/mayo.edu
Hypothesis
We conducted this pooled analysis to assess the prognostic value of pretreatment Quality of Life (QOL) assessments on overall survival (OS) in advanced non-small cell lung cancer (NSCLC).
Methods
Four hundred twenty patients with advanced NSCLC (stages IIIB with pleural effusion and IV) from six North Central Cancer Treatment Group trials were included in this study. QOL assessments included the single-item Uniscale (355 patients), Lung Cancer Symptom Scale (217 patients), and Functional Assessment of Cancer Therapy-Lung (197 patients). QOL scores were transformed to a 0 to 100 scale with higher scores representing better status and categorized using the sample median or clinically deficient score (CDS, ≤50 versus >50). Cox proportional hazards models stratified by study were used to evaluate the prognostic importance of QOL on OS alone and in the presence of other prognostic factors such as performance status, age, gender, body mass index, and laboratory parameters.
Results
Pretreatment QOL accessed by Uniscale was significantly associated with OS univariately (p < 0.0001). Uniscale (p < 0.0001; hazard ratio = 1.6 for the sample median and 2.0 for the CDS categorization) and body mass index were the only significant predictors of OS multivariately. The median survival of patients who had a Uniscale score less than or equal to the CDS (≤50) was 5.7 versus 11.1 months for the >50 group; and 7.8 versus 13 months for the less than or equal to sample median (≤83) group and >83 group, respectively. The Lung Cancer Symptom Scale and the Functional Assessment of Cancer Therapy-Lung total scores were not significant predictors of OS.
Conclusions
Pretreatment QOL measured by Uniscale is a significant and an independent prognostic factor for OS, and QOL should be routinely integrated as a stratification factor in advanced NSCLC trials.
Keywords: Non-small cell, QOL, Survival
Lung cancer is the leading cause of cancer-related deaths among both men and women in the United States.1 Non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer cases. At diagnosis, approximately 39% of patients with NSCLC have advanced disease (stage IIIB with a positive pleural effusion and stage IV) and are generally considered to be incurable.2 Systemic therapies seem to add about 2 to 4 months to the median survival of advanced patients with NSCLC compared with best supportive care.3 Even so, the prognosis of patients is generally poor with median survival varying between 6 and 12 months and 1-year survival rates between 30 and 36%.4
Patient subgroups segregated by known prognostic factors also differ in median survival by a few months. Imbalances between treatment groups could easily influence survival as much as therapies and confound trial results. Thus, without proper stratification for these important prognostic factors, the results of clinical trials may be misinterpreted. Hence, identification of pretreatment prognostic factors is instrumental in that it could shed light on the interpretation and design of future clinical trials. This information could also be used to guide physicians to identify the best treatment for individual patients.
Southwest Oncology Group investigators reported the following independent prognostic factors for advanced NSCLC: performance score (PS), age, gender, and cisplatin-based therapy.5 A previous study performed by the North Central Cancer Treatment Group (NCCTG), which included 1053 patients revealed that patients who had high white blood cell counts, low hemoglobin (Hgb) levels, Eastern Cooperative Oncology Group PS >0, body mass index (BMI) <18.5 kg/m2, and stage IV disease had significantly worse survival than other patients.6 The International Association for the Study of Lung Cancer tumor staging project identified PS, age, and gender in addition to stage as important prognostic factors for survival in lung cancer.7 The prognostic effect of pretreatment patient self-reported quality of life (QOL) scores has been investigated in several malignancies, such as advanced colorectal, hepatic, esophageal, ovarian, and lung cancer, and has been shown to be a significant predictor of overall survival (OS).818 The prognostic value of pretreatment QOL on survival has been demonstrated in patients with advanced NSCLC.1216 The most commonly used tool, European Organization for Research and Treatment of Cancer Lung Cancer Group (EORTC) QLQ-C30, consists of five function scales, three symptom scales, six single-item scales, and a global health status/QOL scale (30 general cancer-related questions). The lung cancer-specific module QLQ-LC13 adds 13 lung cancer-related questions (thus 43 in total). Although a multi-item index has merits of accurateness and consistency, it imposes a fair amount of burden on patients, thereby limiting application in clinical trials research.19 Conflicting findings regarding the prognostic value of subscales and global QOL of EORTC QLQ-C30 in advanced NSCLC have also been reported.1214,20,21 Moreover, the issue of multicollinearity by examining subscales and global QOL at the same time could potentially impair the model stability. 13,22 Therefore, the single-item Uniscale has been an attractive and a widely used QOL tool in clinical studies because of its simplicity, good validity, and responsiveness to change over time.19
