Medical Manuscript
Survival Prediction in Ambulatory Patients With Stage III/IV Non-Small Cell Lung Cancer Using the Palliative Performance Scale, ECOG, and Lung Cancer Symptom Scale
American Journal of Hospice & Palliative Medicine® 1-7 ª The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1049909115570707 ajhpm.sagepub.com
Sean O’Mahony, MB, BCH, BAO1, Susan Nathan, MD1, Roozbeh Mohajer, MD2, Philip Bonomi, MD1, Marta Batus, MD1, Mary Jo Fidler, MD1, Kalani Wells, MS1, Naomi Kern, MS3, Shannon Sims, MD/PHD1, and Darpan Amin, BS1
Abstract Objectives: Patients with advanced non-small cell lung cancer (NSCLC) have a life expectancy of less than 1 year. Therefore, it is important to maximize their quality of life and find a tool that can more accurately predict survival. Materials: The Palliative Performance Scale (PPS) is used to predict survival for patients with advanced disease based on functional dimensions. The value of the PPS in ambulatory patients with cancer has not been examined to date. The Lung Cancer Symptom Scale (LCSS) measures six major symptoms and their effect on symptomatic distress and activity. We evaluated 62 patients with stage III or IV NSCLC and Eastern Cooperative Oncology Group (ECOG) Scale Score 1 at baseline in a thoracic oncology clinic. In all, 62 patients had LCSS and PPS evaluated at baseline and 54 patients had 4-week follow-up using LCSS, PPS, and ECOG. Results: Fifty-four patients completed baseline and follow-up. Mean age was 63.7 years. Sixty-three percent were receiving chemotherapy at evaluation. Seventeen patients died. Mean baseline measures were LCSS 6.18 (1-14); PPS 66.6 (40-90); and ECOG 1.82 (1-4). Censored survival times were calculated from enrollment of the first patient for 380 days. A proportional hazardous model was computed for survival status. Hazard ratios for death were 1.25 (P ¼ .013) for LCSS, 2.12 (P ¼ .027) for ECOG, and 1.02 for PPS (P ¼ .49). Conclusions: The LCSS predicted prognosis best in this study. The PPS did not accurately predict prognosis in our patient population. Keywords prognostication, lung cancer, ECOG, palliative performance scale, lung cancer symptom scale, palliative care
Introduction Non-small cell lung cancer (NSCLC) accounts for 87% of all lung cancers and the majority of patients present with advanced, incurable disease.1 Although there have been marked improvements in survival for patients presenting with advanced NSCLC in the last 15 years in response to the development of new cytotoxic agents, median survival times are still low at about 1 year. Physicians, including hospice physicians and oncologists, consistently overestimate patient survival by about 45%. Because of this, it is essential to maximize the quality of life (QOL) for these patients and find tools that accurately predict their prognosis and assess functionality and symptom burden.2 Both the Karnofsky Performance Rating Scale (KPRS) and Eastern Cooperative Oncology Group (ECOG) performance status measure have been shown to predict survival in patients
with advanced cancer. However, they only moderately correlate with clinical estimates of survival.3 Several studies have found an association between reduced KPRS scores and reduced survival: a KPRS score 60% and steep drop-offs in scores 20% at follow-up.13-15 Lung Cancer Symptom Scale. The LCSS is a lung cancer-specific measure of QOL, particularly for use in clinical trials. It evaluates six common symptoms associated with lung cancer (loss of appetite, fatigue, cough, dyspnea, hemoptysis, and pain) and their effect on overall distress, functional activities, and global QOL. It provides a practical measure of QOL that reduces patient and staff burden in serial measurement of QOL during the course of clinical trials. Initially, it is administered by faceto-face interview for demonstration of visual analogue scales (VAS) with a simple example question related to the weather, with telephone interview acceptable once patient is familiar with the VAS. It takes 8 minutes for initial demonstration of the VAS and 3 to 5 minutes for subsequent administrations.