Based on the above discussion, this pooled analysis was performed to further explore the prognostic impact of pretreatment patient self-reported QOL scores on OS, with a focus on single-item Uniscale, in patients with advanced NSCLC. Specially, we attempted to answer the following questions: (1) Is pretreatment Uniscale prognostic for OS in patients with advanced NSCLC? (2) How is this prognostic association affected after adjusting for other known pretreatment factors? In addition, preliminary assessment of two multi-item QOL indices was performed, specifically the Lung Cancer Symptom Scale (LCSS) and the Functional Assessment of Cancer Therapy-Lung (FACT-L) that are lung cancer-specific instruments with acceptable psychometric properties.17
QOL Tools
The single-item Spitzer Uniscale is analogous to other linear analogue self-assessment items and is a measure of overall QOL.23 Patients were asked to mark an “X” in a horizontal rectangle. The distance of the X from the left end of the rectangle was measured and represented the patient self-reported QOL during the past week. The patient scale of LCSS consists of nine single items pertaining to lung cancer. A total score can be obtained by summing these nine items.24 The FACT-L has four general and one lung cancer-specific subscales. The general subscales include physical well-being, social/family well-being, emotional well-being, and functional well-being. Each subscale corresponds to a single summated score and a final total score can be derived by adding these single summated scores.25 All QOL tools were administered at baseline (i.e., after registration but before starting treatment).
Identification of Trials
Data were pooled from six phase II/III NCCTG chemotherapy trials for patients with advanced NSCLC (stage III with pleural effusion and stage IV) with the selection criteria that at least one of the three QOL instruments (Uniscale, LCSS and FACT-L) was collected at baseline. Except 972451 that was a phase III first-line maintenance therapy trial, others were all first-line trials. For the phase III trial, 972451, data from both the treatment and the placebo arms were included, as there were no OS differences reported between the two arms.26 Two of the trials enrolled only elderly patients (≥65 years, trials N0022 and N0222). See Table 1 for more detailed information on the individual trial characteristics.
TABLE 1
TABLE 1
Description of NCCTG Trials Included in the Pooled Analysis
Statistical Analysis
Uniscale scores were obtained by measuring the distance of an X in a box from the left end of the scale and then transformed to a 0 to 100 scale with higher percentages representing better status. Uniscale scores were dichotomized using either the sample median (≤ median versus > median) or the clinically deficient score (CDS, ≤50 versus >50). The scoring cut-off of CDS has been validated by Temel et al.27 and Butt et al.28 The summated total scores of LCSS and FACT-L were transformed and dichotomized in a similar manner.
OS was defined as the time from registration to death because of any cause. Patients with follow-up beyond 5 years were censored at 5 years. The distribution of survival times was estimated using the method of Kaplan-Meier.29 Univariate and multivariate Cox proportional hazards (PH) models were used to evaluate the prognostic importance on OS of all baseline factors that were available in these trials and were previously reported to be of prognostic importance in advanced NSCLC, including age, gender, baseline PS, BMI, Hgb, platelet count (PLT), and absolute neutrophil count (ANC). All models were stratified by trial to account for possible heterogeneity between trials.
Previously known prognostic factors for OS in advanced NSCLC (PS, age, and gender) were included in all multivariate models regardless of their significance in univariate models. Factors other than these known prognostic factors were included in multivariate models if the p value was <0.2 in the univariate model. These included BMI, Hgb, PLT, and ANC that were collected across all trials at baseline. Eastern Cooperative Oncology Group (ECOG) PS was included in the models with two levels (0 versus 1–2) for the purpose of model stability as PS 2 patients accounted for only 6.5% (23) of the total patients. BMI (in kg/m2) was classified into four categories by the conventional criteria: underweight (BMI <18.5), normal (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30).30 Gender-based cut-offs for Hgb and PLT were used based on previously published criteria.6 Anemia (low Hgb) was defined as Hgb <13.2 g/dL for men and <11.5 g/dL for women. High PLT was defined as a PLT count >355 × 109/L for men and >375 × 109/L for women. ANC was dichotomized using the sample median for men and women. All analyses were carried out using SAS V9.1, and S-Plus V8.0.1. Given the two different dichotomizations (CDS and sample median) for each QOL assessment, p values <0.025 in the final multivariate model for each QOL assessment were deemed statistically significant.
The power to detect the effect of a pretreatment factor on OS depends on the prevalence and number of levels of the factor. In general, when Uniscale scores were categorized using CDS, a sample size of 355 patients for the Uniscale analysis provides at least 90% power to detect an effect assessed by a hazard ratio (HR) of 1.6 for this two-level factor with a prevalence of 15 versus 85% (two-sided log-rank test, α level = 0.05) using the actual accrual rates for the pooled data and assuming an exponential distribution for survival with a minimum of 2 years follow-up on each patient.
Martingale residual analyses31 were conducted in the univariate setting to assess the appropriateness of functional forms of QOL scores used in the Cox PH models. Because data were pooled from multiple individual trials, all factors were tested for between-trial heterogeneity by examining the interaction between trial and each factor. The appropriateness of the PH assumption was tested by examining the Schoenfeld residuals32 and a stratified Cox PH model was used as appropriate.
Data were frozen for this analysis on December 10, 2007. Of the 420 eligible patients with advanced NSCLC, 355 patients completed the Uniscale assessment, 217 patients completed LCSS, and 197 patients completed FACT-L at baseline. Data are complete with 97% of the patients followed up until death. All surviving patients have a minimum follow-up of 5 years post study entry. All results presented are specific to Uniscale analysis, unless otherwise noted.
Baseline Patient Characteristics—Uniscale
Table 2 gives a detailed description of patient characteristics. The median Uniscale score at baseline was 83, with 53 patients (15%) below the CDS (≤50). The median age was 66 years (range, 33–87 years), 59% of patients were men, 38% of them had PS of 0, and 47% were anemic at baseline. Five percent of patients were deemed underweight at baseline, 37% overweight, and 23% obese. The median ANC was 5200/mm3 for men and 4410/mm3 for women (range, 1500–92,700/mm3).
TABLE 2
TABLE 2
Baseline Characteristics of the Patient Included in the Uniscale Analysis (n = 355)
OS—Uniscale
The median survival of patients who had a Uniscale score below the CDS (≤50) was 5.7 months (95% CI 4.7– 8.9 months) versus 11.1 months (95% CI 9.6 –12.5 months) for the >50 group. The median survival of patients with a Uniscale score less than or equal to sample median (≤83) was 7.8 months (95% CI 6.0 –9.3 months) versus 13 months (95% CI 10.6 –15.2 months) for the >83 group. Figure 1 shows the Kaplan-Meier OS curves stratified by baseline Uniscale sample median and CDS.
FIGURE 1
FIGURE 1
Kaplan-Meier overall survival curves. A, Subgroups split by Uniscale sample median (83); (B) subgroups split by Uniscale clinically deficient score (50). OS, overall survival.
Univariate and Multivariate Models—Uniscale
In the univariate setting, pretreatment Uniscale score was a significant prognostic factor of OS using both the sample median and the CDS categorizations. Patients with a low Uniscale score had a significantly worse OS (p < 0.0001, HR = 1.6 for the sample median categorization with a 95% CI 1.3–2.0; p < 0.0001, HR = 2.1 with a 95% CI 1.5–2.9 for the CDS categorization). In addition, patients with high PS scores, male gender, low Hgb levels (anemia), and high ANC values had significantly worse survival. Table 3 summarizes the univariate Cox PH model results.
TABLE 3
TABLE 3
Univariate Cox PH Models of Uniscale Scores
In the multivariate analysis, Uniscale and BMI were the only significant predictors of survival. Patients with low-baseline Uniscale values had a significantly worse OS (p < 0.0001, HR = 1.6 with a 95% CI 1.3–2.1 for the sample median categorization; p < 0.0001, HR = 2.0 with a 95% CI 1.5–2.8 for the CDS categorization), and patients who were underweight had significantly worse OS compared with normal BMI patients (p = 0.03, HR = 1.9 with a 95% CI 1.1–3.3 in the sample median categorization model). Further analyses showed that there was no significant interaction between baseline Uniscale and BMI. See Table 4 for multivariate Cox PH model results.
TABLE 4
TABLE 4
Multivariate Cox PH Models of Uniscale Scores
Model Assumptions and Diagnostics—Uniscale
The Martingale residual analyses demonstrated a linear relationship between baseline Uniscale scores and OS, and that the cut-offs chosen for categorization (CDS and median) were justified. All factors except Hgb level satisfied the homogeneity assumption. On further examination, it was deemed that the observed heterogeneity was only quantitative (HRs of anemia versus nonanemia were in the same direction but with varying magnitude across trial), and hence Hgb level was included in all models.