Figure 1. Outline of the recruitment process.
baseline. Patients were asked to indicate their ethnicity/race, education, and employment status. Cognitive screening was conducted by the Palliative Medicine fellows or NP students. The recruitment process is outlined in Figure 1. Measures. In order to limit patient burden and maximize completion of the assessment, all self-report scales were read to study patients who might have difficulty completing the selfreport measures due to fatigue, illness, or difficulty reading due to visual difficulty. It was estimated that the first assessment would take approximately 15 minutes for completion. Background demographic questionnaire. We abstracted the following demographic elements from the electronic medical record (EMR): disease stage, current/prior disease-modifying therapies including platinum-based chemotherapy, single agent therapy, oral EGFR tyrosine kinase inhibitor, radiation, chemoradiation, no chemotherapy, and baseline ECOG scores. Patients answered questions in regard to their ethnic background and employment status. The medical center’s information system analysts evaluated comorbidities using charge entry for clinical diagnoses in the EMR and clinician order entry for medications. Palliative Performance Scale. The PPS is derived from the KPRS. It includes an assessment of oral intake, self-care, and level of consciousness which have been independently validated as being associated with reduced survival in palliative care and hospice patients. In samples of terminally ill patients having cancer with PPS scores of 10% to 20%, dyspnea at rest, delirium and edema, survival of 3 and 6 weeks could be predicted with sensitivity of 85% and 79%, respectively, and specificity of 84% and 72%, respectively.12 Abrupt functional decline on this measure implies shorter survival. However, increase in hazard ratios (HRs) is not monotonic or linear and include
Statistical analysis and data management. The sample size calculation was made using the South Western Oncology Group Sample Size and Power Calculator software. Our null hypothesis was that patients with 20-point difference in the PPS score as compared with patients with j zj
95% Confidence Interval (CI)
0.99 1.25 .98 24.55 17.37 47.23 7.68
0.024 0.121 0.01 22.98 21.64 70.73 6.71
0.47 2.25 1.82 3.42 2.29 2.57 2.33
.64 .03 .07 .001 .022 .01 .02
.94-1.04 1.03-1.51 0.96-1.00 3.92-153.69 1.51-199.64 2.51-888.96 1.38-42.62
Abbreviations: Bllcss, baseline Lung Cancer Symptom Scale score; Bswt, baseline weight; lblecog_2, baseline ECOG score 2; lblecog_3, baseline ECOG score 3; lblecog_4, baseline ECOG score 4; lcblmedpp*2, baseline Palliative Performance Scale (PPS) score stratified based on whether in excess or below median PPS score; LR, likelihood ratio. a Breslow method for ties. No. of patients ¼ 54; no. of failures ¼ 16; time at risk ¼ 10082; LR w2 (7) ¼ 20.65; log likelihood ¼ 47.61; prob > w2 ¼ 0.0043.
study of patients with advanced cancer and non-cancer diagnoses both in the community and acute care setting also found negative correlations between the ECOG and PPS measures.25 Palliative Medicine clinicians are accustomed to using scales such as the PPS and the Edmonton Functional Assessment Tool to aid in prognostication26 and these tools are predictive of shortterm survival. For example, all patients admitted to a hospice unit with a PPS score of 10% died on average in 1.0 day in contrast to patients with scores of 40% or higher. Forty-four percent of the latter group survived to discharge. The PPS did not perform well as a tool to predict survival time in more highly functioning ambulatory patients. As such, the PPS should not be used by Palliative Medicine clinicians to help guide patients and their families in care transitions without validation in this patient population. The utility of such scales to predict survival may be limited by their lack of assistance with identification of the source of disability. Given that lung cancer prevalence increases with age, functional reserve can be expected to be lower in patients with related but relatively stable comorbidities such as congestive heart failure and chronic obstructive pulmonary disease (COPD). There is some evidence to suggest that the PPS may have greater prognostic accuracy for nursing home residents and patients with non-cancer diagnoses.27
Limitations The study was conducted at one site which may limit generalizability to other settings. The sequential sampling method of patients presenting to outpatient appointments may have skewed the sample toward less sick patients. Persons who did not complete follow-up may not have been completely missing at random. Indeed, patients who did not complete follow-up measures had higher symptom intensities at baseline. The study was confined to an academic specialty thoracic oncology multidisciplinary clinic which may limit the generalizability of its findings to community settings where there may be more patients with late-stage presentations and lower levels of functioning. Additionally, chemotherapy treatment and line of therapy were not incorporated into the analysis. Although we attempted to make this test applicable to a greater number of
patients with lung cancer, it could be that it was insufficiently robust to overcome these differences. The accrual period was longer than originally anticipated— 1 year instead of 3 months—because of the availability of the research team. Secular change in practice could have occurred that might have altered the relationship between symptom intensity and longer survival time because of the longer accrual time. Although levels of correlation between the PPS and LCSS were only moderate, some of the relationship between function and symptom burden could have been masked by medications that were in use concurrently, such as nonsteroidal analgesics. The study team did not collect data on symptom management medications such as anti-inflammatory medications which might have been associated with differences in survival time. The analytic model did not include interaction terms between the functional status measure and the symptom status measure. The impact of such complex interactions might better be explored with a factor analysis statistical model. The research model also did not include measures for domains such as social support, mental health, or spiritual well-being which could be in the causal pathway for reduced functioning and shorter survival times and which have been shown to be independently predictive of reduced survival time in patients with chronic medical illnesses.