PS and BMI (underweight) did not satisfy the PH assumption in the univariate settings. However, inspection of the scaled Schoenfeld residual plots (versus time) indicated that the deviations for BMI (underweight) were minor, but the deviations for PS needed further investigation. Therefore, stratified multivariate Cox PH models with PS as a second stratification factor (in addition to trial) were run. The magnitude and significance of the HRs remained similar, except for gender with male patients having a significantly worse outcome compared with female patients (p = 0.006 for the CDS categorization and 0.002 for the median categorization).
Multi-Item QOL Indices
Of 217 patients included in the LCSS analysis, 201 patients (92.6%) died (median survival, 7.9 months). Median LCSS total score was 79.3 (range, 35.3–100), with 9.2% of patients having an LCSS total score below the CDS. The pretreatment LCSS total score was not associated with OS univariately (p = 0.06, HR = 1.4 for the sample median categorization) and thus was not explored further in a multivariate model.
Of 197 patients included in the FACT-L analysis, 189 patients (96.0%) died (median survival, 10.6 months). Median FACT-L total score was 77.2 (range, 28.0 –95.8), with 4.1% of patients having an FACT-L total score below the CDS. Pretreatment FACT-L total score was not associated with OS univariately (p = 0.09, HR = 1.3 for the sample median categorization) and thus was not explored further in a multivariate model.
Advantages of a pooled analysis is that it makes use of data from multiple clinical trials and allows one to assess more generalized and consistent relationships across trials rather than individual trials. For the Uniscale analysis, the large patient sample (n = 355) consisted of a homogeneous population of advanced NSCLC from five prospective NCCTG clinical trials with sufficient follow-up allowing for an adequately powered analysis.
The main finding of the present analysis is that pretreatment patient self-reported QOL, measured by Uniscale, is an independent prognostic factor for OS and this prognostic association remained significant in the presence of other factors including PS, age, gender, BMI, Hgb, PLT, and ANC in patients with advanced NSCLC. Univariately, the hazard of death of patients with Uniscale lower than the CDS (≤50) is twice as high as that of patients with Uniscale >50. After adjusting for age, gender, PS, BMI, Hgb, PLT, and ANC, the strength of this association remained similar. This study showed no significant prognostic association between the total scores of multi-item indices (LCSS and FACT-L) and OS in patients with advanced NSCLC.
There are some limitations to this analysis. First, the phase III first-line maintenance therapy trial, 972451, contributed 47% of patients included in the Uniscale analysis. The median survivals are different between first-line and first-line maintenance treatment, thus the strength of the prognostic impact of baseline Uniscale might need further elaboration. Second, two trials enrolling elderly patients (i.e., age ≥65 years) were included. However, stratifying by trial and including age as a continuous covariate in the multivariate models accounted for some, if not all, of these heterogeneity issues. Third, the retrospective nature of this analysis made the exploration of all factors that might have a potential impact on patient survival not possible as they were not collected. For example, comorbidity score, which captures pre-existing comorbidity conditions and has been indicated to have a significant impact on survival,33 could not be explored in this analysis as data on comorbidity score was not collected on any of the trials. Finally, the sample sizes for the LCSS and FACT-L analyses were limited, thus compromising the ability to do a detailed exploration of these assessments on OS. In addition, only summated total scores, not subscales, of these two tools were explored in this analysis.
This study confirmed the prognostic importance of pretreatment QOL on OS. Although research on prognostic factors of QOL in patients with advanced NSCLC has provided conflicting results regarding different QOL tools and different subscales within one instrument, the basic idea that pretreatment QOL is an important predictor for survival is consistent across most studies. EORTC QLQ-C30 is the most commonly used QOL instrument in the research of QOLs prognostic value on survival in advanced NSCLC. Several studies have demonstrated that the global QOL score assessed with QLQ-C30 is an independent prognostic factor for survival in advanced NSCLC.12,14,15,20 Recently, in a cohort of 391 patients, Efficace et al.13 found that patient self-reported pain and dysphagia measured by QLQ-C30, in addition to gender and PS, were independent significant predictors of survival. An earlier study conducted by Herndon et al.21 had similar finding in that pain, not global QOL, was prognostic for survival. The inconsistency of evidence described above in terms of the prognostic impact of global QOL using EORTC QLQ-C30 could reflect possible multicollinearity that contributed to model instability when global QOL was included in the final multivariate model with other subscales. 13,22 The conflicting results could also be due to different cut-offs used to categorize factors, different selection of factors other than QOL, and even analysis methodology preferences.