Conclusion The PPS does not accurately predict prognosis in ambulatory patients with advanced lung cancer attending a thoracic oncology clinic. Patient reported outcomes such as those reported in the LCSS are potentially valuable tools for facilitating prognostication of survival and associated treatment decisions. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Downloaded from ajh.sagepub.com at FUDAN UNIV LIB on May 7, 2015
O’Mahony et al
7
References 1. Page NC, Read WL, Tierney RM, et al. The epidemiology of small cell lung carcinoma. Proc Amer Soc Clin Oncol. 2002;21: 305a (abstr 1216). 2. Owonikoko TK, Ragin C, Zhengjia C, et al. Real-world effectiveness of systemic agents approved for advanced non-small cell lung cancer: a SEER-medicare analysis. Oncologist. 2013;18(5):600-610. 3. Buccheri G, Ferrigno D, Tamburini M. Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer. 1996;32(7):1135-1141. 4. Evans C, McCarthy M. Referral and survival of patients accepted by terminal care support team. J Epidemiol Community Health. 1984;38(4):310-314. 5. Maltoni M, Nanni O, Derni S, et al. Clinical prediction of survival is more accurate than the Karnofsky performance status in estimating life span of terminally ill cancer patients. Eur J Cancer. 1994;30A(6):764-766. 6. Loprinzi CL, Laurie JA, Wieand HS, et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. J Clin Oncol. 1994;12(3):601-607. 7. Vigano` A, Dorgan M, Buckingham J, Bruera E, Suarez-Almazor ME. Survival prediction in terminal cancer patients: a systemic review of the medical literature. Palliat Med. 2000;14(5):363-74. Review. 8. Downing M, Lau F, Lesperance M, et al. Meta-analysis of survival prediction with Palliative Performance Scale. J Palliat Care. 2007;23(4):245-252. 9. Lau F, Downing M, Lesperance M, Karlson N, Kuziemsky C, Yang J. Using the palliative performance scale to provide meaningful survival estimates. J Pain Symptom Manage. 2009;38(1): 134-144. 10. 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. 11. Hollen PJ, Gralla RJ, Kris MG, et al. Measurement of quality of life in patients with lung cancer in multicenter trials of new therapies. Psychometric assessment of the Lung Cancer Symptom Scale. Cancer. 1994;73(8):2087-2098. 12. Morita T, Tsunoda J, Inoue S, Chihara S. Survival prediction of terminally ill cancer patients by symptoms: development of a simple indicator. Jpn J Clin Oncol. 1999;29(3):156-159. 13. Morita T, Tsunoda J, Inoue S, Chihara S. Validity of the palliative performance scale from a survival perspective. J Pain Symptom Manage. 1999;18(1):2-3. 14. Lau F, Maida V, Downing M, Lesperance M, Karlson N, Kuziemsky C. Use of Palliative Performance Scale for end of life
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
prognostication in a palliative medicine consultation service. J Pain Symptom Manage. 2009;37(6):965-972. Downing GM, Lesperance M, Lau F, Yang J. Survival implications of sudden functional decline as a sentinel event using the Palliative Performance Scale. J Palliat Med. 2010;13(5):549-557. O’Mahony S, Kornblith A, Goulet JL, et al. Desire for hastened death, cancer pain and depression: report of a longitudinal observational study. J Pain Symptom Manage. 2005;29(5):446-457. Wu YL, Fukuoka M, Mok TS, et al. Tumor response and healthrelated quality of life in clinically selected patients from Asia with advanced non-small-cell lung cancer treated with first-line gefitinib: post hoc analyses from the IPASS study. Lung Cancer. 2013; 81(2):280-287. Paddison JS, Temel JS, Fricchione GL, Pirl WF. Using the differential from complete blood counts as a biomarker of fatigue in advanced non-small-cell lung cancer: an exploratory analysis. Palliat Support Care. 2009;7(2):213-217. Cheville AL, Novotny PJ, Sloan JA, et al. Fatigue, dyspnea, and cough comprise a persistent symptom cluster up to five years after diagnosis with lung cancer. J Pain Symptom Manage. 2011;42(2): 202-212. Hoffman AJ, Given BA, von Eye A, Gift AG, Given CW. Relationships among pain, fatigue, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum. 2007;34(4):785-792. Gift AG, Jablonski A, Stommel M, Given CW. Symptom clusters in elderly patients with lung cancer. Oncol Nurs Forum. 