The multi-item indices explored in this analysis, LCSS and FACT-L, are not significantly associated with survival outcome in patients with advanced NSCLC in the univariate setting. The limited research of prognostic impact of QOL on survival using LCSS and FACT-L suggests that the total summated QOL scores or some subscales assessed with these tools may independently predict survival in patients with advanced NSCLC.3335 Changes of LCSS scores from baseline were shown to be associated with efficacy outcomes in a recently reported study.36 The present analysis only explored the total summated scores of LCSS and FACT-L, and further research is needed for the subscales in each tool which might show different features. In addition, because LCSS does not contain many of the important components of the QOL, it may be a limited measurement of QOL.17
One obstacle for routine integration of QOL in clinical trials is due to the increased patient burden. Compared with the multi-item QOL indices, single-item Uniscale is simple and easy to implement and has the inherent advantage of minimal patient burden, especially for those seriously ill patients. However, single-item indices do demonstrate greater variability in the overall range of scores than the multiple-item indices.19,37 Despite this limitation, Uniscale has been shown to be a valid, reliable, and sensitive tool to assess overall QOL and may measure something “broader” than symptom-specific multi-item indices.38 Huschka et al.39 observed that Uniscale actually detects a clinical significant decline better over time in an NCCTG pooled analysis. These merits make Uniscale an attractive stratification factor in advanced NSCLC clinical trials.
The present analysis has confirmed the prognostic impact of previously identified prognostic factors on OS in the univariate settings. Our findings of female gender and good PS as favorable prognostic factors univariately are consistent with previous studies.5,7,13,40 BMI (underweight) was identified to be significantly prognostic for worse survival in the present analysis multivariately. This confirmed the findings of Mandrekar et al.6 in a pooled analysis of NCCTG trials (underweight versus normal weight, HR of OS = 1.77, 95% CI 1.30 –2.40).
In the presence of baseline QOL assessed by Uniscale, the prognostic effect of PS, Hgb, PLT, and ANC were not prominent. The inconsistency of the gender effect on OS between the non-PS-stratified and the PS-stratified multivariate models suggests that the prognostic impact of gender on OS may vary across different levels of PS. Conflicting data have been reported regarding whether PS and gender are significant predictors of survival in the presence of QOL. Langendijk et al.12 observed that PS became nonsignificant in the presence of QOL. Montazeri et al.20 found that both PS and gender’s effect became nonsignificant in the presence of QOL, whereas Efficace et al.13 reported that PS, gender, and QOL were all retained in the final multivariate model. PS is primarily a measure of patient ambulatory ability per se, and thus has limited scope in measuring patient overall well-being compared with Uniscale. Further analysis of our data indicated that baseline PS and Uniscale were correlated (χ2 test, p < 0.001). Together with the finding that the effect of PS became nonsignificant in the multivariate model, QOL assessed by single-item Uniscale might cover the scope of PS in prediction for OS. Of note, however, is that PS 1 and PS 2 were grouped together in this analysis because of a small proportion of PS 2 patients. This might have masked the well-accepted independent negative association between PS 2 and OS.7
The precise prognostic value of baseline Hgb levels on OS remains unclear in advanced NSCLC. There are studies demonstrating baseline Hgb levels as independent prognostic factor for superior survival.5,6,33,41 The Radiation Therapy Oncology Group study’s finding was consistent with ours that Hgb level lost its significance when QOL was added to the model.16 However, the Radiation Therapy Oncology Group study was based on patients with locally advanced NSCLC receiving radiotherapy. Our study also identified ANC as a significant predictor for OS univariately. This may signal some underlying issues, for example, a greater tumor burden (e.g., increasing neutrophils via antibody-dependent cell-mediated cytotoxicity), antichemotactic activities of some mediators produced by the tumor cells. Mandrekar et al. and Sculier et al. reported similar results that white blood cell count was associated with a poor survival outcome.6,7 Similarly, high pretreatment PLT count showed a trend toward worse OS univariately, although this effect was not statistically significant. Thrombocytosis has been recognized as a paraneoplastic symptom and has been shown to predict for shorter survival in lung cancer.33,42 In general, however, there has not been a consistent effort in investigating the prognostic values of complete blood count in advanced NSCLC.
In conclusion, the present analysis demonstrated that pretreatment patient QOL measured by Uniscale is a significant prognostic factor for OS in advanced NSCLC independent of other known factors such as PS, age, gender, BMI, and some laboratory parameters. Based on the current work and a few others published in the literature, it can be concluded that pretreatment patient self-reported QOL is an independent and an important prognostic factor in advanced NSCLC. Pretreatment QOL can prospectively identify patient subgroups with survival more divergent than the survival advantages associated with currently available therapies. This highlights the need to routinely integrate QOL in advanced NSCLC clinical trials either by including it as a stratification factor or by appropriately adjusting for it in the analysis for a proper interpretation of data from trials. Our results also have a broader scope in that a simple, quick, and convenient QOL assessment, Uniscale, can provide clinically meaningful information regarding patient survival.
ACKNOWLEDGEMENT
Supported in part by Public Health Service grants CA-25224, CA-37404, CA-63849, CA-35113, CA-35103, CA-37417, CA-35269, CA-35448, CA-35101, CA-35272, CA-35415, CA-35101, and CA-52352.
This study was conducted as a collaborative trial of the North Central Cancer Treatment Group (NCCTG) and Mayo Clinic.
Additional participating institutions include the following: MedCenter One Health Systems, Bismarck, ND 58506 (Edward J. Wos, DO); Meritcare Hospital CCOP, Fargo, ND 58122 (Preston D. Steen, MD); Duluth CCOP, Duluth, MN 55805 (Daniel A. Nikcevich, MD); Geisinger Clinic and Medical Center CCOP, Danville, PA 17822 (Albert M. Bernath, MD); Iowa Oncology Research Association CCOP, Des Moines, IA 50309-1014 (Roscoe F. Morton, MD); Ochsner CCOP, New Orleans, LA 70121 (Carl G. Kardinal, MD); Toledo Community Hospital Oncology Program CCOP, Toledo, OH 43623 (Paul L. Schaefer, MD); Sioux Community Cancer Consortium, Sioux Falls, SD 57105 (Loren K. Tschetter, MD); Rapid City, SD 59709 (Larry P. Ebbert, MD); Altru Health Systems, Grand Forks, ND 58201 (Tudor Dentchev, MD); CentraCare Clinic, St. Cloud, MN 56301 (Harold E. Windschitl, MD).
Footnotes
The authors declare no potential conflict of interest.
1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2008. CA Cancer J Clin. 2008;58:71–96. [PubMed]
2. NCI. Cancer Statistics Review 1975–2002
3. Non-small Cell Lung Cancer Collaborative Group. Chemotherapy in non-small cell lung cancer: a meta-analysis using updated data on individual patients from 52 randomised clinical trials. BMJ. 1995;311:899–909. [PMC free article] [PubMed]
4. Schiller JH, Harrington D, Belani CP, et al. Comparison of four chemotherapy regimens for advanced non-small-cell lung cancer. N Engl J Med. 2002;346:92–98. [PubMed]
5. Albain KS, Crowley JJ, LeBlanc M, Livingston RB. Survival determinants in extensive-stage non-small-cell lung cancer: the Southwest Oncology Group experience. J Clin Oncol. 1991;9:1618–1626. [PubMed]
6. Mandrekar SJ, Schild SE, Hillman SL, et al. A prognostic model for advanced stage nonsmall cell lung cancer. Pooled analysis of North Central Cancer Treatment Group trials. Cancer. 2006;107:781–792. [PubMed]
7. Sculier JP, Chansky K, Crowley JJ, Van Meerbeeck J, Goldstraw P. International Staging Committee and Participating Institutions. The impact of additional prognostic factors on survival and their relationship with the anatomical extent of disease expressed by the 6th Edition of the TNM Classification of Malignant Tumors and the proposals for the 7th Edition. J Thorac Oncol. 2008;3:457–466. [PubMed]
8. Grothey A, Sargent DJ, Szydlo DW, Zhao X, Campbell M, Goldberg RM, Sloan JA. Baseline quality of life (QOL) is a strong and performance status (PS)-independent prognostic factor for overall survival (OS) in patients with metastatic colorectal cancer (mCRC) ASCO 2008 Gastrointestinal Cancers Symposium. Abstract No. 334.