2004; 31(2):202-212. Gift AG, Stommel M, Jablonski A, Given W. A cluster of symptoms over time in patients with lung cancer. Nurs Res. 2003;52(6): 393-400. Seow H, Barbera L, Dudgeon D, et al. The association of the palliative performance scale and hazard of death in the ambulatory cancer population. J Palliat Med. 2013;16(2):156-162. Myers J, Kim A, Flanagan J, Selby D. Palliative performance scale and survival among outpatients with advanced cancer [published online September 18, 2014]. Support Care Cancer. 2014. de Kock I, Mirhosseini M, Lau F, et al. Conversion of Karnofsky Performance Status (KPS) and Eastern Cooperative Oncology Group Performance Status (ECOG) to Palliative Performance Scale (PPS), and the interchangeability of PPS and KPS in prognostic tools. J Palliat Care. 2013;29(3):163-169. Kaasa T, Wessel J. The Edmonton Functional Assessment Tool. Further development and validation for use in palliative care. J Palliative Care. 2001;17(1):5-11. Harrold J, Rickerson E, Carroll JT. Is the palliative performance scale a useful predictor of mortality in a heterogeneous hospice population? J Palliat Med. 2005;8(3):503-509.
Downloaded from ajh.sagepub.com at FUDAN UNIV LIB on May 7, 2015
Survival Prediction in Ambulatory Patients With Stage III/IV Non-Small Cell Lung Cancer Using the Palliative Performance Scale, ECOG, and Lung Cancer Symptom Scale
American Journal of Hospice & Palliative Medicine® 1-7 ª The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1049909115570707 ajhpm.sagepub.com
Sean O’Mahony, MB, BCH, BAO1, Susan Nathan, MD1, Roozbeh Mohajer, MD2, Philip Bonomi, MD1, Marta Batus, MD1, Mary Jo Fidler, MD1, Kalani Wells, MS1, Naomi Kern, MS3, Shannon Sims, MD/PHD1, and Darpan Amin, BS1
Abstract Objectives: Patients with advanced non-small cell lung cancer (NSCLC) have a life expectancy of less than 1 year. Therefore, it is important to maximize their quality of life and find a tool that can more accurately predict survival. Materials: The Palliative Performance Scale (PPS) is used to predict survival for patients with advanced disease based on functional dimensions. The value of the PPS in ambulatory patients with cancer has not been examined to date. The Lung Cancer Symptom Scale (LCSS) measures six major symptoms and their effect on symptomatic distress and activity. We evaluated 62 patients with stage III or IV NSCLC and Eastern Cooperative Oncology Group (ECOG) Scale Score 1 at baseline in a thoracic oncology clinic. In all, 62 patients had LCSS and PPS evaluated at baseline and 54 patients had 4-week follow-up using LCSS, PPS, and ECOG. Results: Fifty-four patients completed baseline and follow-up. Mean age was 63.7 years. Sixty-three percent were receiving chemotherapy at evaluation. Seventeen patients died. Mean baseline measures were LCSS 6.18 (1-14); PPS 66.6 (40-90); and ECOG 1.82 (1-4). Censored survival times were calculated from enrollment of the first patient for 380 days. A proportional hazardous model was computed for survival status. Hazard ratios for death were 1.25 (P ¼ .013) for LCSS, 2.12 (P ¼ .027) for ECOG, and 1.02 for PPS (P ¼ .49). Conclusions: The LCSS predicted prognosis best in this study. The PPS did not accurately predict prognosis in our patient population. Keywords prognostication, lung cancer, ECOG, palliative performance scale, lung cancer symptom scale, palliative care
Introduction Non-small cell lung cancer (NSCLC) accounts for 87% of all lung cancers and the majority of patients present with advanced, incurable disease.1 Although there have been marked improvements in survival for patients presenting with advanced NSCLC in the last 15 years in response to the development of new cytotoxic agents, median survival times are still low at about 1 year. Physicians, including hospice physicians and oncologists, consistently overestimate patient survival by about 45%. Because of this, it is essential to maximize the quality of life (QOL) for these patients and find tools that accurately predict their prognosis and assess functionality and symptom burden.2 Both the Karnofsky Performance Rating Scale (KPRS) and Eastern Cooperative Oncology Group (ECOG) performance status measure have been shown to predict survival in patients