9. Park SH, Cho MS, Kim YS, et al. Self-reported health-related quality of life predicts survival for patients with advanced gastric cancer treated with first-line chemotherapy. Qual Life Res. 2008;17:207–214. [PubMed]
10. Yeo W, Mo FK, Koh J, et al. Quality of life is predictive of survival in patients with unresectable hepatocellular carcinoma. Ann Oncol. 2006;17:1083–1089. [PubMed]
11. Carey MS, Bacon M, Tu D, Butler L, Bezjak A, Stuart GC. The prognostic effects of performance status and quality of life scores on progression-free survival and overall survival in advanced ovarian cancer. Gynecol Oncol. 2008;108:100–105. [PubMed]
12. Langendijk H, Aaronson NK, de Jong JM, ten Velde GP, Muller MJ, Wouters M. The prognostic impact of quality of life assessed with the EORTC QLQ-C30 in inoperable non-small cell lung carcinoma treated with radiotherapy. Radiother Oncol. 2000;55:19–25. [PubMed]
13. Efficace F, Bottomley A, Smit EF, et al. The EORTC Lung Cancer Group and Quality of Life Unit. 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. Ann Oncol. 2006;17:1698–1704. [PubMed]
14. Maione P, Perrone F, Gallo C, et al. Pretreatment quality of life and functional status assessment significantly predict survival of elderly patients with advanced non-small-cell lung cancer receiving chemotherapy: a prognostic analysis of the multicenter Italian lung cancer in the elderly study. J Clin Oncol. 2005;23:6865–6872. [PubMed]
15. Brown J, Thorpe H, Napp V, et al. Assessment of quality of life in the supportive care setting of the big lung trial in non-small-cell lung cancer. J Clin Oncol. 2005;23:7417–7427. [PubMed]
16. Nicolaou N, Moughan J, Sarna L, et al. Quality of Life (QOL) Super-cedes the Classic Predictors of Survival in Locally Advanced Non-Small Cell Lung Cancer (NSCLC): an analysis of Radiation Therapy Oncology Group (RTOG) 9801. Int J Radiat Oncol Biol Phys. 2007;69:S58–S59.
17. Montazeri A, Gillis CR, McEwen J. Quality of life in patients with lung cancer: a review of literature from 1970 to 1995. Chest. 1998;113:467–481. [PubMed]
18. Gotay CC, Kawamoto CT, Bottomley A, Efficace F. The prognostic significance of patient-reported outcomes in cancer clinical trials. J Clin Oncol. 2008;26:1355–1363. [PubMed]
19. Sloan JA, Aaronson N, Cappelleri JC, Fairclough DL, Varricchio C. Clinical Significance Consensus Meeting Group. Assessing the clinical significance of single items relative to summated scores. Mayo Clin Proc. 2002;77:479–487. [PubMed]
20. Montazeri A, Milroy R, Hole D, et al. Quality of life in lung cancer patients: as an important prognostic factor. Lung Cancer. 2001;31:233–240. [PubMed]
21. Herndon JE, II, Fleishman S, Kornblith AB, Kosty M, Green MR, Holland J. Is quality of life predictive of the survival of patients with advanced nonsmall cell lung carcinoma? Cancer. 1999;85:333–340. [PubMed]
22. Van Steen K, Curran D, Kramer J, et al. Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection. Stat Med. 2002;21:3865–3884. [PubMed]
23. Spitzer WO, Dobson AJ, Hall J, et al. Measuring the quality of life of cancer patients: a concise QL-index for use by physicians. J Chronic Dis. 1981;34:585–597. [PubMed]
24. Hollen PJ, Gralla RJ, Kris MG, Potanovich LM. Quality of life assessment in individuals with lung cancer: testing the Lung Cancer Symptom Scale (LCSS) Eur J Cancer. 1993;29A Suppl 1:S51–S58. [PubMed]
25. Cella DF, Bonomi AE, Lloyd SR, Tulsky DS, Kaplan E, Bonomi P. Reliability and validity of the Functional Assessment of Cancer Therapy-Lung (FACT-L) quality of life instrument. Lung Cancer. 1995;12:199–220. [PubMed]
26. Johnson EA, Marks RS, Mandrekar SJ, et al. Phase III randomized, double-blind study of maintenance CAI or placebo in patients with advanced non-small cell lung cancer (NSCLC) after completion of initial therapy (NCCTG 97-24-51) Lung Cancer. 2008;60:200–207. [PubMed]
27. Temel JS, Pirl WF, Recklitis CJ, Cashavelly B, Lynch TJ. Feasibility and validity of a one-item fatigue screen in a thoracic oncology clinic. J Thorac Oncol. 2006;1:454–459. [PubMed]
28. Butt Z, Wagner LI, Beaumont JL, et al. Use of a single-item screening tool to detect clinically significant fatigue, pain, distress, and anorexia in ambulatory cancer practice. J Pain Symptom Manage. 2008;35:20–30. [PubMed]
29. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457–481.
30. BMI categorization. Center for Disease Control and Prevention. [Accessed on February 2, 2008]. Available at: http://www.cdc.gov/nccdphp/dnpa/healthyweight/assessing/bmi/adult_BMI/about_adult_BMI.htm.
31. Grambsch PM, Therneau TM, Fleming TR. Diagnostic plots to reveal functional form for covariates in multiplicative intensity models. Biometrics. 1995;51:1469–1482. [PubMed]
32. Grambsh PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81:515–526.
33. Jacot W, Colinet B, Bertrand D, et al. Oncolr Health Network. Quality of life and comorbidity score as prognostic determinants in non-small-cell lung cancer patients. Ann Oncol. 2008;19:1458–1464. [PubMed]
34. Gralla R, Hollen P, Eberley S. Quality of life score predicts both response and survival in patients receiving chemotherapy for non-small cell lung cancer [abstract] Support Care Cancer. 1995;3:378–379.
35. Eton DT, Fairclough DL, Cella D, Yount SE, Bonomi P, Johnson DH. Eastern Cooperative Oncology Group. Early change in patient-reported health during lung cancer chemotherapy predicts clinical outcomes beyond those predicted by baseline report: results from Eastern Cooperative Oncology Group Study 5592. J Clin Oncol. 2003;21:1536–1543. [PubMed]
36. de Marinis F, Pereira JR, Fossella F, et al. Lung Cancer Symptom Scale outcomes in relation to standard efficacy measures: an analysis of the phase III study of pemetrexed versus docetaxel in advanced non-small cell lung cancer. J Thorac Oncol. 2008;3:30–36. [PubMed]
37. Sloan JA, Dueck A. Issues for statisticians in conducting analyses and translating results for quality of life end points in clinical trials. J Biopharm Stat. 2004;14:73–96. [PubMed]
38. Sloan JA, Loprinzi CL, Kuross SA, et al. Randomized comparison of four tools measuring overall quality of life in patients with advanced cancer. J Clin Oncol. 1998;16:3662–3673. [PubMed]
39. Huschka MM, Mandrekar SJ, Schaefer PL, Jett JR, Sloan JA. A pooled analysis of quality of life measures and adverse events data in north central cancer treatment group lung cancer clinical trials. Cancer. 2007;109:787–795. [PubMed]
40. Smit EF, van Meerbeeck JP, Lianes P, et al. European Organization for Research and Treatment of Cancer Lung Cancer Group. Three-arm randomized study of two cisplatin-based regimens and paclitaxel plus gemcitabine in advanced non-small-cell lung cancer: a phase III trial of the European Organization for Research and Treatment of Cancer Lung Cancer Group—EORTC 08975. J Clin Oncol. 2003;21:3909–3917. [PubMed]
41. Caro JJ, Salas M, Ward A, Goss G. Anemia as an independent prognostic factor for survival in patients with cancer: a systemic, quantitative review. Cancer. 2001;91:2214–2221. [PubMed]
42. Aoe K, Hiraki A, Ueoka H, et al. Thrombocytosis as a useful prognostic indicator in patients with lung cancer. Respiration. 2004;71:170–173. [PubMed]
43. Colon-Otero G, Niedringhaus RD, Hillman SH, et al. North Central Cancer Treatment Group. A phase II trial of edatrexate, vinblastine, adriamycin, cisplastin, and filgrastim (EVAC/G-CSF) in patients with non-small-cell carcinoma of the lungs: a North Central Cancer Treatment Group Trial. Am J Clin Oncol. 2001;24:551–555. [PubMed]
44. Marks RS, Graham DL, Sloan JA, et al. A phase II study of the dolastatin 15 analogue LU 103793 in the treatment of advanced non-small-cell lung cancer. Am J Clin Oncol. 2003;26:336–337. [PubMed]
45. Kanard A, Jatoi A, Castillo R, et al. Oral vinorelbine for the treatment of metastatic non-small cell lung cancer in elderly patients: a phase II trial of efficacy and toxicity. Lung Cancer. 2004;43:345–353. [PubMed]
46. Jatoi A, Hillman S, Stella P, et al. Why do oncologists prescribe—or not prescribe—conventional chemotherapy to geriatric patients with metastatic non-small cell lung cancer? An exploratory study from the North Central Cancer Treatment Group. Submitted for publication. [PubMed]