with advanced cancer. However, they only moderately correlate with clinical estimates of survival.3 Several studies have found an association between reduced KPRS scores and reduced survival: a KPRS score 60% and steep drop-offs in scores 20% at follow-up.13-15 Lung Cancer Symptom Scale. The LCSS is a lung cancer-specific measure of QOL, particularly for use in clinical trials. It evaluates six common symptoms associated with lung cancer (loss of appetite, fatigue, cough, dyspnea, hemoptysis, and pain) and their effect on overall distress, functional activities, and global QOL. It provides a practical measure of QOL that reduces patient and staff burden in serial measurement of QOL during the course of clinical trials. Initially, it is administered by faceto-face interview for demonstration of visual analogue scales (VAS) with a simple example question related to the weather, with telephone interview acceptable once patient is familiar with the VAS. It takes 8 minutes for initial demonstration of the VAS and 3 to 5 minutes for subsequent administrations.
Figure 1. Outline of the recruitment process.
baseline. Patients were asked to indicate their ethnicity/race, education, and employment status. Cognitive screening was conducted by the Palliative Medicine fellows or NP students. The recruitment process is outlined in Figure 1. Measures. In order to limit patient burden and maximize completion of the assessment, all self-report scales were read to study patients who might have difficulty completing the selfreport measures due to fatigue, illness, or difficulty reading due to visual difficulty. It was estimated that the first assessment would take approximately 15 minutes for completion. Background demographic questionnaire. We abstracted the following demographic elements from the electronic medical record (EMR): disease stage, current/prior disease-modifying therapies including platinum-based chemotherapy, single agent therapy, oral EGFR tyrosine kinase inhibitor, radiation, chemoradiation, no chemotherapy, and baseline ECOG scores. Patients answered questions in regard to their ethnic background and employment status. The medical center’s information system analysts evaluated comorbidities using charge entry for clinical diagnoses in the EMR and clinician order entry for medications. Palliative Performance Scale. The PPS is derived from the KPRS. It includes an assessment of oral intake, self-care, and level of consciousness which have been independently validated as being associated with reduced survival in palliative care and hospice patients. In samples of terminally ill patients having cancer with PPS scores of 10% to 20%, dyspnea at rest, delirium and edema, survival of 3 and 6 weeks could be predicted with sensitivity of 85% and 79%, respectively, and specificity of 84% and 72%, respectively.12 Abrupt functional decline on this measure implies shorter survival. However, increase in hazard ratios (HRs) is not monotonic or linear and include
Statistical analysis and data management. The sample size calculation was made using the South Western Oncology Group Sample Size and Power Calculator software. Our null hypothesis was that patients with 20-point difference in the PPS score as compared with patients with j zj
95% Confidence Interval (CI)
0.99 1.25 .98 24.55 17.37 47.23 7.68
0.024 0.121 0.01 22.98 21.64 70.73 6.71
0.47 2.25 1.82 3.42 2.29 2.57 2.33
.64 .03 .07 .001 .022 .01 .02
.94-1.04 1.03-1.51 0.96-1.00 3.92-153.69 1.51-199.64 2.51-888.96 1.38-42.62
Abbreviations: Bllcss, baseline Lung Cancer Symptom Scale score; Bswt, baseline weight; lblecog_2, baseline ECOG score 2; lblecog_3, baseline ECOG score 3; lblecog_4, baseline ECOG score 4; lcblmedpp*2, baseline Palliative Performance Scale (PPS) score stratified based on whether in excess or below median PPS score; LR, likelihood ratio. a Breslow method for ties. No. of patients ¼ 54; no. of failures ¼ 16; time at risk ¼ 10082; LR w2 (7) ¼ 20.65; log likelihood ¼ 47.61; prob > w2 ¼ 0.0043.
study of patients with advanced cancer and non-cancer diagnoses both in the community and acute care setting also found negative correlations between the ECOG and PPS measures.25 Palliative Medicine clinicians are accustomed to using scales such as the PPS and the Edmonton Functional Assessment Tool to aid in prognostication26 and these tools are predictive of shortterm survival. For example, all patients admitted to a hospice unit with a PPS score of 10% died on average in 1.0 day in contrast to patients with scores of 40% or higher. Forty-four percent of the latter group survived to discharge. The PPS did not perform well as a tool to predict survival time in more highly functioning ambulatory patients. As such, the PPS should not be used by Palliative Medicine clinicians to help guide patients and their families in care transitions without validation in this patient population. The utility of such scales to predict survival may be limited by their lack of assistance with identification of the source of disability. Given that lung cancer prevalence increases with age, functional reserve can be expected to be lower in patients with related but relatively stable comorbidities such as congestive heart failure and chronic obstructive pulmonary disease (COPD). There is some evidence to suggest that the PPS may have greater prognostic accuracy for nursing home residents and patients with non-cancer diagnoses.27
Limitations The study was conducted at one site which may limit generalizability to other settings. The sequential sampling method of patients presenting to outpatient appointments may have skewed the sample toward less sick patients. Persons who did not complete follow-up may not have been completely missing at random. Indeed, patients who did not complete follow-up measures had higher symptom intensities at baseline. The study was confined to an academic specialty thoracic oncology multidisciplinary clinic which may limit the generalizability of its findings to community settings where there may be more patients with late-stage presentations and lower levels of functioning. Additionally, chemotherapy treatment and line of therapy were not incorporated into the analysis. Although we attempted to make this test applicable to a greater number of
patients with lung cancer, it could be that it was insufficiently robust to overcome these differences. The accrual period was longer than originally anticipated— 1 year instead of 3 months—because of the availability of the research team. Secular change in practice could have occurred that might have altered the relationship between symptom intensity and longer survival time because of the longer accrual time. Although levels of correlation between the PPS and LCSS were only moderate, some of the relationship between function and symptom burden could have been masked by medications that were in use concurrently, such as nonsteroidal analgesics. The study team did not collect data on symptom management medications such as anti-inflammatory medications which might have been associated with differences in survival time. The analytic model did not include interaction terms between the functional status measure and the symptom status measure. The impact of such complex interactions might better be explored with a factor analysis statistical model. The research model also did not include measures for domains such as social support, mental health, or spiritual well-being which could be in the causal pathway for reduced functioning and shorter survival times and which have been shown to be independently predictive of reduced survival time in patients with chronic medical illnesses.
Conclusion The PPS does not accurately predict prognosis in ambulatory patients with advanced lung cancer attending a thoracic oncology clinic. Patient reported outcomes such as those reported in the LCSS are potentially valuable tools for facilitating prognostication of survival and associated treatment decisions. Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Downloaded from ajh.sagepub.com at FUDAN UNIV LIB on May 7, 2015
O’Mahony et al
7
References 1. Page NC, Read WL, Tierney RM, et al. The epidemiology of small cell lung carcinoma. Proc Amer Soc Clin Oncol. 2002;21: 305a (abstr 1216). 2. Owonikoko TK, Ragin C, Zhengjia C, et al. Real-world effectiveness of systemic agents approved for advanced non-small cell lung cancer: a SEER-medicare analysis. Oncologist. 2013;18(5):600-610. 3. Buccheri G, Ferrigno D, Tamburini M. Karnofsky and ECOG performance status scoring in lung cancer: a prospective, longitudinal study of 536 patients from a single institution. Eur J Cancer. 1996;32(7):1135-1141. 4. Evans C, McCarthy M. Referral and survival of patients accepted by terminal care support team. J Epidemiol Community Health. 1984;38(4):310-314. 5. Maltoni M, Nanni O, Derni S, et al. Clinical prediction of survival is more accurate than the Karnofsky performance status in estimating life span of terminally ill cancer patients. Eur J Cancer. 1994;30A(6):764-766. 6. Loprinzi CL, Laurie JA, Wieand HS, et al. Prospective evaluation of prognostic variables from patient-completed questionnaires. J Clin Oncol. 1994;12(3):601-607. 7. Vigano` A, Dorgan M, Buckingham J, Bruera E, Suarez-Almazor ME. Survival prediction in terminal cancer patients: a systemic review of the medical literature. Palliat Med. 2000;14(5):363-74. Review. 8. Downing M, Lau F, Lesperance M, et al. Meta-analysis of survival prediction with Palliative Performance Scale. J Palliat Care. 2007;23(4):245-252. 9. Lau F, Downing M, Lesperance M, Karlson N, Kuziemsky C, Yang J. Using the palliative performance scale to provide meaningful survival estimates. J Pain Symptom Manage. 2009;38(1): 134-144. 10. 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. 11. Hollen PJ, Gralla RJ, Kris MG, et al. Measurement of quality of life in patients with lung cancer in multicenter trials of new therapies. Psychometric assessment of the Lung Cancer Symptom Scale. Cancer. 1994;73(8):2087-2098. 12. Morita T, Tsunoda J, Inoue S, Chihara S. Survival prediction of terminally ill cancer patients by symptoms: development of a simple indicator. Jpn J Clin Oncol. 1999;29(3):156-159. 13. Morita T, Tsunoda J, Inoue S, Chihara S. Validity of the palliative performance scale from a survival perspective. J Pain Symptom Manage. 1999;18(1):2-3. 14. Lau F, Maida V, Downing M, Lesperance M, Karlson N, Kuziemsky C. Use of Palliative Performance Scale for end of life
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
prognostication in a palliative medicine consultation service. J Pain Symptom Manage. 2009;37(6):965-972. Downing GM, Lesperance M, Lau F, Yang J. Survival implications of sudden functional decline as a sentinel event using the Palliative Performance Scale. J Palliat Med. 2010;13(5):549-557. O’Mahony S, Kornblith A, Goulet JL, et al. Desire for hastened death, cancer pain and depression: report of a longitudinal observational study. J Pain Symptom Manage. 2005;29(5):446-457. Wu YL, Fukuoka M, Mok TS, et al. Tumor response and healthrelated quality of life in clinically selected patients from Asia with advanced non-small-cell lung cancer treated with first-line gefitinib: post hoc analyses from the IPASS study. Lung Cancer. 2013; 81(2):280-287. Paddison JS, Temel JS, Fricchione GL, Pirl WF. Using the differential from complete blood counts as a biomarker of fatigue in advanced non-small-cell lung cancer: an exploratory analysis. Palliat Support Care. 2009;7(2):213-217. Cheville AL, Novotny PJ, Sloan JA, et al. Fatigue, dyspnea, and cough comprise a persistent symptom cluster up to five years after diagnosis with lung cancer. J Pain Symptom Manage. 2011;42(2): 202-212. Hoffman AJ, Given BA, von Eye A, Gift AG, Given CW. Relationships among pain, fatigue, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum. 2007;34(4):785-792. Gift AG, Jablonski A, Stommel M, Given CW. Symptom clusters in elderly patients with lung cancer. Oncol Nurs Forum. 2004; 31(2):202-212. Gift AG, Stommel M, Jablonski A, Given W. A cluster of symptoms over time in patients with lung cancer. Nurs Res. 2003;52(6): 393-400. Seow H, Barbera L, Dudgeon D, et al. The association of the palliative performance scale and hazard of death in the ambulatory cancer population. J Palliat Med. 2013;16(2):156-162. Myers J, Kim A, Flanagan J, Selby D. Palliative performance scale and survival among outpatients with advanced cancer [published online September 18, 2014]. Support Care Cancer. 2014. de Kock I, Mirhosseini M, Lau F, et al. Conversion of Karnofsky Performance Status (KPS) and Eastern Cooperative Oncology Group Performance Status (ECOG) to Palliative Performance Scale (PPS), and the interchangeability of PPS and KPS in prognostic tools. J Palliat Care. 2013;29(3):163-169. Kaasa T, Wessel J. The Edmonton Functional Assessment Tool. Further development and validation for use in palliative care. J Palliative Care. 2001;17(1):5-11. Harrold J, Rickerson E, Carroll JT. Is the palliative performance scale a useful predictor of mortality in a heterogeneous hospice population? J Palliat Med. 2005;8(3):503-509.
Downloaded from ajh.sagepub.com at FUDAN UNIV LIB on May 7, 2015
